AITD-00000 |
0-1 Loss Function |
0-1损失函数 |
|
[1] |
|
AITD-00001 |
Absolute Loss Function |
绝对损失函数 |
|
[1] |
|
AITD-00002 |
Absolute Value Rectification |
绝对值整流 |
|
[1] |
|
AITD-00003 |
Accept-Reject Sampling Method |
接受-拒绝抽样法/接受-拒绝采样法 |
|
[1] |
|
AITD-00004 |
Acceptance Distribution |
接受分布 |
|
[1] |
|
AITD-00005 |
Access Parameters |
访问参数 |
|
[1] |
|
AITD-00006 |
Accumulated Error Backpropagation |
累积误差反向传播 |
|
[1] |
|
AITD-00007 |
Accuracy |
准确率 |
|
[1] |
|
AITD-00008 |
Acoustic |
声学 |
|
[1] |
|
AITD-00009 |
Acoustic Modeling |
声学建模 |
|
[1] |
|
AITD-00010 |
Acquisition Function |
采集函数 |
|
[1] |
|
AITD-00011 |
Action |
动作 |
|
[1] |
|
AITD-00012 |
Action Value Function |
动作价值函数 |
|
[1] |
|
AITD-00013 |
Actionism |
行为主义 |
|
[1] |
|
AITD-00014 |
Activation |
活性值 |
|
[1] |
|
AITD-00015 |
Activation Function |
激活函数 |
|
[1][2][3][4] |
机器学习 |
AITD-00016 |
Active Learning |
主动学习 |
|
[1] |
机器学习 |
AITD-00017 |
Actor |
演员 |
|
[1] |
|
AITD-00018 |
Actor-Critic Algorithm |
演员-评论员算法 |
|
[1] |
|
AITD-00019 |
Actor-Critic Method |
演员-评论员法 |
|
[1] |
|
AITD-00020 |
Adaptive Bitrate Algorithm |
自适应比特率算法 |
ABR |
[1] |
|
AITD-00021 |
Adaptive Boosting |
AdaBoost |
|
[1] |
|
AITD-00022 |
Adaptive Gradient Algorithm |
AdaGrad |
|
[1] |
|
AITD-00023 |
Adaptive Moment Estimation Algorithm |
Adam算法 |
Adam |
[1] |
|
AITD-00024 |
Adaptive Resonance Theory |
自适应谐振理论 |
ART |
[1] |
|
AITD-00025 |
Additive Model |
加性模型 |
|
[1] |
|
AITD-00026 |
Adversarial |
对抗 |
|
[1] |
|
AITD-00027 |
Adversarial Example |
对抗样本 |
|
[1] |
|
AITD-00028 |
Adversarial Networks |
对抗网络 |
|
[1] |
|
AITD-00029 |
Adversarial Training |
对抗训练 |
|
[1] |
|
AITD-00030 |
Affine Layer |
仿射层 |
|
[1] |
|
AITD-00031 |
Affine Transformation |
仿射变换 |
|
[1] |
|
AITD-00032 |
Affinity Matrix |
亲和矩阵 |
|
[1] |
|
AITD-00033 |
Agent |
智能体 |
|
[1][2][3][4] |
|
AITD-00034 |
Agglomerative |
聚合 |
|
[1] |
|
AITD-00035 |
Agnostic PAC Learnable |
不可知PAC可学习 |
|
[1] |
|
AITD-00036 |
Algorithm |
算法 |
|
[1][2][3] |
|
AITD-00037 |
Almost Everywhere |
几乎处处 |
|
[1] |
|
AITD-00038 |
Almost Sure |
几乎必然 |
|
[1] |
|
AITD-00039 |
Almost Sure Convergence |
几乎必然收敛 |
|
[1] |
|
AITD-00040 |
Alpha-Beta Pruning |
α-β修剪法 |
|
[1] |
|
AITD-00041 |
Alternative Splicing Dataset |
选择性剪接数据集 |
|
[1] |
|
AITD-00042 |
Ambiguity |
分歧 |
|
[1] |
|
AITD-00043 |
Analytic Gradient |
解析梯度 |
|
[1] |
|
AITD-00044 |
Ancestral Sampling |
原始采样 |
|
[1] |
|
AITD-00045 |
Annealed Importance Sampling |
退火重要采样 |
|
[1] |
|
AITD-00046 |
Anomaly Detection |
异常检测 |
|
[1] |
|
AITD-00047 |
Aperiodic |
非周期的 |
|
[1] |
|
AITD-00048 |
Aperiodic Graph |
非周期性图 |
|
[1] |
|
AITD-00049 |
Application-Specific Integrated Circuit |
专用集成电路 |
|
[1] |
|
AITD-00050 |
Approximate Bayesian Computation |
近似贝叶斯计算 |
|
[1] |
|
AITD-00051 |
Approximate Dynamic Programming |
近似动态规划 |
|
[1] |
|
AITD-00052 |
Approximate Inference |
近似推断 |
|
[1] |
|
AITD-00053 |
Approximation |
近似 |
|
[1] |
|
AITD-00054 |
Approximation Error |
近似误差 |
|
[1] |
|
AITD-00055 |
Architecture |
架构 |
|
[1] |
|
AITD-00056 |
Area Under ROC Curve |
AUC(ROC曲线下方面积,度量分类模型好坏的标准) |
AUC |
[1] |
机器学习 |
AITD-00057 |
Arithmetic Coding |
算术编码 |
|
[1] |
|
AITD-00058 |
Artificial General Intelligence |
通用人工智能 |
AGI |
[1] |
|
AITD-00059 |
Artificial Intelligence |
人工智能 |
AI |
[1][2][3][4][5] |
机器学习 |
AITD-00060 |
Artificial Neural Network |
人工神经网络 |
ANN |
[1][2] |
机器学习 |
AITD-00061 |
Artificial Neuron |
人工神经元 |
|
[1] |
|
AITD-00062 |
Association Analysis |
关联分析 |
|
[1] |
|
AITD-00063 |
Associative Memory |
联想记忆 |
|
[1] |
|
AITD-00064 |
Associative Memory Model |
联想记忆模型 |
|
[1] |
|
AITD-00065 |
Asymptotically Unbiased |
渐近无偏 |
|
[1] |
|
AITD-00066 |
Asynchronous Stochastic Gradient Descent |
异步随机梯度下降 |
|
[1] |
|
AITD-00067 |
Asynchronous |
异步 |
|
[1] |
|
AITD-00068 |
Atrous Convolution |
空洞卷积 |
|
[1] |
|
AITD-00069 |
Attention |
注意力 |
|
[1][2] |
机器学习 |
AITD-00070 |
Attention Cue |
注意力提示 |
|
[1] |
|
AITD-00071 |
Attention Distribution |
注意力分布 |
|
[1] |
|
AITD-00072 |
Attention Mechanism |
注意力机制 |
|
[1][2][3] |
|
AITD-00073 |
Attention Model |
注意力模型 |
|
[1] |
|
AITD-00074 |
Attractor |
吸引点 |
|
[1] |
|
AITD-00075 |
Attribute |
属性 |
|
[1] |
|
AITD-00076 |
Attribute Conditional Independence Assumption |
属性条件独立性假设 |
|
[1] |
|
AITD-00077 |
Attribute Space |
属性空间 |
|
[1] |
|
AITD-00078 |
Attribute Value |
属性值 |
|
[1] |
|
AITD-00079 |
Augmented Lagrangian |
增广拉格朗日法 |
|
[1] |
|
AITD-00080 |
Auto-Regressive Network |
自回归网络 |
|
[1] |
|
AITD-00081 |
Autoencoder |
自编码器 |
AE |
[1] |
|
AITD-00082 |
Automatic Differentiation |
自动微分 |
AD |
[1] |
|
AITD-00083 |
Automatic Speech Recognition |
自动语音识别 |
ASR |
[1] |
|
AITD-00084 |
Automatic Summarization |
自动摘要 |
|
[1] |
|
AITD-00085 |
Autoregressive Generative Model |
自回归生成模型 |
|
[1] |
|
AITD-00086 |
Autoregressive Model |
自回归模型 |
AR |
[1] |
|
AITD-00087 |
Autoregressive Process |
自回归过程 |
|
[1] |
|
AITD-00088 |
Average Gradient |
平均梯度 |
|
[1] |
|
AITD-00089 |
Average Pooling Layer |
平均汇聚层 |
|
[1] |
|
AITD-00090 |
Average-Pooling |
平均汇聚 |
|
[1] |
|
AITD-00091 |
Averaged Perceptron |
平均感知器 |
|
[1] |
|
AITD-00092 |
Back Propagation |
反向传播 |
BP |
[1][2][3] |
机器学习 |
AITD-00093 |
Back Propagation Algorithm |
反向传播算法 |
|
[1] |
|
AITD-00094 |
Back Propagation Through Time |
随时间反向传播 |
BPTT |
[1] |
|
AITD-00095 |
Back-Off |
回退 |
|
[1] |
|
AITD-00096 |
Backward |
后向 |
|
[1] |
|
AITD-00097 |
Backward Induction |
反向归纳 |
|
[1] |
|
AITD-00098 |
Backward Search |
反向搜索 |
|
[1] |
|
AITD-00099 |
Bag of Words |
词袋 |
BOW |
[1] |
|
AITD-00100 |
Bagging |
袋装 |
|
[1] |
机器学习 |
AITD-00101 |
Bandit |
赌博机/老虎机 |
|
[1] |
|
AITD-00102 |
Bandpass Filter |
带通滤波器 |
|
[1] |
|
AITD-00103 |
Base |
基 |
|
[1] |
|
AITD-00104 |
Base Classifier |
基分类器 |
|
[1] |
|
AITD-00105 |
Base Learner |
基学习器 |
|
[1] |
|
AITD-00106 |
Base Learning Algorithm |
基学习算法 |
|
[1] |
|
AITD-00107 |
Base Vector |
基向量 |
|
[1] |
|
AITD-00108 |
Baseline |
基准 |
|
[1] |
机器学习 |
AITD-00109 |
Basin of Attraction |
吸引域 |
|
[1] |
|
AITD-00110 |
Batch |
批量 |
|
[1] |
|
AITD-00111 |
Batch Gradient Descent |
批量梯度下降法 |
BGD |
[1] |
|
AITD-00112 |
Batch Learning |
批量学习 |
|
[1] |
|
AITD-00113 |
Batch Normalization |
批量规范化 |
BN |
[1] |
|
AITD-00114 |
Batch Size |
批量大小 |
|
[1] |
|
AITD-00115 |
Baum-Welch Algorithm |
Baum-Welch算法 |
|
[1] |
|
AITD-00116 |
Bayes Classifier |
贝叶斯分类器 |
|
[1] |
|
AITD-00117 |
Bayes Decision Rule |
贝叶斯决策准则 |
|
[1] |
|
AITD-00118 |
Bayes Error |
贝叶斯误差 |
|
[1] |
|
AITD-00119 |
Bayes Model Averaging |
贝叶斯模型平均 |
BMA |
[1] |
|
AITD-00120 |
Bayes Optimal Classifier |
贝叶斯最优分类器 |
|
[1] |
|
AITD-00121 |
Bayes Risk |
贝叶斯风险 |
|
[1] |
|
AITD-00122 |
Bayes' Rule |
贝叶斯规则 |
|
[1] |
|
AITD-00123 |
Bayes' Theorem |
贝叶斯定理 |
|
[1] |
|
AITD-00124 |
Bayesian Decision Theory |
贝叶斯决策理论 |
|
[1] |
|
AITD-00125 |
Bayesian Estimation |
贝叶斯估计 |
|
[1] |
|
AITD-00126 |
Bayesian Inference |
贝叶斯推断 |
|
[1] |
统计,机器学习 |
AITD-00127 |
Bayesian Learning |
贝叶斯学习 |
|
[1] |
|
AITD-00128 |
Bayesian Linear Regression |
贝叶斯线性回归 |
|
[1] |
|
AITD-00129 |
Bayesian Network |
贝叶斯网/贝叶斯网络 |
|
[1] |
Network翻译为网或网络皆可,只要统一翻译成网或者统一翻译成网络即可;统计,机器学习 |
AITD-00130 |
Bayesian Optimization |
贝叶斯优化 |
|
[1] |
|
AITD-00131 |
Bayesian Probability |
贝叶斯概率 |
|
[1] |
|
AITD-00132 |
Bayesian Statistics |
贝叶斯统计 |
|
[1] |
|
AITD-00133 |
Beam Search |
束搜索 |
|
[1] |
|
AITD-00134 |
Benchmark |
基准 |
|
[1] |
|
AITD-00135 |
Belief Network |
信念网/信念网络 |
BN |
[1] |
Network翻译为网或网络皆可,只要统一翻译成网或者统一翻译成网络即可 |
AITD-00136 |
Belief Propagation |
信念传播 |
BP |
[1] |
|
AITD-00137 |
Bellman Equation |
贝尔曼方程 |
|
[1] |
|
AITD-00138 |
Bellman Optimality Equation |
贝尔曼最优方程 |
|
[1] |
|
AITD-00139 |
Bernoulli Distribution |
伯努利分布 |
|
[1] |
统计 |
AITD-00140 |
Bernoulli Output Distribution |
伯努利输出分布 |
|
[1] |
|
AITD-00141 |
Best-Arm Problem |
最优臂问题 |
|
[1] |
|
AITD-00142 |
Beta Distribution |
贝塔分布 |
|
[1] |
|
AITD-00143 |
Between-Class Scatter Matrix |
类间散度矩阵 |
|
[1] |
|
AITD-00144 |
BFGS |
BFGS |
|
[1] |
|
AITD-00145 |
Bi-Directional Long-Short Term Memory |
双向长短期记忆 |
Bi-LSTM |
[1] |
|
AITD-00146 |
Bi-Partition |
二分法 |
|
[1] |
|
AITD-00147 |
Bias |
偏差/偏置 |
|
[1][2][3][4] |
看上下语境;机器学习 |
AITD-00148 |
Bias In Affine Function |
偏置 |
|
[1] |
看上下语境 |
AITD-00149 |
Bias In Statistics |
偏差 |
|
[1] |
看上下语境 |
AITD-00150 |
Bias Shift |
偏置偏移 |
|
[1] |
|
AITD-00151 |
Bias-Variance Decomposition |
偏差 - 方差分解 |
|
[1] |
|
AITD-00152 |
Bias-Variance Dilemma |
偏差 - 方差困境 |
|
[1] |
|
AITD-00153 |
Biased |
有偏 |
|
[1] |
机器学习 |
AITD-00154 |
Biased Importance Sampling |
有偏重要采样 |
|
[1] |
|
AITD-00155 |
Bidirectional Language Model |
双向语言模型 |
|
[1] |
|
AITD-00156 |
Bidirectional Recurrent Neural Network |
双向循环神经网络 |
Bi-RNN |
[1] |
|
AITD-00157 |
Bigram |
二元语法 |
|
[1] |
|
AITD-00158 |
Bilingual Evaluation Understudy |
BLEU |
|
[1] |
|
AITD-00159 |
Binary Classification |
二分类 |
|
[1] |
|
AITD-00160 |
Binary Relation |
二元关系 |
|
[1] |
|
AITD-00161 |
Binary Sparse Coding |
二值稀疏编码 |
|
[1] |
|
AITD-00162 |
Binomial Distribution |
二项分布 |
|
[1] |
|
AITD-00163 |
Binomial Logistic Regression Model |
二项对数几率回归 |
|
[1] |
|
AITD-00164 |
Binomial Test |
二项检验 |
|
[1] |
|
AITD-00165 |
Biological Plausibility |
生物学合理性 |
|
[1] |
|
AITD-00166 |
Bit |
比特 |
|
[1] |
|
AITD-00167 |
Block |
块 |
|
[1] |
|
AITD-00168 |
Block Coordinate Descent |
块坐标下降 |
|
[1] |
|
AITD-00169 |
Block Gibbs Sampling |
块吉布斯采样 |
|
[1] |
|
AITD-00170 |
Boilerplate Code |
样板代码 |
|
[1] |
|
AITD-00171 |
Boltzmann |
玻尔兹曼 |
|
[1] |
|
AITD-00172 |
Boltzmann Distribution |
玻尔兹曼分布 |
|
[1] |
|
AITD-00173 |
Boltzmann Factor |
玻尔兹曼因子 |
|
[1] |
|
AITD-00174 |
Boltzmann Machine |
玻尔兹曼机 |
|
[1] |
|
AITD-00175 |
Boosting |
Boosting(一种模型训练加速方式) |
|
[1] |
|
AITD-00176 |
Boosting Tree |
提升树 |
|
[1] |
|
AITD-00177 |
Bootstrap Aggregating |
Bagging |
|
[1] |
|
AITD-00178 |
Bootstrap Sampling |
自助采样法 |
|
[1] |
|
AITD-00179 |
Bootstrapping |
自助法/自举法 |
|
[1] |
|
AITD-00180 |
Bottleneck Layer |
瓶颈层 |
|
[1] |
|
AITD-00181 |
Bottom-Up |
自下而上 |
|
[1] |
|
AITD-00182 |
Bounding Boxes |
边界框 |
|
[1] |
|
AITD-00183 |
Break-Event Point |
平衡点 |
BEP |
[1] |
|
AITD-00184 |
Bridge Sampling |
桥式采样 |
|
[1] |
|
AITD-00185 |
Broadcasting |
广播 |
|
[1] |
|
AITD-00186 |
Broyden's Algorithm |
Broyden类算法 |
|
[1] |
|
AITD-00187 |
Bucketing |
分桶 |
|
[1] |
|
AITD-00188 |
Burn-In Period |
预烧期 |
|
[1] |
|
AITD-00189 |
Burning-In |
磨合 |
|
[1] |
|
AITD-00190 |
Calculus |
微积分 |
|
[1] |
|
AITD-00191 |
Calculus of Variations |
变分法 |
|
[1] |
|
AITD-00192 |
Calibration |
校准 |
|
[1] |
|
AITD-00193 |
Canonical |
正则的 |
|
[1] |
|
AITD-00194 |
Canonical Correlation Analysis |
典型相关分析 |
CCA |
[1] |
|
AITD-00195 |
Capacity |
容量 |
|
[1] |
|
AITD-00196 |
Cartesian Coordinate |
笛卡尔坐标 |
|
[1] |
|
AITD-00197 |
Cascade |
级联 |
|
[1] |
|
AITD-00198 |
Cascade-Correlation |
级联相关 |
|
[1] |
|
AITD-00199 |
Catastrophic Forgetting |
灾难性遗忘 |
|
[1] |
|
AITD-00200 |
Categorical Attribute |
分类属性 |
|
[1] |
|
AITD-00201 |
Categorical Distribution |
类别分布 |
|
[1] |
|
AITD-00202 |
Causal Factor |
因果因子 |
|
[1] |
|
AITD-00203 |
Causal Modeling |
因果模型 |
|
[1] |
|
AITD-00204 |
Cell |
单元 |
|
[1] |
|
AITD-00205 |
Centered Difference |
中心差分 |
|
[1] |
|
AITD-00206 |
Central Limit Theorem |
中心极限定理 |
|
[1] |
|
AITD-00207 |
Chain Rule |
链式法则 |
|
[1] |
|
AITD-00208 |
Channel |
通道 |
|
[1] |
|
AITD-00209 |
Chaos |
混沌 |
|
[1] |
|
AITD-00210 |
Chebyshev Distance |
切比雪夫距离 |
|
[1] |
|
AITD-00211 |
Chord |
弦 |
|
[1] |
|
AITD-00212 |
Chordal Graph |
弦图 |
|
[1] |
|
AITD-00213 |
City Block Distance |
街区距离 |
|
[1] |
|
AITD-00214 |
Class |
类别 |
|
[1] |
|
AITD-00215 |
Class Label |
类标记 |
|
[1] |
|
AITD-00216 |
Class-Conditional Probability |
类条件概率 |
|
[1] |
|
AITD-00217 |
Class-Imbalance |
类别不平衡 |
|
[1] |
|
AITD-00218 |
Classification |
分类 |
|
[1][2] |
|
AITD-00219 |
Classification And Regression Tree |
分类与回归树 |
CART |
[1] |
|
AITD-00220 |
Classifier |
分类器 |
|
[1] |
|
AITD-00221 |
Clip Gradient |
梯度截断 |
|
[1] |
|
AITD-00222 |
Clipping The Gradient |
截断梯度 |
|
[1] |
|
AITD-00223 |
Clique |
团 |
|
[1] |
|
AITD-00224 |
Clique Potential |
团势能 |
|
[1] |
|
AITD-00225 |
Clockwork RNN |
时钟循环神经网络 |
|
[1] |
|
AITD-00226 |
Closed Form Solution |
闭式解 |
|
[1] |
|
AITD-00227 |
Closed-Form |
闭式 |
|
[1] |
|
AITD-00228 |
Cluster |
簇 |
|
[1] |
|
AITD-00229 |
Cluster Analysis |
聚类分析 |
|
[1] |
|
AITD-00230 |
Cluster Assumption |
聚类假设 |
|
[1] |
|
AITD-00231 |
Clustering |
聚类 |
|
[1] |
|
AITD-00232 |
Clustering Ensemble |
聚类集成 |
|
[1] |
|
AITD-00233 |
Co-Adapting |
共适应 |
|
[1] |
|
AITD-00234 |
Co-Occurrence |
共现 |
|
[1] |
|
AITD-00235 |
Co-Occurrence Frequency |
共现词频 |
|
[1] |
|
AITD-00236 |
Co-Training |
协同训练 |
|
[1] |
|
AITD-00237 |
Code |
编码 |
|
[1] |
|
AITD-00238 |
Codebook Learning |
码书学习 |
|
[1] |
|
AITD-00239 |
Coding Matrix |
编码矩阵 |
|
[1] |
|
AITD-00240 |
Collaborative Filtering |
协同过滤 |
|
[1] |
|
AITD-00241 |
Collapsed Gibbs Sampling |
收缩的吉布斯抽样 |
|
[1] |
|
AITD-00242 |
Collinearity |
共线性 |
|
[1] |
|
AITD-00243 |
COLT |
国际学习理论会议 |
|
[1] |
|
AITD-00244 |
Column |
列 |
|
[1] |
|
AITD-00245 |
Column Space |
列空间 |
|
[1] |
|
AITD-00246 |
Combinatorial Optimization |
组合优化 |
|
[1] |
|
AITD-00247 |
Committee-Based Learning |
基于委员会的学习 |
|
[1] |
|
AITD-00248 |
Common Cause |
共因 |
|
[1] |
|
AITD-00249 |
Common Parent |
同父 |
|
[1] |
|
AITD-00250 |
Compact Singular Value Decomposition |
紧奇异值分解 |
|
[1] |
|
AITD-00251 |
Competitive Learning |
竞争型学习 |
|
[1] |
|
AITD-00252 |
Complementary Slackness |
互补松弛 |
|
[1] |
|
AITD-00253 |
Complete Graph |
完全图 |
|
[1] |
|
AITD-00254 |
Complete Linkage |
完全连接 |
|
[1] |
|
AITD-00255 |
Complete-Data |
完全数据 |
|
[1] |
|
AITD-00256 |
Complex Cell |
复杂细胞 |
|
[1] |
|
AITD-00257 |
Component Learner |
组件学习器 |
|
[1] |
|
AITD-00258 |
Compositionality |
组合性 |
|
[1] |
|
AITD-00259 |
Comprehensibility |
可解释性 |
|
[1] |
|
AITD-00260 |
Computation Cost |
计算代价 |
|
[1] |
|
AITD-00261 |
Computation Graph |
计算图 |
|
[1] |
|
AITD-00262 |
Computational Learning Theory |
计算学习理论 |
|
[1] |
|
AITD-00263 |
Computational Linguistics |
计算语言学 |
|
[1] |
|
AITD-00264 |
Computer Vision |
计算机视觉 |
|
[1] |
|
AITD-00265 |
Concatenate |
连结 |
|
[1] |
|
AITD-00266 |
Concept Class |
概念类 |
|
[1] |
|
AITD-00267 |
Concept Drift |
概念漂移 |
|
[1] |
|
AITD-00268 |
Concept Learning System |
概念学习系统 |
CLS |
[1] |
|
AITD-00269 |
Concept Shift |
概念偏移 |
|
[1] |
|
AITD-00270 |
Conditional Computation |
条件计算 |
|
[1] |
|
AITD-00271 |
Conditional Entropy |
条件熵 |
|
[1] |
|
AITD-00272 |
Conditional Independence |
条件独立 |
|
[1] |
|
AITD-00273 |
Conditional Language Model |
条件语言模型 |
|
[1] |
|
AITD-00274 |
Conditional Mutual Information |
条件互信息 |
|
[1] |
|
AITD-00275 |
Conditional Probability |
条件概率 |
|
[1] |
|
AITD-00276 |
Conditional Probability Density Function |
条件概率密度函数 |
|
[1] |
|
AITD-00277 |
Conditional Probability Distribution |
条件概率分布 |
|
[1] |
|
AITD-00278 |
Conditional Probability Table |
条件概率表 |
CPT |
[1] |
|
AITD-00279 |
Conditional Random Field |
条件随机场 |
CRF |
[1] |
|
AITD-00280 |
Conditional Risk |
条件风险 |
|
[1] |
|
AITD-00281 |
Conditionally Independent |
条件独立的 |
|
[1] |
|
AITD-00282 |
Conference On Neural Information Processing Systems |
国际神经信息处理系统会议 |
NeurIPS |
[1] |
|
AITD-00283 |
Confidence |
置信度 |
|
[1] |
|
AITD-00284 |
Conflict Resolution |
冲突消解 |
|
[1] |
|
AITD-00285 |
Confusion Matrix |
混淆矩阵 |
|
[1] |
机器学习 |
AITD-00286 |
Conjugate |
共轭 |
|
[1] |
|
AITD-00287 |
Conjugate Directions |
共轭方向 |
|
[1] |
|
AITD-00288 |
Conjugate Distribution |
共轭分布 |
|
[1] |
|
AITD-00289 |
Conjugate Gradient |
共轭梯度 |
|
[1] |
优化,数学 |
AITD-00290 |
Conjugate Prior |
共轭先验 |
|
[1] |
|
AITD-00291 |
Connection Weight |
连接权 |
|
[1] |
|
AITD-00292 |
Connectionism |
连接主义 |
|
[1] |
|
AITD-00293 |
Consistency |
一致性 |
|
[1] |
|
AITD-00294 |
Consistency Convergence |
一致性收敛 |
|
[1] |
|
AITD-00295 |
Constrained Optimization |
约束优化 |
|
[1] |
|
AITD-00296 |
Content-Addressable Memory |
基于内容寻址的存储 |
CAM |
[1] |
|
AITD-00297 |
Context Variable |
上下文变量 |
|
[1] |
|
AITD-00298 |
Context Vector |
上下文向量 |
|
[1] |
|
AITD-00299 |
Context Window |
上下文窗口 |
|
[1] |
|
AITD-00300 |
Context Word |
上下文词 |
|
[1] |
|
AITD-00301 |
Context-Specific Independences |
特定上下文独立 |
|
[1] |
|
AITD-00302 |
Contextual Bandit |
上下文赌博机/上下文老虎机 |
|
[1] |
|
AITD-00303 |
Contextualized Representation |
基于上下文的表示 |
|
[1] |
|
AITD-00304 |
Contingency Table |
列联表 |
|
[1] |
|
AITD-00305 |
Continous Bag-Of-Words Model |
连续词袋模型 |
CBOW |
[1] |
|
AITD-00306 |
Continuation Method |
延拓法 |
|
[1] |
|
AITD-00307 |
Continuing Task |
持续式任务 |
|
[1] |
|
AITD-00308 |
Continuous Attribute |
连续属性 |
|
[1] |
|
AITD-00309 |
Continuous Learning |
持续学习 |
|
[1] |
|
AITD-00310 |
Continuous Optimization |
连续优化 |
|
[1] |
|
AITD-00311 |
Contractive |
收缩 |
|
[1] |
|
AITD-00312 |
Contractive Autoencoder |
收缩自编码器 |
|
[1] |
|
AITD-00313 |
Contractive Neural Network |
收缩神经网络 |
|
[1] |
|
AITD-00314 |
Contrastive Divergence |
对比散度 |
|
[1] |
|
AITD-00315 |
Controller |
控制器 |
|
[1] |
|
AITD-00316 |
Convergence |
收敛 |
|
[1] |
|
AITD-00317 |
Conversational Agent |
会话智能体 |
|
[1] |
|
AITD-00318 |
Convex Optimization |
凸优化 |
|
[1] |
|
AITD-00319 |
Convex Quadratic Programming |
凸二次规划 |
|
[1] |
|
AITD-00320 |
Convex Set |
凸集 |
|
[1] |
|
AITD-00321 |
Convexity |
凸性 |
|
[1] |
|
AITD-00322 |
Convolution |
卷积 |
|
[1] |
|
AITD-00323 |
Convolutional Boltzmann Machine |
卷积玻尔兹曼机 |
|
[1] |
|
AITD-00324 |
Convolutional Deep Belief Network |
卷积深度信念网络 |
CDBN |
[1] |
|
AITD-00325 |
Convolutional Kernel |
卷积核 |
|
[1] |
|
AITD-00326 |
Convolutional Network |
卷积网络 |
|
[1] |
|
AITD-00327 |
Convolutional Neural Network |
卷积神经网络 |
CNN |
[1][2][3] |
|
AITD-00328 |
Coordinate |
坐标 |
|
[1] |
|
AITD-00329 |
Coordinate Ascent |
坐标上升 |
|
[1] |
|
AITD-00330 |
Coordinate Descent |
坐标下降 |
|
[1] |
|
AITD-00331 |
Coparent |
共父 |
|
[1] |
|
AITD-00332 |
Corpus |
语料库 |
|
[1] |
|
AITD-00333 |
Correlation |
相关系数 |
|
[1] |
|
AITD-00334 |
Correlation Coefficient |
相关系数 |
|
[1] |
|
AITD-00335 |
Cosine |
余弦 |
|
[1] |
|
AITD-00336 |
Cosine Decay |
余弦衰减 |
|
[1] |
|
AITD-00337 |
Cosine Similarity |
余弦相似度 |
|
[1] |
|
AITD-00338 |
Cost |
代价 |
|
[1] |
|
AITD-00339 |
Cost Curve |
代价曲线 |
|
[1] |
|
AITD-00340 |
Cost Function |
代价函数 |
|
[1][2][3] |
|
AITD-00341 |
Cost Matrix |
代价矩阵 |
|
[1] |
|
AITD-00342 |
Cost-Sensitive |
代价敏感 |
|
[1] |
|
AITD-00343 |
Covariance |
协方差 |
|
[1] |
|
AITD-00344 |
Covariance Matrix |
协方差矩阵 |
|
[1] |
|
AITD-00345 |
Covariance RBM |
协方差RBM |
|
[1] |
|
AITD-00346 |
Covariate Shift |
协变量偏移 |
|
[1] |
|
AITD-00347 |
Coverage |
覆盖 |
|
[1] |
|
AITD-00348 |
Credit Assignment Problem |
贡献度分配问题 |
CAP |
[1] |
|
AITD-00349 |
Criterion |
准则 |
|
[1] |
|
AITD-00350 |
Critic |
评论员 |
|
[1] |
|
AITD-00351 |
Critic Network |
评价网络 |
|
[1] |
|
AITD-00352 |
Critical Point |
临界点 |
|
[1] |
|
AITD-00353 |
Critical Temperatures |
临界温度 |
|
[1] |
|
AITD-00354 |
Cross Correlation |
互相关 |
|
[1] |
|
AITD-00355 |
Cross Entropy |
交叉熵 |
|
[1] |
|
AITD-00356 |
Cross Validation |
交叉验证 |
|
[1] |
|
AITD-00357 |
Cross-Entropy Loss Function |
交叉熵损失函数 |
|
[1] |
|
AITD-00358 |
Crowdsourcing |
众包 |
|
[1] |
|
AITD-00359 |
Cumulative Distribution Function |
累积分布函数 |
CDF |
[1] |
|
AITD-00360 |
Cumulative Function |
累积函数 |
|
[1] |
|
AITD-00361 |
Curriculum Learning |
课程学习 |
|
[1] |
|
AITD-00362 |
Curse of Dimensionality |
维数灾难 |
|
[1] |
|
AITD-00363 |
Curvature |
曲率 |
|
[1] |
|
AITD-00364 |
Curve-Fitting |
曲线拟合 |
|
[1] |
|
AITD-00365 |
Cut Point |
截断点 |
|
[1] |
|
AITD-00366 |
Cutting Plane Algorithm |
割平面法 |
|
[1] |
|
AITD-00367 |
Cybernetics |
控制论 |
|
[1] |
|
AITD-00368 |
Cyclic Learning Rate |
循环学习率 |
|
[1] |
|
AITD-00369 |
Damping |
衰减 |
|
[1] |
|
AITD-00370 |
Damping Factor |
阻尼因子 |
|
[1] |
|
AITD-00371 |
Data |
数据 |
|
[1] |
|
AITD-00372 |
Data Augmentation |
数据增强 |
|
[1] |
|
AITD-00373 |
Data Generating Distribution |
数据生成分布 |
|
[1] |
|
AITD-00374 |
Data Generating Process |
数据生成过程 |
|
[1] |
|
AITD-00375 |
Data Instance |
数据样本 |
|
[1] |
|
AITD-00376 |
Data Mining |
数据挖掘 |
|
[1] |
|
AITD-00377 |
Data Parallelism |
数据并行 |
|
[1] |
|
AITD-00378 |
Data Point |
数据点 |
|
[1] |
|
AITD-00379 |
Data Preprocessing |
数据预处理 |
|
[1] |
|
AITD-00380 |
Data Set |
数据集 |
|
[1][2] |
|
AITD-00381 |
Data Wrangling |
数据整理 |
|
[1] |
|
AITD-00382 |
Dataset Augmentation |
数据集增强 |
|
[1] |
|
AITD-00383 |
Davidon-Fletcher-Powell |
DFP |
|
[1] |
|
AITD-00384 |
Debugging Strategy |
调试策略 |
|
[1] |
|
AITD-00385 |
Decision Boundary |
决策边界 |
|
[1] |
|
AITD-00386 |
Decision Function |
决策函数 |
|
[1] |
|
AITD-00387 |
Decision Stump |
决策树桩 |
|
[1] |
|
AITD-00388 |
Decision Surface |
决策平面 |
|
[1] |
|
AITD-00389 |
Decision Tree |
决策树 |
DT |
[1][2][3][4] |
|
AITD-00390 |
Decoder |
解码器 |
|
[1] |
|
AITD-00391 |
Decoding |
解码 |
|
[1] |
|
AITD-00392 |
Decompose |
分解 |
|
[1] |
|
AITD-00393 |
Deconvolution |
反卷积 |
|
[1] |
|
AITD-00394 |
Deconvolutional Network |
反卷积网络 |
|
[1] |
|
AITD-00395 |
Deduction |
演绎 |
|
[1] |
|
AITD-00396 |
Deep Belief Network |
深度信念网络 |
DBN |
[1] |
|
AITD-00397 |
Deep Boltzmann Machine |
深度玻尔兹曼机 |
DBM |
[1] |
|
AITD-00398 |
Deep Circuit |
深度回路 |
|
[1] |
|
AITD-00399 |
Deep Convolutional Generative Adversarial Network |
深度卷积生成对抗网络 |
DCGAN |
[1] |
|
AITD-00400 |
Deep Feedforward Network |
深度前馈网络 |
|
[1] |
|
AITD-00401 |
Deep Generative Model |
深度生成模型 |
|
[1] |
|
AITD-00402 |
Deep Learning |
深度学习 |
DL |
[1][2][3][4][5] |
|
AITD-00403 |
Deep Model |
深度模型 |
|
[1] |
|
AITD-00404 |
Deep Network |
深度网络 |
|
[1] |
|
AITD-00405 |
Deep Neural Network |
深度神经网络 |
DNN |
[1][2][3][4][5][6][7] |
|
AITD-00406 |
Deep Q-Learning |
深度 Q 学习 |
|
[1][2] |
|
AITD-00407 |
Deep Q-Network |
深度Q网络 |
DQN |
[1] |
|
AITD-00408 |
Deep Reinforcement Learning |
深度强化学习 |
|
[1] |
|
AITD-00409 |
Deep Sequence Model |
深度序列模型 |
|
[1] |
|
AITD-00410 |
Default Rule |
默认规则 |
|
[1] |
|
AITD-00411 |
Definite Integral |
定积分 |
|
[1] |
|
AITD-00412 |
Degree Of Belief |
信任度 |
|
[1] |
|
AITD-00413 |
Delta-Bar-Delta |
Delta-Bar-Delta |
|
[1] |
|
AITD-00414 |
Denoising |
去噪 |
|
[1] |
|
AITD-00415 |
Denoising Autoencoder |
去噪自编码器 |
|
[1] |
|
AITD-00416 |
Denoising Score Matching |
去躁分数匹配 |
|
[1] |
|
AITD-00417 |
Denominator Layout |
分母布局 |
|
[1] |
|
AITD-00418 |
Dense |
稠密 |
|
[1] |
|
AITD-00419 |
Density Estimation |
密度估计 |
|
[1] |
|
AITD-00420 |
Density-Based Clustering |
密度聚类 |
|
[1] |
|
AITD-00421 |
Dependency |
依赖 |
|
[1] |
|
AITD-00422 |
Depth |
深度 |
|
[1] |
|
AITD-00423 |
Derivative |
导数 |
|
[1] |
|
AITD-00424 |
Description |
描述 |
|
[1] |
|
AITD-00425 |
Design Matrix |
设计矩阵 |
|
[1] |
|
AITD-00426 |
Detailed Balance |
细致平衡 |
|
[1] |
|
AITD-00427 |
Detailed Balance Equation |
细致平衡方程 |
|
[1] |
|
AITD-00428 |
Detector Stage |
探测级 |
|
[1] |
|
AITD-00429 |
Determinant |
行列式 |
|
[1] |
|
AITD-00430 |
Deterministic |
确定性 |
|
[1] |
|
AITD-00431 |
Deterministic Model |
确定性模型 |
|
[1] |
|
AITD-00432 |
Deterministic Policy |
确定性策略 |
|
[1] |
|
AITD-00433 |
Development Set |
开发集 |
|
[1] |
|
AITD-00434 |
Diagonal Matrix |
对角矩阵 |
|
[1] |
|
AITD-00435 |
Diameter |
直径 |
|
[1] |
|
AITD-00436 |
Dictionary |
字典 |
|
[1] |
|
AITD-00437 |
Dictionary Learning |
字典学习 |
|
[1] |
|
AITD-00438 |
Differentiable Function |
可微函数 |
|
[1] |
|
AITD-00439 |
Differentiable Neural Computer |
可微分神经计算机 |
|
[1] |
|
AITD-00440 |
Differential Entropy |
微分熵 |
|
[1] |
|
AITD-00441 |
Differential Equation |
微分方程 |
|
[1] |
|
AITD-00442 |
Differentiation |
微分 |
|
[1] |
|
AITD-00443 |
Dilated Convolution |
膨胀卷积 |
|
[1] |
|
AITD-00444 |
Dimension |
维度 |
|
[1] |
|
AITD-00445 |
Dimension Reduction |
降维 |
|
[1] |
|
AITD-00446 |
Dimensionality Reduction Algorithm |
降维算法 |
|
[1][2] |
|
AITD-00447 |
Dirac Delta Function |
Dirac Delta函数 |
|
[1] |
|
AITD-00448 |
Dirac Distribution |
Dirac分布 |
|
[1] |
|
AITD-00449 |
Directed |
有向 |
|
[1] |
|
AITD-00450 |
Directed Acyclic Graph |
有向非循环图 |
DAG |
[1] |
|
AITD-00451 |
Directed Edge |
有向边 |
|
[1] |
|
AITD-00452 |
Directed Graph |
有向图 |
|
[1] |
|
AITD-00453 |
Directed Graphical Model |
有向图模型 |
|
[1] |
|
AITD-00454 |
Directed Model |
有向模型 |
|
[1] |
|
AITD-00455 |
Directed Separation |
有向分离 |
|
[1] |
|
AITD-00456 |
Directional Derivative |
方向导数 |
|
[1] |
|
AITD-00457 |
Dirichlet Distribution |
狄利克雷分布 |
|
[1] |
|
AITD-00458 |
Disagreement Measure |
不合度量 |
|
[1] |
|
AITD-00459 |
Disagreement-Based Methods |
基于分歧的方法 |
|
[1] |
|
AITD-00460 |
Discount Factor |
衰减系数 |
|
[1] |
|
AITD-00461 |
Discounted Return |
折扣回报 |
|
[1] |
|
AITD-00462 |
Discrete Optimization |
离散优化 |
|
[1] |
|
AITD-00463 |
Discriminant Function |
判别函数 |
|
[1] |
|
AITD-00464 |
Discriminative Approach |
判别方法 |
|
[1] |
|
AITD-00465 |
Discriminative Model |
判别式模型 |
|
[1] |
|
AITD-00466 |
Discriminative RBM |
判别RBM |
|
[1] |
|
AITD-00467 |
Discriminator |
判别器 |
|
[1] |
|
AITD-00468 |
Discriminator Network |
判别网络 |
|
[1] |
|
AITD-00469 |
Distance |
距离 |
|
[1] |
|
AITD-00470 |
Distance Measure |
距离度量 |
|
[1] |
|
AITD-00471 |
Distance Metric Learning |
距离度量学习 |
|
[1] |
|
AITD-00472 |
Distributed Representation |
分布式表示 |
|
[1] |
|
AITD-00473 |
Distribution |
分布 |
|
[1] |
|
AITD-00474 |
Diverge |
发散 |
|
[1] |
|
AITD-00475 |
Divergence |
散度 |
|
[1] |
|
AITD-00476 |
Diversity |
多样性 |
|
[1] |
|
AITD-00477 |
Diversity Measure |
多样性度量/差异性度量 |
|
[1] |
|
AITD-00478 |
Divide-And-Conquer |
分而治之 |
|
[1] |
|
AITD-00479 |
Divisive |
分裂 |
|
[1] |
|
AITD-00480 |
Domain |
领域 |
|
[1] |
|
AITD-00481 |
Domain Adaptation |
领域自适应 |
|
[1] |
|
AITD-00482 |
Dominant Eigenvalue |
主特征值 |
|
[1] |
|
AITD-00483 |
Dominant Eigenvector |
主特征向量 |
|
[1] |
|
AITD-00485 |
Dominant Strategy |
占优策略 |
|
[1] |
|
AITD-00486 |
Dot Product |
点积 |
|
[1] |
|
AITD-00487 |
Double Backprop |
双反向传播 |
|
[1] |
|
AITD-00488 |
Doubly Block Circulant Matrix |
双重分块循环矩阵 |
|
[1] |
|
AITD-00489 |
Down Sampling |
下采样 |
|
[1] |
|
AITD-00490 |
Downstream Task |
下游任务 |
|
[1] |
|
AITD-00491 |
Dropout |
暂退法 |
|
[1] |
|
AITD-00492 |
Dropout Boosting |
暂退Boosting |
|
[1] |
|
AITD-00493 |
Dropout Mask |
暂退掩码 |
|
[1] |
|
AITD-00494 |
Dropout Method |
暂退法 |
|
[1] |
|
AITD-00495 |
Dual Algorithm |
对偶算法 |
|
[1] |
|
AITD-00496 |
Dual Problem |
对偶问题 |
|
[1] |
|
AITD-00497 |
Dummy Node |
哑结点 |
|
[1] |
|
AITD-00498 |
Dying ReLU Problem |
死亡ReLU问题 |
|
[1] |
|
AITD-00499 |
Dynamic Bayesian Network |
动态贝叶斯网络 |
|
[1] |
|
AITD-00500 |
Dynamic Computational Graph |
动态计算图 |
|
[1] |
|
AITD-00501 |
Dynamic Fusion |
动态融合 |
|
[1] |
|
AITD-00502 |
Dynamic Programming |
动态规划 |
|
[1] |
|
AITD-00503 |
Dynamic Structure |
动态结构 |
|
[1] |
|
AITD-00504 |
Dynamical System |
动力系统 |
|
[1] |
|
AITD-00505 |
Eager Learning |
急切学习 |
|
[1] |
|
AITD-00506 |
Early Stopping |
早停 |
|
[1] |
|
AITD-00507 |
Earth-Mover's Distance |
推土机距离 |
EMD |
[1] |
|
AITD-00508 |
Echo State Network |
回声状态网络 |
|
[1] |
|
AITD-00509 |
Edge |
边 |
|
[1] |
|
AITD-00510 |
Edge Device |
边缘设备 |
|
[1] |
|
AITD-00511 |
Effective Capacity |
有效容量 |
|
[1] |
|
AITD-00512 |
Eigendecomposition |
特征分解 |
|
[1] |
|
AITD-00513 |
Eigenvalue |
特征值 |
|
[1] |
|
AITD-00514 |
Eigenvalue Decomposition |
特征值分解 |
|
[1] |
|
AITD-00515 |
Elastic Net Regularization |
弹性网络正则化 |
|
[1] |
|
AITD-00516 |
Elastic Weight Consolidation |
弹性权重巩固 |
|
[1] |
|
AITD-00517 |
Element-Wise Product |
逐元素积 |
|
[1] |
|
AITD-00518 |
Elementary Basis Vectors |
基本单位向量 |
|
[1] |
|
AITD-00519 |
Ellipsoid Method |
椭球法 |
|
[1] |
|
AITD-00520 |
Embedding |
嵌入 |
|
[1] |
|
AITD-00521 |
Embedding Lookup Table |
嵌入表 |
|
[1] |
|
AITD-00522 |
Emotional Analysis |
情绪分析 |
|
[1] |
|
AITD-00523 |
Empirical Conditional Entropy |
经验条件熵 |
|
[1] |
|
AITD-00524 |
Empirical Distribution |
经验分布 |
|
[1] |
|
AITD-00525 |
Empirical Entropy |
经验熵 |
|
[1] |
|
AITD-00526 |
Empirical Error |
经验误差 |
|
[1] |
|
AITD-00527 |
Empirical Frequency |
经验频率 |
|
[1] |
|
AITD-00528 |
Empirical Loss |
经验损失 |
|
[1] |
|
AITD-00529 |
Empirical Risk |
经验风险 |
|
[1] |
|
AITD-00530 |
Empirical Risk Minimization |
经验风险最小化 |
ERM |
[1] |
|
AITD-00531 |
Encoder |
编码器 |
|
[1] |
|
AITD-00532 |
Encoder-Decoder |
编码器-解码器(模型) |
|
[1][2] |
|
AITD-00533 |
Encoding |
编码 |
|
[1] |
|
AITD-00534 |
End-To-End |
端到端 |
|
[1] |
|
AITD-00535 |
End-To-End Learning |
端到端学习 |
|
[1] |
|
AITD-00536 |
End-To-End Memory Network |
端到端记忆网络 |
Memn2N |
[1] |
|
AITD-00537 |
Energy Function |
能量函数 |
|
[1] |
|
AITD-00538 |
Energy Gap |
能量差异 |
|
[1] |
|
AITD-00539 |
Energy-Based Model |
基于能量的模型 |
|
[1] |
|
AITD-00540 |
Ensemble |
集成 |
|
[1] |
|
AITD-00541 |
Ensemble Learning |
集成学习 |
|
[1] |
|
AITD-00542 |
Ensemble Pruning |
集成修剪 |
|
[1] |
|
AITD-00543 |
Entropy |
熵 |
|
[1] |
|
AITD-00544 |
Entropy Encoding |
熵编码 |
|
[1] |
|
AITD-00545 |
Environment |
环境 |
|
[1] |
|
AITD-00546 |
Episode |
回合 |
|
[1] |
|
AITD-00547 |
Episodic Task |
回合式任务 |
|
[1] |
|
AITD-00548 |
Epoch |
轮 |
|
[1] |
|
AITD-00549 |
Equal-Width Convolution |
等宽卷积 |
|
[1] |
|
AITD-00550 |
Equality Constraint |
等式约束 |
|
[1] |
|
AITD-00551 |
Equilibrium Distribution |
均衡分布 |
|
[1] |
|
AITD-00552 |
Equivariance |
等变 |
|
[1] |
|
AITD-00553 |
Equivariant Representations |
等变表示 |
|
[1] |
|
AITD-00554 |
Error |
误差 |
|
[1] |
|
AITD-00555 |
Error Backpropagation Algorithm |
误差反向传播算法 |
|
[1] |
|
AITD-00556 |
Error Backpropagation |
误差反向传播 |
|
[1] |
|
AITD-00557 |
Error Bar |
误差条 |
|
[1] |
|
AITD-00558 |
Error Correcting Output Codes |
纠错输出编码 |
ECOC |
[1] |
|
AITD-00559 |
Error Function |
误差函数 |
|
[1] |
|
AITD-00560 |
Error Metric |
误差度量 |
|
[1] |
|
AITD-00561 |
Error Rate |
错误率 |
|
[1] |
|
AITD-00562 |
Error-Ambiguity Decomposition |
误差-分歧分解 |
|
[1] |
|
AITD-00563 |
Estimation Error |
估计误差 |
|
[1] |
|
AITD-00564 |
Estimation Of Mathematical Expectation |
数学期望估计 |
|
[1] |
|
AITD-00565 |
Estimator |
估计/估计量 |
|
[1] |
|
AITD-00566 |
Euclidean Distance |
欧氏距离 |
|
[1] |
|
AITD-00567 |
Euclidean Norm |
欧几里得范数 |
|
[1] |
|
AITD-00568 |
Euclidean Space |
欧氏空间 |
|
[1] |
|
AITD-00569 |
Euler-Lagrange Equation |
欧拉-拉格朗日方程 |
|
[1] |
|
AITD-00570 |
Evaluation Criterion |
评价准则 |
|
[1] |
|
AITD-00571 |
Evidence |
证据 |
|
[1] |
|
AITD-00572 |
Evidence Lower Bound |
证据下界 |
ELBO |
[1] |
|
AITD-00573 |
Evolution |
演化 |
|
[1] |
|
AITD-00574 |
Evolutionary Computation |
演化计算 |
|
[1] |
|
AITD-00575 |
Exact |
确切的 |
|
[1] |
|
AITD-00576 |
Exact Inference |
精确推断 |
|
[1] |
|
AITD-00577 |
Example |
样例 |
|
[1] |
|
AITD-00578 |
Excess Error |
额外误差 |
|
[1] |
|
AITD-00579 |
Exchangeable |
可交换的 |
|
[1] |
|
AITD-00580 |
Expectation |
期望 |
|
[1] |
|
AITD-00581 |
Expectation Maximization Algorithm |
期望极大算法 |
|
[1] |
|
AITD-00582 |
Expectation Maximization |
期望最大化 |
EM |
[1] |
|
AITD-00583 |
Expectation Step |
E步 |
|
[1] |
|
AITD-00584 |
Expected Error |
期望错误 |
|
[1] |
|
AITD-00585 |
Expected Loss |
期望损失 |
|
[1] |
|
AITD-00586 |
Expected Return |
期望回报 |
|
[1] |
|
AITD-00587 |
Expected Risk |
期望风险 |
|
[1] |
|
AITD-00588 |
Expected Value |
期望值 |
|
[1] |
|
AITD-00589 |
Experience |
经验 |
|
[1] |
|
AITD-00590 |
Experience Replay |
经验回放 |
|
[1] |
|
AITD-00591 |
Expert Network |
专家网络 |
|
[1] |
|
AITD-00592 |
Expert System |
专家系统 |
|
[1] |
|
AITD-00593 |
Explaining Away |
相消解释 |
|
[1] |
|
AITD-00594 |
Explaining Away Effect |
相消解释作用 |
|
[1] |
|
AITD-00595 |
Explanatory Factort |
解释因子 |
|
[1] |
|
AITD-00596 |
Explicit Density Model |
显式密度模型 |
|
[1] |
|
AITD-00597 |
Exploding Gradient |
梯度爆炸 |
|
[1] |
|
AITD-00598 |
Exploitation |
利用 |
|
[1] |
|
AITD-00599 |
Exploration |
探索 |
|
[1] |
|
AITD-00600 |
Exploration-Exploitation Dilemma |
探索-利用窘境 |
|
[1] |
|
AITD-00601 |
Exponential Decay |
指数衰减 |
|
[1] |
|
AITD-00602 |
Exponential Distribution |
指数分布 |
|
[1] |
|
AITD-00603 |
Exponential Linear Unit |
指数线性单元 |
ELU |
[1] |
|
AITD-00604 |
Exponential Loss |
指数损失 |
|
[1] |
|
AITD-00605 |
Exponential Loss Function |
指数损失函数 |
|
[1] |
|
AITD-00606 |
Exponentially Weighted Moving Average |
指数加权移动平均 |
|
[1] |
|
AITD-00607 |
Exposure Bias |
曝光偏差 |
|
[1] |
|
AITD-00608 |
External Memory |
外部记忆 |
|
[1] |
|
AITD-00609 |
Extreme Learning Machine |
超限学习机 |
ELM |
[1] |
|
AITD-00610 |
F Measure |
F值 |
|
[1] |
|
AITD-00611 |
F-Score |
F分数 |
|
[1] |
|
AITD-00612 |
Factor |
因子 |
|
[1] |
|
AITD-00613 |
Factor Analysis |
因子分析 |
|
[1] |
|
AITD-00614 |
Factor Graph |
因子图 |
|
[1] |
|
AITD-00615 |
Factor Loading |
因子负荷量 |
|
[1] |
|
AITD-00616 |
Factorization |
因子分解 |
|
[1] |
|
AITD-00617 |
Factorized |
分解的 |
|
[1] |
|
AITD-00618 |
Factors of Variation |
变差因素 |
|
[1] |
|
AITD-00619 |
False Negative |
假负例 |
|
[1] |
|
AITD-00620 |
False Positive |
假正例 |
|
[1] |
|
AITD-00621 |
False Positive Rate |
假正例率 |
FPR |
[1] |
|
AITD-00622 |
Fast Dropout |
快速暂退法 |
|
[1] |
|
AITD-00623 |
Fast Persistent Contrastive Divergence |
快速持续性对比散度 |
|
[1] |
|
AITD-00624 |
Fault-Tolerant Asynchronous Training |
容错异步训练 |
|
[1] |
|
AITD-00625 |
Feasible |
可行 |
|
[1] |
|
AITD-00626 |
Feature |
特征 |
|
[1] |
|
AITD-00627 |
Feature Engineering |
特征工程 |
|
[1] |
|
AITD-00628 |
Feature Extraction |
特征抽取 |
|
[1] |
|
AITD-00629 |
Feature Extractor |
特征提取器 |
|
[1] |
|
AITD-00630 |
Feature Function |
特征函数 |
|
[1] |
|
AITD-00631 |
Feature Map |
特征图 |
|
[1] |
|
AITD-00632 |
Feature Scaling Transform |
特征尺度变换 |
|
[1] |
|
AITD-00633 |
Feature Selection |
特征选择 |
|
[1][2][3] |
|
AITD-00634 |
Feature Space |
特征空间 |
|
[1] |
|
AITD-00635 |
Feature Vector |
特征向量 |
|
[1] |
|
AITD-00636 |
Featured Learning |
特征学习 |
|
[1] |
|
AITD-00637 |
Feedback |
反馈 |
|
[1] |
|
AITD-00638 |
Feedforward |
前馈 |
|
[1] |
|
AITD-00639 |
Feedforward Classifier |
前馈分类器 |
|
[1] |
|
AITD-00640 |
Feedforward Network |
前馈网络 |
|
[1] |
|
AITD-00641 |
Feedforward Neural Network |
前馈神经网络 |
FNN |
[1][2][3] |
|
AITD-00642 |
Few-Shot Learning |
少试学习 |
|
[1] |
|
AITD-00643 |
Fidelity |
逼真度 |
|
[1] |
|
AITD-00644 |
Field Programmable Gated Array |
现场可编程门阵列 |
|
[1] |
|
AITD-00645 |
Filter |
滤波器 |
|
[1] |
|
AITD-00646 |
Filter Method |
过滤式方法 |
|
[1] |
|
AITD-00647 |
Fine-Tuning |
微调 |
|
[1] |
|
AITD-00648 |
Finite Difference |
有限差分 |
|
[1] |
|
AITD-00649 |
First Layer |
第一层 |
|
[1] |
|
AITD-00650 |
First-Order Method |
一阶方法 |
|
[1] |
|
AITD-00651 |
First-Order Rule |
一阶规则 |
|
[1] |
|
AITD-00652 |
Fisher Information Matrix |
Fisher信息矩阵 |
|
[1] |
|
AITD-00653 |
Fixed Point Equation |
不动点方程 |
|
[1] |
|
AITD-00654 |
Fixed-Point Arithmetic |
不动点运算 |
|
[1] |
|
AITD-00655 |
Flat Minima |
平坦最小值 |
|
[1] |
|
AITD-00656 |
Flip |
翻转 |
|
[1] |
|
AITD-00657 |
Flipping Output |
翻转法 |
|
[1] |
|
AITD-00658 |
Float-Point Arithmetic |
浮点运算 |
|
[1] |
|
AITD-00659 |
Fluctuation |
振荡 |
|
[1] |
|
AITD-00660 |
Focus Attention |
聚焦式注意力 |
|
[1] |
|
AITD-00661 |
Folk Theorem |
无名氏定理 |
|
[1] |
|
AITD-00662 |
Forget Gate |
遗忘门 |
|
[1] |
|
AITD-00663 |
Forward |
前向 |
|
[1] |
|
AITD-00664 |
Forward KL Divergence |
前向KL散度 |
|
[1] |
|
AITD-00665 |
Forward Mode Accumulation |
前向模式累加 |
|
[1] |
|
AITD-00666 |
Forward Propagation |
前向传播/正向传播 |
|
[1] |
|
AITD-00667 |
Forward Search |
前向搜索 |
|
[1] |
|
AITD-00668 |
Forward Stagewise Algorithm |
前向分步算法 |
|
[1] |
|
AITD-00669 |
Forward-Backward Algorithm |
前向-后向算法 |
|
[1] |
|
AITD-00670 |
Fourier Transform |
傅立叶变换 |
|
[1] |
|
AITD-00671 |
Fovea |
中央凹 |
|
[1] |
|
AITD-00672 |
Fractionally Strided Convolution |
微步卷积 |
|
[1] |
|
AITD-00673 |
Free Energy |
自由能 |
|
[1] |
|
AITD-00674 |
Frequentist |
频率主义学派 |
|
[1] |
|
AITD-00675 |
Frequentist Probability |
频率派概率 |
|
[1] |
|
AITD-00676 |
Frequentist Statistics |
频率派统计 |
|
[1] |
|
AITD-00677 |
Frobenius Norm |
Frobenius 范数 |
|
[1] |
|
AITD-00678 |
Full |
全 |
|
[1] |
|
AITD-00679 |
Full Conditional Distribution |
满条件分布 |
|
[1] |
|
AITD-00680 |
Full Conditional Probability |
全条件概率 |
|
[1] |
|
AITD-00681 |
Full Padding |
全填充 |
|
[1] |
|
AITD-00682 |
Full Singular Value Decomposition |
完全奇异值分解 |
|
[1] |
|
AITD-00683 |
Full-Rank Matrix |
满秩矩阵 |
|
[1] |
|
AITD-00684 |
Fully Connected Layer |
全连接层 |
|
[1] |
|
AITD-00685 |
Fully Connected Neural Network |
全连接神经网络 |
FCNN |
[1] |
|
AITD-00686 |
Fully Convolutional Network |
全卷积网络 |
FCN |
[1] |
|
AITD-00687 |
Function |
函数 |
|
[1] |
|
AITD-00688 |
Functional |
泛函 |
|
[1] |
|
AITD-00689 |
Functional Derivative |
泛函导数 |
|
[1] |
|
AITD-00690 |
Functional Margin |
函数间隔 |
|
[1] |
|
AITD-00691 |
Functional Neuron |
功能神经元 |
|
[1] |
|
AITD-00692 |
Gabor Function |
Gabor函数 |
|
[1] |
|
AITD-00693 |
Gain Ratio |
増益率 |
|
[1] |
|
AITD-00694 |
Game Payoff |
博弈效用 |
|
[1] |
|
AITD-00695 |
Game Theory |
博弈论 |
|
[1] |
|
AITD-00696 |
Gamma Distribution |
Gamma分布 |
|
[1] |
|
AITD-00697 |
Gate |
门 |
|
[1] |
|
AITD-00698 |
Gate Controlled RNN |
门控循环神经网络 |
|
[1] |
|
AITD-00699 |
Gated |
门控 |
|
[1] |
|
AITD-00700 |
Gated Control |
门控 |
|
[1] |
|
AITD-00701 |
Gated Recurrent Net |
门控循环网络 |
GRN |
[1] |
|
AITD-00702 |
Gated Recurrent Unit |
门控循环单元 |
GRU |
[1] |
|
AITD-00703 |
Gated RNN |
门控RNN |
|
[1] |
|
AITD-00704 |
Gater |
选通器 |
|
[1] |
|
AITD-00705 |
Gating Mechanism |
门控机制 |
|
[1] |
|
AITD-00706 |
Gaussian Distribution |
高斯分布 |
|
[1] |
|
AITD-00707 |
Gaussian Error Linear Unit |
高斯误差线性单元 |
GELU |
[1] |
|
AITD-00708 |
Gaussian Kernel |
高斯核 |
|
[1] |
|
AITD-00709 |
Gaussian Kernel Function |
高斯核函数 |
|
[1] |
|
AITD-00710 |
Gaussian Mixture Model |
高斯混合模型 |
GMM |
[1] |
|
AITD-00711 |
Gaussian Mixtures |
高斯混合(模型) |
|
[1] |
|
AITD-00712 |
Gaussian Output Distribution |
高斯输出分布 |
|
[1] |
|
AITD-00713 |
Gaussian Process |
高斯过程 |
GP |
[1][2] |
|
AITD-00714 |
Gaussian Process Regression |
高斯过程回归 |
GPR |
[1] |
|
AITD-00715 |
Gaussian RBM |
高斯RBM |
|
[1] |
|
AITD-00716 |
Gaussian-Bernoulli RBM |
高斯-伯努利RBM |
|
[1] |
|
AITD-00717 |
General Problem Solving |
通用问题求解 |
|
[1] |
|
AITD-00718 |
General Purpose GPU |
通用GPU |
|
[1] |
|
AITD-00719 |
Generalization Ability |
泛化能力 |
|
[1] |
|
AITD-00720 |
Generalization Error |
泛化误差 |
|
[1] |
|
AITD-00721 |
Generalization Error Bound |
泛化误差上界 |
|
[1] |
|
AITD-00722 |
Generalize |
泛化 |
|
[1] |
|
AITD-00723 |
Generalized Bregman Divergence |
一般化 Bregman 散度 |
|
[1] |
|
AITD-00724 |
Generalized Expectation Maximization |
广义期望极大 |
GEM |
[1] |
|
AITD-00725 |
Generalized Function |
广义函数 |
|
[1] |
|
AITD-00726 |
Generalized Lagrange Function |
广义拉格朗日函数 |
|
[1] |
|
AITD-00727 |
Generalized Lagrangian |
广义拉格朗日 |
|
[1] |
|
AITD-00728 |
Generalized Linear Model |
广义线性模型 |
|
[1] |
|
AITD-00729 |
Generalized Pseudolikelihood |
广义伪似然 |
|
[1] |
|
AITD-00730 |
Generalized Pseudolikelihood Estimator |
广义伪似然估计 |
|
[1] |
|
AITD-00731 |
Generalized Rayleigh Quotient |
广义瑞利商 |
|
[1] |
|
AITD-00732 |
Generalized Score Matching |
广义得分匹配 |
|
[1] |
|
AITD-00733 |
Generative Adversarial Framework |
生成式对抗框架 |
|
[1] |
|
AITD-00734 |
Generative Adversarial Network |
生成对抗网络 |
|
[1][2][3] |
|
AITD-00735 |
Generative Approach |
生成方法 |
|
[1] |
|
AITD-00736 |
Generative Model |
生成式模型 |
|
[1][2][3] |
|
AITD-00737 |
Generative Modeling |
生成式建模 |
|
[1] |
机器学习 |
AITD-00738 |
Generative Moment Matching Network |
生成矩匹配网络 |
|
[1] |
|
AITD-00739 |
Generative Pre-Training |
生成式预训练 |
GPT |
[1] |
|
AITD-00740 |
Generative Stochastic Network |
生成随机网络 |
|
[1] |
|
AITD-00741 |
Generative Weight |
生成权重 |
|
[1] |
|
AITD-00742 |
Generator |
生成器 |
|
[1] |
|
AITD-00743 |
Generator Network |
生成器网络 |
|
[1] |
|
AITD-00744 |
Genetic Algorithm |
遗传算法 |
GA |
[1][2][3][4][5][6] |
机器学习 |
AITD-00745 |
Geometric Margin |
几何间隔 |
|
[1] |
|
AITD-00746 |
Giant Magnetoresistance |
巨磁阻 |
|
[1] |
|
AITD-00747 |
Gibbs Distribution |
吉布斯分布 |
|
[1] |
|
AITD-00748 |
Gibbs Sampling |
吉布斯采样/吉布斯抽样 |
|
[1] |
|
AITD-00749 |
Gibbs Steps |
吉布斯步数 |
|
[1] |
|
AITD-00750 |
Gini Index |
基尼指数 |
|
[1] |
|
AITD-00751 |
Global Contrast Normalization |
全局对比度规范化 |
|
[1] |
|
AITD-00752 |
Global Markov Property |
全局马尔可夫性 |
|
[1] |
|
AITD-00753 |
Global Minima |
全局极小值 |
|
[1] |
|
AITD-00754 |
Global Minimizer |
全局极小解 |
|
[1] |
|
AITD-00755 |
Global Minimum |
全局最小 |
|
[1] |
|
AITD-00756 |
Global Optimization |
全局优化 |
|
[1] |
|
AITD-00757 |
Gradient |
梯度 |
|
[1] |
|
AITD-00758 |
Gradient Ascent |
梯度上升 |
|
[1] |
|
AITD-00759 |
Gradient Ascent Method |
梯度上升法 |
|
[1] |
|
AITD-00760 |
Gradient Boosting |
梯度提升 |
|
[1] |
|
AITD-00761 |
Gradient Boosting Tree |
梯度提升树 |
|
[1] |
|
AITD-00762 |
Gradient Clipping |
梯度截断 |
|
[1] |
|
AITD-00763 |
Gradient Descent |
梯度下降 |
|
[1][2][3] |
机器学习 |
AITD-00764 |
Gradient Descent In One-Dimensional Space |
一维梯度下降 |
|
[1] |
|
AITD-00765 |
Gradient Descent Method |
梯度下降法 |
|
[1] |
|
AITD-00766 |
Gradient Energy Distribution |
梯度能量分布 |
|
[1] |
|
AITD-00767 |
Gradient Estimation |
梯度估计 |
|
[1] |
|
AITD-00768 |
Gradient Exploding Problem |
梯度爆炸问题 |
|
[1] |
|
AITD-00769 |
Gradient Field |
梯度场 |
|
[1] |
|
AITD-00770 |
Gradual Warmup |
逐渐预热 |
|
[1] |
|
AITD-00771 |
Gram Matrix |
Gram 矩阵 |
|
[1] |
|
AITD-00772 |
Graph |
图 |
|
[1] |
|
AITD-00773 |
Graph Analytics |
图分析 |
|
[1] |
|
AITD-00774 |
Graph Attention Network |
图注意力网络 |
GAT |
[1] |
|
AITD-00775 |
Graph Convolutional Network |
图卷积神经网络/图卷积网络 |
GCN |
[1] |
|
AITD-00776 |
Graph Neural Network |
图神经网络 |
GNN |
[1] |
|
AITD-00777 |
Graph Theory |
图论 |
|
[1] |
|
AITD-00778 |
Graphical Model |
图模型 |
GM |
[1] |
|
AITD-00779 |
Graphics Processing Unit |
图形处理器 |
|
[1] |
|
AITD-00780 |
Greedy |
贪心 |
|
[1] |
|
AITD-00781 |
Greedy Algorithm |
贪心算法 |
|
[1] |
|
AITD-00782 |
Greedy Layer-Wise Pretraining |
贪心逐层预训练 |
|
[1] |
|
AITD-00783 |
Greedy Layer-Wise Training |
贪心逐层训练 |
|
[1] |
|
AITD-00784 |
Greedy Layer-Wise Unsupervised Pretraining |
贪心逐层无监督预训练 |
|
[1] |
|
AITD-00785 |
Greedy Search |
贪心搜索 |
|
[1] |
|
AITD-00786 |
Greedy Supervised Pretraining |
贪心监督预训练 |
|
[1] |
|
AITD-00787 |
Greedy Unsupervised Pretraining |
贪心无监督预训练 |
|
[1] |
|
AITD-00788 |
Grid Search |
网格搜索 |
|
[1] |
|
AITD-00789 |
Grid World |
网格世界 |
|
[1] |
|
AITD-00790 |
Ground Truth |
真实值 |
|
[1] |
|
AITD-00791 |
Growth Function |
增长函数 |
|
[1] |
|
AITD-00792 |
Hadamard Product |
Hadamard积 |
|
[1] |
|
AITD-00793 |
Hamming Distance |
汉明距离 |
|
[1] |
|
AITD-00794 |
Hard Attention |
硬性注意力 |
|
[1] |
|
AITD-00795 |
Hard Clustering |
硬聚类 |
|
[1] |
|
AITD-00796 |
Hard Margin |
硬间隔 |
|
[1] |
|
AITD-00797 |
Hard Margin Maximization |
硬间隔最大化 |
|
[1] |
|
AITD-00798 |
Hard Mixture Of Experts |
硬专家混合体 |
|
[1] |
|
AITD-00799 |
Hard Tanh |
硬双曲正切函数 |
|
[1] |
|
AITD-00800 |
Hard Target |
硬目标 |
|
[1] |
|
AITD-00801 |
Hard Voting |
硬投票 |
|
[1] |
|
AITD-00802 |
Harmonic Mean |
调和平均 |
|
[1] |
|
AITD-00803 |
Harmonium |
簧风琴 |
|
[1] |
|
AITD-00804 |
Harmony |
Harmony |
|
[1] |
|
AITD-00805 |
Harris Chain |
哈里斯链 |
|
[1] |
|
AITD-00806 |
Hausdorff Distance |
豪斯多夫距离 |
|
[1] |
|
AITD-00807 |
Hebbian Rule |
赫布法则 |
|
[1] |
|
AITD-00808 |
Hebbian Theory |
赫布理论 |
|
[1] |
|
AITD-00809 |
Helmholtz Machine |
Helmholtz机 |
|
[1] |
|
AITD-00810 |
Hesse Matrix |
海赛矩阵 |
|
[1] |
|
AITD-00811 |
Hessian |
Hessian |
|
[1] |
|
AITD-00812 |
Hessian Matrix |
黑塞矩阵 |
|
[1] |
|
AITD-00813 |
Heterogeneous Information Network |
异质信息网络 |
HIN |
[1] |
|
AITD-00814 |
Heteroscedastic |
异方差 |
|
[1] |
|
AITD-00815 |
Hidden Dynamic Model |
隐动态模型 |
|
[1] |
|
AITD-00816 |
Hidden Layer |
隐藏层 |
|
[1] |
|
AITD-00817 |
Hidden Markov Model |
隐马尔可夫模型 |
HMM |
[1] |
|
AITD-00818 |
Hidden State |
隐状态 |
|
[1] |
|
AITD-00819 |
Hidden Unit |
隐藏单元 |
|
[1] |
|
AITD-00820 |
Hidden Variable |
隐变量 |
|
[1] |
|
AITD-00821 |
Hierarchical Clustering |
层次聚类 |
|
[1] |
|
AITD-00822 |
Hierarchical Reinforcement Learning |
分层强化学习 |
HRL |
[1] |
|
AITD-00823 |
Hierarchical Softmax |
层序Softmax/层序软最大化 |
|
[1] |
|
AITD-00824 |
Hilbert Space |
希尔伯特空间 |
|
[1] |
|
AITD-00825 |
Hill Climbing |
爬山 |
|
[1] |
|
AITD-00826 |
Hinge Loss Function |
合页损失函数/Hinge损失函数 |
|
[1] |
|
AITD-00827 |
Histogram Method |
直方图方法 |
|
[1] |
|
AITD-00828 |
Hold-Out |
留出法 |
|
[1] |
|
AITD-00829 |
Homogeneous |
同质 |
|
[1] |
|
AITD-00830 |
Hopfield Network |
Hopfield网络 |
|
[1] |
|
AITD-00831 |
Huffman Coding |
霍夫曼编码 |
|
[1] |
|
AITD-00832 |
Hybrid Computing |
混合计算 |
|
[1] |
|
AITD-00833 |
Hyperbolic Tangent Function |
双曲正切函数 |
|
[1] |
|
AITD-00834 |
Hyperparameter |
超参数 |
|
[1][2] |
|
AITD-00835 |
Hyperparameter Optimization |
超参数优化 |
|
[1] |
|
AITD-00836 |
Hyperplane |
超平面 |
|
[1] |
数学 |
AITD-00837 |
Hypothesis |
假设 |
|
[1] |
|
AITD-00838 |
Hypothesis Space |
假设空间 |
|
[1] |
|
AITD-00839 |
Hypothesis Test |
假设检验 |
|
[1] |
|
AITD-00840 |
I.I.D. Assumption |
独立同分布假设 |
|
[1] |
|
AITD-00841 |
Identically Distributed |
同分布的 |
|
[1] |
|
AITD-00842 |
Identifiable |
可辨认的 |
|
[1] |
|
AITD-00843 |
Identity Function |
恒等函数 |
|
[1] |
|
AITD-00844 |
Identity Mapping |
恒等映射 |
|
[1] |
|
AITD-00845 |
Identity Matrix |
单位矩阵 |
|
[1] |
|
AITD-00846 |
Ill Conditioning |
病态 |
|
[1] |
|
AITD-00847 |
Ill-Formed Problem |
病态问题 |
|
[1] |
|
AITD-00848 |
Image |
图像 |
|
[1] |
|
AITD-00849 |
Image Restoration |
图像还原 |
|
[1] |
|
AITD-00850 |
Imitation Learning |
模仿学习 |
|
[1] |
|
AITD-00851 |
Immorality |
不道德 |
|
[1] |
|
AITD-00852 |
Imperfect Information |
不完美信息 |
|
[1] |
|
AITD-00853 |
Implicit Density Model |
隐式密度模型 |
|
[1] |
|
AITD-00854 |
Import |
导入 |
|
[1] |
|
AITD-00855 |
Importance Sampling |
重要性采样 |
|
[1] |
|
AITD-00856 |
Improved Iterative Scaling |
改进的迭代尺度法 |
IIS |
[1] |
|
AITD-00857 |
Incomplete-Data |
不完全数据 |
|
[1] |
|
AITD-00858 |
Incremental Learning |
增量学习 |
|
[1] |
|
AITD-00859 |
Indefinite Integral |
不定积分 |
|
[1] |
|
AITD-00860 |
Independence |
独立 |
|
[1] |
|
AITD-00861 |
Independent |
相互独立的 |
|
[1] |
|
AITD-00862 |
Independent and Identically Distributed |
独立同分布 |
I.I.D. |
[1] |
|
AITD-00863 |
Independent Component Analysis |
独立成分分析 |
ICA |
[1] |
|
AITD-00864 |
Independent Subspace Analysis |
独立子空间分析 |
|
[1] |
|
AITD-00865 |
Index of Matrix |
索引 |
|
[1] |
|
AITD-00866 |
Indicator Function |
指示函数 |
|
[1] |
|
AITD-00867 |
Individual Learner |
个体学习器 |
|
[1] |
|
AITD-00868 |
Induction |
归纳 |
|
[1] |
|
AITD-00869 |
Inductive Bias |
归纳偏好 |
|
[1] |
|
AITD-00870 |
Inductive Learning |
归纳学习 |
|
[1] |
|
AITD-00871 |
Inductive Logic Programming |
归纳逻辑程序设计 |
ILP |
[1] |
|
AITD-00872 |
Inductive Transfer Learning |
归纳迁移学习 |
|
[1] |
|
AITD-00873 |
Inequality Constraint |
不等式约束 |
|
[1] |
|
AITD-00874 |
Inference |
推断 |
|
[1] |
|
AITD-00875 |
Infinite |
无限 |
|
[1] |
|
AITD-00876 |
Infinitely Exchangeable |
无限可交换 |
|
[1] |
|
AITD-00877 |
Information Divergence |
信息散度 |
|
[1] |
|
AITD-00878 |
Information Entropy |
信息熵 |
|
[1] |
|
AITD-00879 |
Information Gain |
信息增益 |
|
[1] |
统计 |
AITD-00880 |
Information Gain Ratio |
信息增益比 |
|
[1] |
统计 |
AITD-00881 |
Information Retrieval |
信息检索 |
|
[1] |
|
AITD-00882 |
Information Theory |
信息论 |
|
[1] |
|
AITD-00883 |
Inner Product |
内积 |
|
[1] |
|
AITD-00884 |
Input |
输入 |
|
[1] |
|
AITD-00885 |
Input Distribution |
输入分布 |
|
[1] |
|
AITD-00886 |
Input Gate |
输入门 |
|
[1] |
|
AITD-00887 |
Input Layer |
输入层 |
|
[1] |
|
AITD-00888 |
Input Space |
输入空间 |
|
[1] |
|
AITD-00889 |
Insensitive Loss |
不敏感损失 |
|
[1] |
|
AITD-00890 |
Instance |
示例 |
|
[1] |
|
AITD-00891 |
Instance Segmentation |
实例分割 |
|
[1] |
|
AITD-00892 |
Integer Linear Programming |
整数线性规划 |
ILP |
[1] |
|
AITD-00893 |
Integer Programming |
整数规划 |
|
[1] |
|
AITD-00894 |
Integration |
积分 |
|
[1] |
|
AITD-00895 |
Inter-Cluster Similarity |
簇间相似度 |
|
[1] |
|
AITD-00896 |
Internal Covariate Shift |
内部协变量偏移 |
|
[1] |
|
AITD-00897 |
Internal Node |
内部结点 |
|
[1] |
|
AITD-00898 |
International Conference For Machine Learning |
国际机器学习大会 |
ICML |
[1] |
|
AITD-00899 |
Intervention Query |
干预查询 |
|
[1] |
|
AITD-00900 |
Intra-Attention |
内部注意力 |
|
[1] |
|
AITD-00901 |
Intra-Cluster Similarity |
簇内相似度 |
|
[1] |
|
AITD-00902 |
Intrinsic Value |
固有值 |
|
[1] |
|
AITD-00903 |
Invariance |
不变性 |
|
[1] |
|
AITD-00904 |
Invariant |
不变 |
|
[1] |
|
AITD-00905 |
Inverse Matrix |
逆矩阵 |
|
[1] |
|
AITD-00906 |
Inverse Reinforcement Learning |
逆强化学习 |
IRL |
[1] |
|
AITD-00907 |
Inverse Resolution |
逆归结 |
|
[1] |
|
AITD-00908 |
Inverse Time Decay |
逆时衰减 |
|
[1] |
|
AITD-00909 |
Invert |
求逆 |
|
[1] |
|
AITD-00910 |
Irreducible |
不可约的 |
|
[1] |
|
AITD-00911 |
Irrelevant Feature |
无关特征 |
|
[1] |
|
AITD-00912 |
Isometric Mapping |
等度量映射 |
Isomap |
[1] |
|
AITD-00913 |
Isotonic Regression |
等分回归 |
|
[1] |
|
AITD-00914 |
Isotropic |
各向同性 |
|
[1] |
|
AITD-00915 |
Isotropic Gaussian Distribution |
各向同性高斯分布 |
|
[1] |
|
AITD-00916 |
Iteration |
迭代 |
|
[1][2] |
数学、机器学习 |
AITD-00917 |
Iterative Dichotomiser |
迭代二分器 |
|
[1] |
|
AITD-00918 |
Jacobian |
雅克比 |
|
[1] |
|
AITD-00919 |
Jacobian Matrix |
雅可比矩阵 |
|
[1] |
|
AITD-00920 |
Jensen Inequality |
Jensen不等式 |
|
[1] |
|
AITD-00921 |
Jensen-Shannon Divergence |
JS散度 |
JSD |
[1] |
|
AITD-00922 |
Joint Probability Density Function |
联合概率密度函数 |
|
[1] |
|
AITD-00923 |
Joint Probability Distribution |
联合概率分布 |
|
[1] |
|
AITD-00924 |
Junction Tree Algorithm |
联合树算法 |
|
[1] |
|
AITD-00925 |
K-Armed Bandit Problem |
k-摇臂老虎机 |
|
[1] |
|
AITD-00926 |
K-Fold Cross Validation |
k 折交叉验证 |
K-FOLD CV |
[1] |
统计 |
AITD-00927 |
K-Means Clustering |
k-均值聚类 |
|
[1][2] |
|
AITD-00928 |
K-Nearest Neighbor Classifier |
k-近邻分类器 |
|
[1] |
|
AITD-00929 |
K-Nearest Neighbor Method |
k-近邻 |
K-NN |
[1] |
统计 |
AITD-00930 |
Karush-Kuhn-Tucker Condition |
KKT条件 |
|
[1] |
|
AITD-00931 |
Karush–Kuhn–Tucker |
Karush–Kuhn–Tucker |
|
[1] |
|
AITD-00932 |
Kd Tree |
Kd 树 |
|
[1] |
|
AITD-00933 |
Kernel Density Estimation |
核密度估计 |
|
[1] |
|
AITD-00934 |
Kernel Function |
核函数 |
|
[1] |
|
AITD-00935 |
Kernel Machine |
核机器 |
|
[1] |
|
AITD-00936 |
Kernel Matrix |
核矩阵 |
|
[1] |
|
AITD-00937 |
Kernel Method |
核方法 |
|
[1] |
机器学习 |
AITD-00938 |
Kernel Regression |
核回归 |
|
[1] |
|
AITD-00939 |
Kernel Trick |
核技巧 |
|
[1] |
|
AITD-00940 |
Kernelized |
核化 |
|
[1] |
|
AITD-00941 |
Kernelized Linear Discriminant Analysis |
核线性判别分析 |
KLDA |
[1] |
|
AITD-00942 |
Kernelized PCA |
核主成分分析 |
KPCA |
[1] |
|
AITD-00943 |
Key-Value Store |
键-值数据库 |
|
[1] |
|
AITD-00944 |
KL Divergence |
KL散度 |
|
[1] |
|
AITD-00945 |
Knowledge |
知识 |
|
[1] |
|
AITD-00946 |
Knowledge Base |
知识库 |
|
[1] |
|
AITD-00947 |
Knowledge Distillation |
知识蒸馏 |
|
[1] |
|
AITD-00948 |
Knowledge Engineering |
知识工程 |
|
[1] |
|
AITD-00949 |
Knowledge Graph |
知识图谱 |
|
[1][2][3] |
|
AITD-00950 |
Knowledge Representation |
知识表征 |
|
[1] |
|
AITD-00951 |
Kronecker Product |
Kronecker积 |
|
[1] |
|
AITD-00952 |
Krylov Method |
Krylov方法 |
|
[1] |
|
AITD-00953 |
L-BFGS |
L-BFGS |
|
[1] |
|
AITD-00954 |
Label |
标签/标记 |
|
[1] |
|
AITD-00955 |
Label Propagation |
标记传播 |
|
[1] |
|
AITD-00956 |
Label Smoothing |
标签平滑 |
|
[1] |
|
AITD-00957 |
Label Space |
标记空间 |
|
[1] |
|
AITD-00958 |
Labeled |
标注 |
|
[1] |
|
AITD-00959 |
Lagrange Dual Problem |
拉格朗日对偶问题 |
|
[1] |
|
AITD-00960 |
Lagrange Duality |
拉格朗日对偶性 |
|
[1] |
|
AITD-00961 |
Lagrange Function |
拉格朗日函数 |
|
[1] |
|
AITD-00962 |
Lagrange Multiplier |
拉格朗日乘子 |
|
[1] |
|
AITD-00963 |
Language Model |
语言模型 |
|
[1] |
|
AITD-00964 |
Language Modeling |
语言模型化 |
|
[1] |
|
AITD-00965 |
Laplace Distribution |
Laplace分布 |
|
[1] |
|
AITD-00966 |
Laplace Smoothing |
拉普拉斯平滑 |
|
[1] |
|
AITD-00967 |
Laplacian Correction |
拉普拉斯修正 |
|
[1] |
|
AITD-00968 |
Large Learning Step |
大学习步骤 |
|
[1] |
|
AITD-00969 |
Las Vegas Method |
拉斯维加斯方法 |
|
[1] |
|
AITD-00970 |
Latent |
潜在 |
|
[1] |
|
AITD-00971 |
Latent Dirichlet Allocation |
潜在狄利克雷分配 |
LDA |
[1] |
|
AITD-00972 |
Latent Layer |
潜层 |
|
[1] |
|
AITD-00973 |
Latent Semantic Analysis |
潜在语义分析 |
LSA |
[1] |
|
AITD-00974 |
Latent Semantic Indexing |
潜在语义索引 |
LSI |
[1] |
|
AITD-00975 |
Latent Variable |
潜变量/隐变量 |
|
[1] |
|
AITD-00976 |
Law of Large Numbers |
大数定律 |
|
[1] |
|
AITD-00977 |
Layer |
层 |
|
[1] |
|
AITD-00978 |
Layer Normalization |
层规范化 |
|
[1] |
|
AITD-00979 |
Layer-Wise |
逐层的 |
|
[1] |
|
AITD-00980 |
Layer-Wise Adaptive Rate Scaling |
逐层适应率缩放 |
LARS |
[1] |
|
AITD-00981 |
Layer-Wise Normalization |
逐层规范化 |
|
[1] |
|
AITD-00982 |
Layer-Wise Pretraining |
逐层预训练 |
|
[1] |
|
AITD-00983 |
Layer-Wise Training |
逐层训练 |
|
[1] |
|
AITD-00984 |
Lazy Learning |
懒惰学习 |
|
[1] |
|
AITD-00985 |
Leaf Node |
叶结点 |
|
[1] |
|
AITD-00986 |
Leaky Lelu Function |
泄漏线性整流函数 |
|
[1] |
|
AITD-00987 |
Leaky Relu |
泄漏修正线性单元/泄漏整流线性单元 |
|
[1] |
|
AITD-00988 |
Leaky Unit |
渗漏单元 |
|
[1] |
|
AITD-00989 |
Learned |
学成 |
|
[1] |
|
AITD-00990 |
Learned Approximate Inference |
学习近似推断 |
|
[1] |
|
AITD-00991 |
Learner |
学习器 |
|
[1] |
|
AITD-00992 |
Learning |
学习 |
|
[1] |
|
AITD-00993 |
Learning Algorithm |
学习算法 |
|
[1] |
|
AITD-00994 |
Learning By Analogy |
类比学习 |
|
[1] |
|
AITD-00995 |
Learning Rate |
学习率 |
|
[1] |
|
AITD-00996 |
Learning Rate Annealing |
学习率退火 |
|
[1] |
|
AITD-00997 |
Learning Rate Decay |
学习率衰减 |
|
[1] |
|
AITD-00998 |
Learning Rate Warmup |
学习率预热 |
|
[1] |
|
AITD-00999 |
Learning To Learn |
学习的学习 |
|
[1] |
|
AITD-01000 |
Learning Vector Quantization |
学习向量量化 |
LVQ |
[1] |
|
AITD-01001 |
Least General Generalization |
最小一般泛化 |
|
[1] |
|
AITD-01002 |
Least Mean Squares |
最小均方 |
LMS |
[1] |
|
AITD-01003 |
Least Square Method |
最小二乘法 |
LSM |
[1] |
|
AITD-01004 |
Least Squares Regression Tree |
最小二乘回归树 |
|
[1] |
|
AITD-01005 |
Leave-One-Out Cross Validation |
留一交叉验证 |
|
[1] |
|
AITD-01006 |
Leave-One-Out |
留一法 |
LOO |
[1] |
|
AITD-01007 |
Lebesgue-Integrable |
勒贝格可积 |
|
[1] |
|
AITD-01008 |
Left Eigenvector |
左特征向量 |
|
[1] |
|
AITD-01009 |
Left Singular Vector |
左奇异向量 |
|
[1] |
|
AITD-01010 |
Leibniz's Rule |
莱布尼兹法则 |
|
[1] |
|
AITD-01011 |
Lifelong Learning |
终身学习 |
|
[1] |
|
AITD-01012 |
Likelihood |
似然 |
|
[1] |
|
AITD-01013 |
Line Search |
线搜索 |
|
[1] |
|
AITD-01014 |
Linear Auto-Regressive Network |
线性自回归网络 |
|
[1] |
|
AITD-01015 |
Linear Chain |
线性链 |
|
[1] |
|
AITD-01016 |
Linear Chain Conditional Random Field |
线性链条件随机场 |
|
[1] |
|
AITD-01017 |
Linear Classification Model |
线性分类模型 |
|
[1] |
|
AITD-01018 |
Linear Classifier |
线性分类器 |
|
[1] |
|
AITD-01019 |
Linear Combination |
线性组合 |
|
[1] |
数学 |
AITD-01020 |
Linear Dependence |
线性相关 |
|
[1] |
|
AITD-01021 |
Linear Discriminant Analysis |
线性判别分析 |
LDA |
[1] |
统计、机器学习 |
AITD-01022 |
Linear Factor Model |
线性因子模型 |
|
[1] |
|
AITD-01023 |
Linear Mapping |
线性映射 |
|
[1] |
|
AITD-01024 |
Linear Model |
线性模型 |
LR |
[1][2] |
统计、机器学习 |
AITD-01025 |
Linear Programming |
线性规划 |
|
[1] |
|
AITD-01026 |
Linear Regression |
线性回归 |
|
[1][2][3] |
统计、数学 |
AITD-01027 |
Linear Scaling Rule |
线性缩放规则 |
|
[1] |
|
AITD-01028 |
Linear Scan |
线性扫描 |
|
[1] |
|
AITD-01029 |
Linear Space |
线性空间 |
|
[1] |
|
AITD-01030 |
Linear Support Vector Machine |
线性支持向量机 |
|
[1] |
|
AITD-01031 |
Linear Support Vector Machine In Linearly Separable Case |
线性可分支持向量机 |
|
[1] |
|
AITD-01032 |
Linear Threshold Units |
线性阈值单元 |
|
[1] |
|
AITD-01033 |
Linear Transformation |
线性变换 |
|
[1] |
|
AITD-01034 |
Linearly Independent |
线性无关 |
|
[1] |
|
AITD-01035 |
Linearly Separable |
线性可分 |
|
[1] |
|
AITD-01036 |
Linearly Separable Data Set |
线性可分数据集 |
|
[1] |
|
AITD-01037 |
Link Analysis |
链接分析 |
|
[1] |
|
AITD-01038 |
Link Function |
联系函数 |
|
[1] |
|
AITD-01039 |
Link Prediction |
链接预测 |
|
[1] |
|
AITD-01040 |
Link Table |
连接表 |
|
[1] |
|
AITD-01041 |
Linkage |
连接 |
|
[1] |
|
AITD-01042 |
Linked Importance Sampling |
链接重要采样 |
|
[1] |
|
AITD-01043 |
Lipschitz |
Lipschitz |
|
[1] |
|
AITD-01044 |
Lipschitz Constant |
Lipschitz常数 |
|
[1] |
|
AITD-01045 |
Lipschitz Continuous |
Lipschitz连续 |
|
[1] |
|
AITD-01046 |
Liquid State Machine |
流体状态机 |
|
[1] |
|
AITD-01047 |
Local Conditional Probability Distribution |
局部条件概率分布 |
|
[1] |
|
AITD-01048 |
Local Constancy Prior |
局部不变性先验 |
|
[1] |
|
AITD-01049 |
Local Contrast Normalization |
局部对比度规范化 |
|
[1] |
|
AITD-01050 |
Local Curvature |
局部曲率 |
|
[1] |
|
AITD-01051 |
Local Descent |
局部下降 |
|
[1] |
|
AITD-01052 |
Local Invariances |
局部不变性 |
|
[1] |
|
AITD-01053 |
Local Kernel |
局部核 |
|
[1] |
|
AITD-01054 |
Local Markov Property |
局部马尔可夫性 |
|
[1] |
|
AITD-01055 |
Local Maxima |
局部极大值 |
|
[1] |
|
AITD-01056 |
Local Maximum |
局部极大点 |
|
[1] |
|
AITD-01057 |
Local Minima |
局部极小 |
|
[1] |
|
AITD-01058 |
Local Minimizer |
局部最小解 |
|
[1] |
|
AITD-01059 |
Local Minimum |
局部极小 |
|
[1] |
|
AITD-01060 |
Local Representation |
局部式表示/局部式表征 |
|
[1] |
|
AITD-01061 |
Local Response Normalization |
局部响应规范化 |
LRN |
[1] |
|
AITD-01062 |
Locally Linear Embedding |
局部线性嵌入 |
LLE |
[1] |
|
AITD-01063 |
Log Likelihood |
对数似然函数 |
|
[1] |
|
AITD-01064 |
Log Linear Model |
对数线性模型 |
|
[1] |
|
AITD-01065 |
Log-Likelihood |
对数似然 |
|
[1] |
|
AITD-01066 |
Log-Likelihood Loss Function |
对数似然损失函数 |
|
[1] |
|
AITD-01067 |
Log-Linear Regression |
对数线性回归 |
|
[1] |
|
AITD-01068 |
Logarithmic Loss Function |
对数损失函数 |
|
[1] |
|
AITD-01069 |
Logarithmic Scale |
对数尺度 |
|
[1] |
|
AITD-01070 |
Logistic Distribution |
对数几率分布 |
|
[1] |
|
AITD-01071 |
Logistic Function |
对数几率函数 |
|
[1] |
|
AITD-01072 |
Logistic Loss |
对率损失 |
|
[1] |
|
AITD-01073 |
Logistic Regression |
对数几率回归 |
LR |
[1][2] |
统计、机器学习 |
AITD-01074 |
Logistic Sigmoid |
对数几率Sigmoid |
|
[1] |
|
AITD-01075 |
Logit |
对数几率 |
|
[1] |
|
AITD-01076 |
Long Short Term Memory |
长短期记忆 |
LSTM |
[1][2][3][4] |
|
AITD-01077 |
Long Short-Term Memory Network |
长短期记忆网络 |
LSTM |
[1] |
|
AITD-01078 |
Long-Term Dependencies Problem |
长程依赖问题 |
|
[1] |
|
AITD-01079 |
Long-Term Dependency |
长期依赖 |
|
[1] |
|
AITD-01080 |
Long-Term Memory |
长期记忆 |
|
[1] |
|
AITD-01081 |
Loop |
环 |
|
[1] |
|
AITD-01082 |
Loopy Belief Propagation |
环状信念传播 |
LBP |
[1] |
|
AITD-01083 |
Loss |
损失 |
|
[1] |
|
AITD-01084 |
Loss Function |
损失函数 |
|
[1][2] |
机器学习 |
AITD-01085 |
Low Rank Matrix Approximation |
低秩矩阵近似 |
|
[1] |
|
AITD-01086 |
Lp Distance |
Lp距离 |
|
[1] |
|
AITD-01087 |
Machine Learning Model |
机器学习模型 |
|
[1] |
|
AITD-01088 |
Machine Learning |
机器学习 |
ML |
[1] |
机器学习 |
AITD-01089 |
Machine Translation |
机器翻译 |
MT |
[1] |
|
AITD-01090 |
Macro Average |
宏平均 |
|
[1] |
|
AITD-01091 |
Macro-F1 |
宏F1 |
|
[1] |
|
AITD-01092 |
Macro-P |
宏查准率 |
|
[1] |
|
AITD-01093 |
Macron-R |
宏查全率 |
|
[1] |
|
AITD-01094 |
Mahalanobis Distance |
马哈拉诺比斯距离 |
|
[1] |
|
AITD-01095 |
Main Diagonal |
主对角线 |
|
[1] |
|
AITD-01096 |
Majority Voting |
绝对多数投票 |
|
[1] |
|
AITD-01097 |
Majority Voting Rule |
多数表决规则 |
|
[1] |
|
AITD-01098 |
Manhattan Distance |
曼哈顿距离 |
|
[1] |
|
AITD-01099 |
Manifold |
流形 |
|
[1] |
|
AITD-01100 |
Manifold Assumption |
流形假设 |
|
[1] |
|
AITD-01101 |
Manifold Learning |
流形学习 |
|
[1] |
|
AITD-01102 |
Manifold Tangent Classifier |
流形正切分类器 |
|
[1] |
|
AITD-01103 |
Margin |
间隔 |
|
[1] |
统计 |
AITD-01104 |
Margin Theory |
间隔理论 |
|
[1] |
|
AITD-01105 |
Marginal Distribution |
边缘分布 |
|
[1] |
|
AITD-01106 |
Marginal Independence |
边缘独立性 |
|
[1] |
|
AITD-01107 |
Marginal Likelihood |
边缘似然函数 |
|
[1] |
|
AITD-01108 |
Marginal Probability Distribution |
边缘概率分布 |
|
[1] |
|
AITD-01109 |
Marginalization |
边缘化 |
|
[1] |
|
AITD-01110 |
Markov Blanket |
马尔可夫毯 |
|
[1] |
|
AITD-01111 |
Markov Chain |
马尔可夫链 |
|
[1] |
|
AITD-01112 |
Markov Chain Monte Carlo |
马尔可夫链蒙特卡罗 |
MCMC |
[1] |
|
AITD-01113 |
Markov Decision Process |
马尔可夫决策过程 |
MDP |
[1] |
|
AITD-01114 |
Markov Network |
马尔可夫网络 |
|
[1] |
|
AITD-01115 |
Markov Process |
马尔可夫过程 |
|
[1] |
|
AITD-01116 |
Markov Property |
马尔可夫性质 |
|
[1] |
|
AITD-01117 |
Markov Random Field |
马尔可夫随机场 |
MRF |
[1] |
|
AITD-01118 |
Mask |
掩码 |
|
[1] |
|
AITD-01119 |
Mask Language Modeling |
掩码语言模型化 |
|
[1] |
|
AITD-01120 |
Masked Self-Attention |
掩蔽自注意力 |
|
[1] |
|
AITD-01121 |
Mathematical Optimization |
数学优化 |
|
[1] |
|
AITD-01122 |
Matrix |
矩阵 |
|
[1] |
|
AITD-01123 |
Matrix Calculus |
矩阵微积分 |
|
[1] |
|
AITD-01124 |
Matrix Completion |
矩阵补全 |
|
[1] |
|
AITD-01125 |
Matrix Decomposition |
矩阵分解 |
|
[1] |
|
AITD-01126 |
Matrix Inversion |
逆矩阵 |
|
[1] |
|
AITD-01127 |
Matrix Product |
矩阵乘积 |
|
[1] |
|
AITD-01128 |
Max Norm |
最大范数 |
|
[1] |
|
AITD-01129 |
Max Pooling |
最大汇聚 |
|
[1] |
|
AITD-01130 |
Maxima |
极大值 |
|
[1] |
|
AITD-01131 |
Maximal Clique |
最大团 |
|
[1] |
|
AITD-01132 |
Maximization |
极大 |
|
[1] |
|
AITD-01133 |
Maximization Step |
M步 |
|
[1] |
|
AITD-01134 |
Maximization-Maximization Algorithm |
极大-极大算法 |
|
[1] |
|
AITD-01135 |
Maximum A Posteriori |
最大后验 |
|
[1] |
|
AITD-01136 |
Maximum A Posteriori Estimation |
最大后验估计 |
MAP |
[1] |
|
AITD-01137 |
Maximum Entropy Model |
最大熵模型 |
|
[1] |
|
AITD-01138 |
Maximum Likelihood |
极大似然 |
|
[1] |
|
AITD-01139 |
Maximum Likelihood Estimation |
极大似然估计 |
MLE |
[1] |
|
AITD-01140 |
Maximum Likelihood Method |
极大似然法 |
|
[1] |
|
AITD-01141 |
Maximum Margin |
最大间隔 |
|
[1] |
|
AITD-01142 |
Maximum Mean Discrepancy |
最大平均偏差 |
|
[1] |
|
AITD-01143 |
Maximum Posterior Probability Estimation |
最大后验概率估计 |
MAP |
[1] |
|
AITD-01144 |
Maximum Weighted Spanning Tree |
最大带权生成树 |
|
[1] |
|
AITD-01145 |
Maxout |
Maxout |
|
[1] |
|
AITD-01146 |
Maxout Unit |
Maxout单元 |
|
[1] |
|
AITD-01147 |
Mean |
均值 |
|
[1] |
|
AITD-01148 |
Mean Absolute Error |
平均绝对误差 |
|
[1] |
|
AITD-01149 |
Mean And Covariance RBM |
均值和协方差RBM |
|
[1] |
|
AITD-01150 |
Mean Filed |
平均场 |
|
[1] |
|
AITD-01151 |
Mean Filter |
均值滤波 |
|
[1] |
|
AITD-01152 |
Mean Pooling |
平均汇聚 |
|
[1] |
|
AITD-01153 |
Mean Product of Student t-Distribution |
学生 t 分布均值乘积 |
|
[1] |
|
AITD-01154 |
Mean Squared Error |
均方误差 |
|
[1] |
|
AITD-01155 |
Mean-Covariance Restricted Boltzmann Machine |
均值-协方差受限玻尔兹曼机 |
|
[1] |
|
AITD-01156 |
Mean-Field |
平均场 |
|
[1] |
|
AITD-01157 |
Meanfield |
均匀场 |
|
[1] |
|
AITD-01158 |
Measure Theory |
测度论 |
|
[1] |
|
AITD-01159 |
Measure Zero |
零测度 |
|
[1] |
|
AITD-01160 |
Median |
中位数 |
|
[1] |
|
AITD-01161 |
Memory |
记忆 |
|
[1] |
|
AITD-01162 |
Memory Augmented Neural Network |
记忆增强神经网络 |
MANN |
[1] |
|
AITD-01163 |
Memory Capacity |
记忆容量 |
|
[1] |
|
AITD-01164 |
Memory Cell |
记忆元 |
|
[1] |
|
AITD-01165 |
Memory Network |
记忆网络 |
MN |
[1] |
|
AITD-01166 |
Memory Segment |
记忆片段 |
|
[1] |
|
AITD-01167 |
Mercer Kernel |
Mercer 核 |
|
[1] |
|
AITD-01168 |
Message |
消息 |
|
[1] |
|
AITD-01169 |
Message Passing |
消息传递 |
|
[1] |
|
AITD-01170 |
Message Passing Neural Network |
消息传递神经网络 |
MPNN |
[1] |
|
AITD-01171 |
Meta-Learner |
元学习器 |
|
[1] |
|
AITD-01172 |
Meta-Learning |
元学习 |
|
[1] |
|
AITD-01173 |
Meta-Optimization |
元优化 |
|
[1] |
|
AITD-01174 |
Meta-Rule |
元规则 |
|
[1] |
|
AITD-01175 |
Metric |
指标 |
|
[1][2] |
|
AITD-01176 |
Metric Learning |
度量学习 |
|
[1] |
|
AITD-01177 |
Micro Average |
微平均 |
|
[1] |
|
AITD-01178 |
Micro-F1 |
微F1 |
|
[1] |
|
AITD-01179 |
Micro-P |
微査准率 |
|
[1] |
|
AITD-01180 |
Micro-R |
微查全率 |
|
[1] |
|
AITD-01181 |
Min-Max Normalization |
最小最大值规范化 |
|
[1] |
|
AITD-01182 |
Mini-Batch Gradient |
小批量梯度 |
|
[1] |
|
AITD-01183 |
Mini-Batch Gradient Descent |
小批量梯度下降法 |
|
[1] |
|
AITD-01184 |
Mini-Batch SGD |
小批次随机梯度下降 |
|
[1] |
|
AITD-01185 |
Minibatch |
小批量 |
|
[1] |
|
AITD-01186 |
Minibatch Stochastic |
小批量随机 |
|
[1] |
|
AITD-01187 |
Minima |
极小值 |
|
[1] |
|
AITD-01188 |
Minimal Description Length |
最小描述长度 |
MDL |
[1] |
|
AITD-01189 |
Minimax Game |
极小极大博弈 |
|
[1] |
|
AITD-01190 |
Minimum |
极小点 |
|
[1] |
|
AITD-01191 |
Minkowski Distance |
闵可夫斯基距离 |
|
[1] |
|
AITD-01192 |
Misclassification Cost |
误分类代价 |
|
[1] |
|
AITD-01193 |
Mixing |
混合 |
|
[1] |
|
AITD-01194 |
Mixing Time |
混合时间 |
|
[1] |
|
AITD-01195 |
Mixture Density Network |
混合密度网络 |
|
[1] |
|
AITD-01196 |
Mixture Distribution |
混合分布 |
|
[1] |
|
AITD-01197 |
Mixture of Experts |
混合专家模型 |
|
[1] |
|
AITD-01198 |
Mixture-of-Gaussian |
高斯混合 |
|
[1] |
|
AITD-01199 |
Modality |
模态 |
|
[1] |
|
AITD-01200 |
Mode |
峰值 |
|
[1] |
|
AITD-01201 |
Model |
模型 |
|
[1] |
|
AITD-01202 |
Model Averaging |
模型平均 |
|
[1] |
|
AITD-01203 |
Model Collapse |
模型坍塌 |
|
[1] |
|
AITD-01204 |
Model Complexity |
模型复杂度 |
|
[1] |
|
AITD-01205 |
Model Compression |
模型压缩 |
|
[1] |
|
AITD-01206 |
Model Identifiability |
模型可辨识性 |
|
[1] |
|
AITD-01207 |
Model Parallelism |
模型并行 |
|
[1] |
|
AITD-01208 |
Model Parameter |
模型参数 |
|
[1] |
|
AITD-01209 |
Model Predictive Control |
模型预测控制 |
MPC |
[1] |
|
AITD-01210 |
Model Selection |
模型选择 |
|
[1] |
|
AITD-01211 |
Model-Agnostic Meta-Learning |
模型无关的元学习 |
MAML |
[1] |
|
AITD-01212 |
Model-Based Learning |
有模型学习 |
|
[1] |
|
AITD-01213 |
Model-Based Reinforcement Learning |
基于模型的强化学习 |
|
[1] |
|
AITD-01214 |
Model-Free Learning |
免模型学习 |
|
[1] |
|
AITD-01215 |
Model-Free Reinforcement Learning |
模型无关的强化学习 |
|
[1] |
|
AITD-01216 |
Moment |
矩 |
|
[1] |
|
AITD-01217 |
Moment Matching |
矩匹配 |
|
[1] |
|
AITD-01218 |
Momentum |
动量 |
|
[1] |
|
AITD-01219 |
Momentum Method |
动量法 |
|
[1] |
|
AITD-01220 |
Monte Carlo |
蒙特卡罗 |
|
[1] |
|
AITD-01221 |
Monte Carlo Estimate |
蒙特卡罗估计 |
|
[1] |
|
AITD-01222 |
Monte Carlo Integration |
蒙特卡罗积分 |
|
[1] |
|
AITD-01223 |
Monte Carlo Method |
蒙特卡罗方法 |
|
[1] |
|
AITD-01224 |
Moore's Law |
摩尔定律 |
|
[1] |
|
AITD-01225 |
Moore-Penrose Pseudoinverse |
Moore-Penrose 伪逆 |
|
[1] |
|
AITD-01226 |
Moral Graph |
端正图/道德图 |
|
[1] |
|
AITD-01227 |
Moralization |
道德化 |
|
[1] |
|
AITD-01228 |
Most General Unifier |
最一般合一置换 |
|
[1] |
|
AITD-01229 |
Moving Average |
移动平均 |
MA |
[1] |
|
AITD-01230 |
Multi-Armed Bandit Problem |
多臂赌博机问题 |
|
[1] |
|
AITD-01231 |
Multi-Class Classification |
多分类 |
|
[1] |
|
AITD-01232 |
Multi-Classifier System |
多分类器系统 |
|
[1] |
|
AITD-01233 |
Multi-Document Summarization |
多文档摘要 |
|
[1] |
|
AITD-01234 |
Multi-Head Attention |
多头注意力 |
|
[1] |
|
AITD-01235 |
Multi-Head Self-Attention |
多头自注意力 |
|
[1] |
|
AITD-01236 |
Multi-Hop |
多跳 |
|
[1] |
|
AITD-01237 |
Multi-Kernel Learning |
多核学习 |
|
[1] |
|
AITD-01238 |
Multi-Label Classification |
多标签分类 |
|
[1] |
|
AITD-01239 |
Multi-Label Learning |
多标记学习 |
|
[1] |
|
AITD-01240 |
Multi-Layer Feedforward Neural Networks |
多层前馈神经网络 |
|
[1] |
|
AITD-01241 |
Multi-Layer Perceptron |
多层感知机 |
MLP |
[1][2][3] |
|
AITD-01242 |
Multi-Nominal Logistic Regression Model |
多项对数几率回归模型 |
|
[1] |
|
AITD-01243 |
Multi-Prediction Deep Boltzmann Machine |
多预测深度玻尔兹曼机 |
|
[1] |
|
AITD-01244 |
Multi-Response Linear Regression |
多响应线性回归 |
MLR |
[1] |
|
AITD-01245 |
Multi-View Learning |
多视图学习 |
|
[1] |
|
AITD-01246 |
Multicollinearity |
多重共线性 |
|
[1] |
|
AITD-01247 |
Multimodal |
多峰值 |
|
[1] |
|
AITD-01248 |
Multimodal Learning |
多模态学习 |
|
[1] |
|
AITD-01249 |
Multinomial Distribution |
多项分布 |
|
[1] |
|
AITD-01250 |
Multinoulli Distribution |
Multinoulli分布 |
|
[1] |
|
AITD-01251 |
Multinoulli Output Distribution |
Multinoulli输出分布 |
|
[1] |
|
AITD-01252 |
Multiple Dimensional Scaling |
多维缩放 |
|
[1] |
|
AITD-01253 |
Multiple Linear Regression |
多元线性回归 |
MLR |
[1][2][3] |
统计 |
AITD-01254 |
Multitask Learning |
多任务学习 |
|
[1] |
|
AITD-01255 |
Multivariate Decision Tree |
多变量决策树 |
|
[1] |
|
AITD-01256 |
Multivariate Gaussian Distribution |
多元高斯分布 |
|
[1] |
|
AITD-01257 |
Multivariate Normal Distribution |
多元正态分布 |
|
[1] |
|
AITD-01258 |
Mutual Information |
互信息 |
|
[1] |
|
AITD-01259 |
N-Gram |
N元 |
|
[1] |
|
AITD-01260 |
N-Gram Feature |
N元特征 |
|
[1] |
|
AITD-01261 |
N-Gram Model |
N元模型 |
|
[1] |
|
AITD-01262 |
Naive Bayes Algorithm |
朴素贝叶斯算法 |
|
[1] |
|
AITD-01263 |
Naive Bayes Classifier |
朴素贝叶斯分类器 |
|
[1] |
|
AITD-01264 |
Naive Bayes |
朴素贝叶斯 |
NB |
[1] |
|
AITD-01265 |
Named Entity Recognition |
命名实体识别 |
|
[1] |
|
AITD-01266 |
Narrow Convolution |
窄卷积 |
|
[1] |
|
AITD-01267 |
Nash Equilibrium |
纳什均衡 |
|
[1] |
|
AITD-01268 |
Nash Reversion |
纳什回归 |
|
[1] |
|
AITD-01269 |
Nats |
奈特 |
|
[1] |
|
AITD-01270 |
Natural Exponential Decay |
自然指数衰减 |
|
[1] |
|
AITD-01271 |
Natural Language Generation |
自然语言生成 |
NLG |
[1] |
|
AITD-01272 |
Natural Language Processing |
自然语言处理 |
NLP |
[1][2][3][4][5][6][7] |
机器学习 |
AITD-01273 |
Nearest Neighbor |
最近邻 |
|
[1] |
|
AITD-01274 |
Nearest Neighbor Classifier |
最近邻分类器 |
|
[1] |
|
AITD-01275 |
Nearest Neighbor Graph |
最近邻图 |
|
[1] |
|
AITD-01276 |
Nearest Neighbor Regression |
最近邻回归 |
|
[1] |
|
AITD-01277 |
Nearest-Neighbor Search |
最近邻搜索 |
|
[1] |
|
AITD-01278 |
Negative Class |
负类 |
|
[1] |
|
AITD-01279 |
Negative Correlation |
负相关法 |
|
[1] |
|
AITD-01280 |
Negative Definite |
负定 |
|
[1] |
|
AITD-01281 |
Negative Log Likelihood |
负对数似然函数 |
|
[1] |
|
AITD-01282 |
Negative Part Function |
负部函数 |
|
[1] |
|
AITD-01283 |
Negative Phase |
负相 |
|
[1] |
|
AITD-01284 |
Negative Sample |
负例 |
|
[1] |
|
AITD-01285 |
Negative Sampling |
负采样 |
|
[1] |
|
AITD-01286 |
Negative Semidefinite |
半负定 |
|
[1] |
|
AITD-01287 |
Neighbourhood Component Analysis |
近邻成分分析 |
NCA |
[1] |
|
AITD-01288 |
Nesterov Accelerated Gradient |
Nesterov加速梯度 |
NAG |
[1] |
|
AITD-01289 |
Nesterov Momentum |
Nesterov动量法 |
|
[1] |
|
AITD-01290 |
Net Activation |
净活性值 |
|
[1] |
|
AITD-01291 |
Net Input |
净输入 |
|
[1] |
|
AITD-01292 |
Network |
网络 |
|
[1] |
|
AITD-01293 |
Network Capacity |
网络容量 |
|
[1] |
|
AITD-01294 |
Neural Architecture Search |
神经架构搜索 |
NAS |
[1] |
|
AITD-01295 |
Neural Auto-Regressive Density Estimator |
神经自回归密度估计器 |
|
[1] |
|
AITD-01296 |
Neural Auto-Regressive Network |
神经自回归网络 |
|
[1] |
|
AITD-01297 |
Neural Language Model |
神经语言模型 |
|
[1] |
|
AITD-01298 |
Neural Machine Translation |
神经机器翻译 |
|
[1] |
|
AITD-01299 |
Neural Model |
神经模型 |
|
[1] |
|
AITD-01300 |
Neural Network |
神经网络 |
NN |
[1] |
|
AITD-01301 |
Neural Turing Machine |
神经图灵机 |
NTM |
[1] |
|
AITD-01302 |
Neurodynamics |
神经动力学 |
|
[1] |
|
AITD-01303 |
Neuromorphic Computing |
神经形态计算 |
|
[1][2][3] |
|
AITD-01304 |
Neuron |
神经元 |
|
[1] |
|
AITD-01305 |
Newton Method |
牛顿法 |
|
[1] |
|
AITD-01306 |
No Free Lunch Theorem |
没有免费午餐定理 |
NFL |
[1] |
|
AITD-01307 |
Node |
结点 |
|
[1] |
|
AITD-01308 |
Noise |
噪声 |
|
[1] |
|
AITD-01309 |
Noise Distribution |
噪声分布 |
|
[1] |
|
AITD-01310 |
Noise-Contrastive Estimation |
噪声对比估计 |
NCE |
[1] |
|
AITD-01311 |
Nominal Attribute |
列名属性 |
|
[1] |
|
AITD-01312 |
Non-Autoregressive Process |
非自回归过程 |
|
[1] |
|
AITD-01313 |
Non-Convex Optimization |
非凸优化 |
|
[1] |
|
AITD-01314 |
Non-Informative Prior |
无信息先验 |
|
[1] |
|
AITD-01315 |
Non-Linear Model |
非线性模型 |
|
[1] |
|
AITD-01316 |
Non-Linear Oscillation |
非线性振荡 |
|
[1] |
|
AITD-01317 |
Non-Linear Support Vector Machine |
非线性支持向量机 |
|
[1] |
|
AITD-01318 |
Non-Metric Distance |
非度量距离 |
|
[1] |
|
AITD-01319 |
Non-Negative Matrix Factorization |
非负矩阵分解 |
NMF |
[1] |
|
AITD-01320 |
Non-Ordinal Attribute |
无序属性 |
|
[1] |
|
AITD-01321 |
Non-Parametric |
非参数 |
|
[1] |
|
AITD-01322 |
Non-Parametric Model |
非参数化模型 |
|
[1] |
|
AITD-01323 |
Non-Probabilistic Model |
非概率模型 |
|
[1] |
|
AITD-01324 |
Non-Saturating Game |
非饱和博弈 |
|
[1] |
|
AITD-01325 |
Non-Separable |
不可分 |
|
[1] |
|
AITD-01326 |
Nonconvex |
非凸 |
|
[1] |
|
AITD-01327 |
Nondistributed |
非分布式 |
|
[1] |
|
AITD-01328 |
Nondistributed Representation |
非分布式表示 |
|
[1] |
|
AITD-01329 |
Nonlinear Autoregressive With Exogenous Inputs Model |
有外部输入的非线性自回归模型 |
NARX |
[1] |
|
AITD-01330 |
Nonlinear Conjugate Gradients |
非线性共轭梯度 |
|
[1] |
|
AITD-01331 |
Nonlinear Independent Components Estimation |
非线性独立成分估计 |
|
[1] |
|
AITD-01332 |
Nonlinear Programming |
非线性规划 |
|
[1] |
|
AITD-01333 |
Nonparametric Density Estimation |
非参数密度估计 |
|
[1] |
|
AITD-01334 |
Norm |
范数 |
|
[1] |
|
AITD-01335 |
Norm-Preserving |
范数保持性 |
|
[1] |
|
AITD-01336 |
Normal Distribution |
正态分布 |
|
[1] |
|
AITD-01337 |
Normal Equation |
正规方程 |
|
[1] |
|
AITD-01338 |
Normalization |
规范化 |
|
[1] |
统计、机器学习 |
AITD-01339 |
Normalization Factor |
规范化因子 |
|
[1] |
|
AITD-01340 |
Normalized |
规范化的 |
|
[1] |
|
AITD-01341 |
Normalized Initialization |
标准初始化 |
|
[1] |
|
AITD-01342 |
Nuclear Norm |
核范数 |
|
[1] |
|
AITD-01343 |
Null Space |
零空间 |
|
[1] |
|
AITD-01344 |
Number of Epochs |
轮数 |
|
[1] |
|
AITD-01345 |
Numerator Layout |
分子布局 |
|
[1] |
|
AITD-01346 |
Numeric Value |
数值 |
|
[1] |
|
AITD-01347 |
Numerical Attribute |
数值属性 |
|
[1] |
|
AITD-01348 |
Numerical Differentiation |
数值微分 |
|
[1] |
|
AITD-01349 |
Numerical Method |
数值方法 |
|
[1] |
|
AITD-01350 |
Numerical Optimization |
数值优化 |
|
[1] |
|
AITD-01351 |
Object Detection |
目标检测 |
|
[1] |
|
AITD-01352 |
Object Recognition |
对象识别 |
|
[1] |
|
AITD-01353 |
Objective |
目标 |
|
[1] |
|
AITD-01354 |
Objective Function |
目标函数 |
|
[1] |
|
AITD-01355 |
Oblique Decision Tree |
斜决策树 |
|
[1] |
|
AITD-01356 |
Observable Variable |
观测变量 |
|
[1] |
|
AITD-01357 |
Observation Sequence |
观测序列 |
|
[1] |
|
AITD-01358 |
Occam's Razor |
奥卡姆剃刀 |
|
[1][2] |
机器学习 |
AITD-01359 |
Odds |
几率 |
|
[1] |
|
AITD-01360 |
Off-Policy |
异策略 |
|
[1] |
|
AITD-01361 |
Offline Inference |
离线推断 |
|
[1] |
|
AITD-01362 |
Offset |
偏移量 |
|
[1] |
|
AITD-01363 |
Offset Vector |
偏移向量 |
|
[1] |
|
AITD-01364 |
On-Policy |
同策略 |
|
[1] |
|
AITD-01365 |
One-Shot Learning |
单试学习 |
|
[1][2] |
|
AITD-01366 |
One-Dependent Estimator |
独依赖估计 |
ODE |
[1] |
|
AITD-01367 |
One-Hot |
独热 |
|
[1] |
|
AITD-01368 |
Online |
在线 |
|
[1] |
|
AITD-01369 |
Online Inference |
在线推断 |
|
[1] |
|
AITD-01370 |
Online Learning |
在线学习 |
|
[1] |
|
AITD-01371 |
Operation |
操作 |
|
[1] |
|
AITD-01372 |
Operator |
运算符 |
|
[1] |
|
AITD-01373 |
Optimal Capacity |
最佳容量 |
|
[1] |
|
AITD-01374 |
Optimization |
最优化 |
|
[1] |
|
AITD-01375 |
Optimization Landscape |
优化地形 |
|
[1] |
|
AITD-01376 |
Optimizer |
优化器 |
|
[1] |
|
AITD-01377 |
Ordered Rule |
带序规则 |
|
[1] |
|
AITD-01378 |
Ordinal Attribute |
有序属性 |
|
[1] |
|
AITD-01379 |
Origin |
原点 |
|
[1] |
|
AITD-01380 |
Orthogonal |
正交 |
|
[1] |
数学 |
AITD-01381 |
Orthogonal Initialization |
正交初始化 |
|
[1] |
|
AITD-01382 |
Orthogonal Matrix |
正交矩阵 |
|
[1] |
|
AITD-01383 |
Orthonormal |
标准正交 |
|
[1] |
|
AITD-01384 |
Out-Of-Bag Estimate |
包外估计 |
|
[1] |
|
AITD-01385 |
Outer Product |
外积 |
|
[1] |
|
AITD-01386 |
Outlier |
异常点 |
|
[1][2] |
|
AITD-01387 |
Output |
输出 |
|
[1] |
|
AITD-01388 |
Output Gate |
输出门 |
|
[1] |
|
AITD-01389 |
Output Layer |
输出层 |
|
[1] |
机器学习 |
AITD-01390 |
Output Smearing |
输出调制法 |
|
[1] |
|
AITD-01391 |
Output Space |
输出空间 |
|
[1] |
|
AITD-01392 |
Over-Parameterized |
过度参数化 |
|
[1] |
|
AITD-01393 |
Overcomplete |
过完备 |
|
[1] |
|
AITD-01394 |
Overestimation |
过估计 |
|
[1] |
|
AITD-01395 |
Overfitting |
过拟合 |
|
[1][2][3] |
机器学习 |
AITD-01396 |
Overfitting Regime |
过拟合机制 |
|
[1] |
|
AITD-01397 |
Overflow |
上溢 |
|
[1] |
|
AITD-01398 |
Oversampling |
过采样 |
|
[1] |
|
AITD-01399 |
PAC Learning |
PAC学习 |
|
[1] |
|
AITD-01400 |
Pac-Learnable |
PAC可学习 |
|
[1] |
|
AITD-01401 |
Padding |
填充 |
|
[1] |
|
AITD-01402 |
Paired t -Test |
成对 t 检验 |
|
[1] |
|
AITD-01403 |
Pairwise |
成对型 |
|
[1] |
|
AITD-01404 |
Pairwise Markov Property |
成对马尔可夫性 |
|
[1] |
|
AITD-01405 |
Parallel Distributed Processing |
分布式并行处理 |
PDP |
[1] |
|
AITD-01406 |
Parallel Tempering |
并行回火 |
|
[1] |
|
AITD-01407 |
Parameter |
参数 |
|
[1] |
|
AITD-01408 |
Parameter Estimation |
参数估计 |
|
[1] |
|
AITD-01409 |
Parameter Server |
参数服务器 |
|
[1] |
|
AITD-01410 |
Parameter Sharing |
参数共享 |
|
[1] |
|
AITD-01411 |
Parameter Space |
参数空间 |
|
[1] |
|
AITD-01412 |
Parameter Tuning |
调参 |
|
[1][2] |
机器学习 |
AITD-01413 |
Parametric Case |
有参情况 |
|
[1] |
|
AITD-01414 |
Parametric Density Estimation |
参数密度估计 |
|
[1] |
|
AITD-01415 |
Parametric Model |
参数化模型 |
|
[1] |
|
AITD-01416 |
Parametric ReLU |
参数化修正线性单元/参数化整流线性单元 |
PReLU |
[1] |
|
AITD-01417 |
Parse Tree |
解析树 |
|
[1] |
|
AITD-01418 |
Part-Of-Speech Tagging |
词性标注 |
|
[1] |
|
AITD-01419 |
Partial Derivative |
偏导数 |
|
[1] |
|
AITD-01420 |
Partially Observable Markov Decision Processes |
部分可观测马尔可夫决策过程 |
POMDP |
[1] |
|
AITD-01421 |
Particle Swarm Optimization |
粒子群优化算法 |
PSO |
[1] |
|
AITD-01422 |
Partition |
划分 |
|
[1] |
|
AITD-01423 |
Partition Function |
配分函数 |
|
[1] |
|
AITD-01424 |
Path |
路径 |
|
[1] |
|
AITD-01425 |
Pattern |
模式 |
|
[1] |
|
AITD-01426 |
Pattern Recognition |
模式识别 |
PR |
[1][2] |
|
AITD-01427 |
Penalty Term |
罚项 |
|
[1] |
|
AITD-01428 |
Perceptron |
感知机 |
|
[1][2] |
机器学习 |
AITD-01429 |
Performance Measure |
性能度量 |
|
[1] |
|
AITD-01430 |
Periodic |
周期的 |
|
[1] |
|
AITD-01431 |
Permutation Invariant |
置换不变性 |
|
[1] |
|
AITD-01432 |
Perplexity |
困惑度 |
|
[1] |
|
AITD-01433 |
Persistent Contrastive Divergence |
持续性对比散度 |
|
[1] |
|
AITD-01434 |
Phoneme |
音素 |
|
[1] |
|
AITD-01435 |
Phonetic |
语音 |
|
[1] |
|
AITD-01436 |
Pictorial Structure |
图形结构 |
|
[1] |
|
AITD-01437 |
Piecewise |
分段 |
|
[1] |
|
AITD-01438 |
Piecewise Constant Decay |
分段常数衰减 |
|
[1] |
|
AITD-01439 |
Pipeline |
流水线 |
|
[1] |
|
AITD-01440 |
Plate Notation |
板块表示 |
|
[1] |
|
AITD-01441 |
Plug And Play Generative Network |
即插即用生成网络 |
|
[1] |
|
AITD-01442 |
Plurality Voting |
相对多数投票 |
|
[1] |
|
AITD-01443 |
Point Estimator |
点估计 |
|
[1] |
|
AITD-01444 |
Pointer Network |
指针网络 |
|
[1] |
|
AITD-01445 |
Polarity Detection |
极性检测 |
|
[1] |
|
AITD-01446 |
Policy |
策略 |
|
[1] |
|
AITD-01447 |
Policy Evaluation |
策略评估 |
|
[1] |
|
AITD-01448 |
Policy Gradient |
策略梯度 |
|
[1] |
|
AITD-01449 |
Policy Improvement |
策略改进 |
|
[1] |
|
AITD-01450 |
Policy Iteration |
策略迭代 |
|
[1] |
|
AITD-01451 |
Policy Search |
策略搜索 |
|
[1] |
|
AITD-01452 |
Polynomial Basis Function |
多项式基函数 |
|
[1] |
|
AITD-01453 |
Polynomial Kernel Function |
多项式核函数 |
|
[1] |
|
AITD-01454 |
Polysemy |
一词多义性 |
|
[1] |
|
AITD-01455 |
Pool |
汇聚 |
|
[1] |
|
AITD-01456 |
Pooling |
汇聚 |
|
[1] |
|
AITD-01457 |
Pooling Function |
汇聚函数 |
|
[1] |
|
AITD-01458 |
Pooling Layer |
汇聚层 |
|
[1] |
|
AITD-01459 |
Poor Conditioning |
病态条件 |
|
[1] |
|
AITD-01460 |
Position Embedding |
位置嵌入 |
|
[1] |
|
AITD-01461 |
Positional Encoding |
位置编码 |
|
[1] |
|
AITD-01462 |
Positive Class |
正类 |
|
[1] |
|
AITD-01463 |
Positive Definite |
正定 |
|
[1] |
|
AITD-01464 |
Positive Definite Kernel Function |
正定核函数 |
|
[1] |
|
AITD-01465 |
Positive Definite Matrix |
正定矩阵 |
|
[1] |
|
AITD-01466 |
Positive Part Function |
正部函数 |
|
[1] |
|
AITD-01467 |
Positive Phase |
正相 |
|
[1] |
|
AITD-01468 |
Positive Recurrent |
正常返的 |
|
[1] |
|
AITD-01469 |
Positive Sample |
正例 |
|
[1] |
|
AITD-01470 |
Positive Semidefinite |
半正定 |
|
[1] |
|
AITD-01471 |
Positive-Semidefinite Matrix |
半正定矩阵 |
|
[1] |
|
AITD-01472 |
Post-Hoc Test |
后续检验 |
|
[1] |
|
AITD-01473 |
Post-Pruning |
后剪枝 |
|
[1] |
|
AITD-01474 |
Posterior Distribution |
后验分布 |
|
[1] |
|
AITD-01475 |
Posterior Inference |
后验推断 |
|
[1] |
|
AITD-01476 |
Posterior Probability |
后验概率 |
|
[1] |
|
AITD-01477 |
Potential Function |
势函数 |
|
[1] |
|
AITD-01478 |
Power Method |
幂法 |
|
[1] |
|
AITD-01479 |
PR Curve |
P-R曲线 |
|
[1] |
|
AITD-01480 |
Pre-Trained Initialization |
预训练初始化 |
|
[1] |
|
AITD-01481 |
Pre-Training |
预训练 |
|
[1] |
|
AITD-01482 |
Precision |
查准率/准确率 |
|
[1] |
数学、HPC |
AITD-01483 |
Precision Matrix |
精度矩阵 |
|
[1] |
|
AITD-01484 |
Predictive Sparse Decomposition |
预测稀疏分解 |
|
[1] |
|
AITD-01485 |
Prepruning |
预剪枝 |
|
[1] |
|
AITD-01486 |
Pretrained Language Model |
预训练语言模型 |
|
[1] |
|
AITD-01487 |
Primal Problem |
主问题 |
|
[1] |
|
AITD-01488 |
Primary Visual Cortex |
初级视觉皮层 |
|
[1] |
|
AITD-01489 |
Principal Component Analysis |
主成分分析 |
PCA |
[1][2][3][4] |
|
AITD-01490 |
Principle Of Multiple Explanations |
多释原则 |
|
[1] |
|
AITD-01491 |
Prior |
先验 |
|
[1] |
|
AITD-01492 |
Prior Knowledge |
先验知识 |
|
[1] |
统计 |
AITD-01493 |
Prior Probability |
先验概率 |
|
[1] |
|
AITD-01494 |
Prior Probability Distribution |
先验概率分布 |
|
[1] |
|
AITD-01495 |
Prior Pseudo-Counts |
伪计数 |
|
[1] |
|
AITD-01496 |
Prior Shift |
先验偏移 |
|
[1] |
|
AITD-01497 |
Priority Rule |
优先级规则 |
|
[1] |
|
AITD-01498 |
Probabilistic Context-Free Grammar |
概率上下文无关文法 |
|
[1] |
|
AITD-01499 |
Probabilistic Density Estimation |
概率密度估计 |
|
[1] |
|
AITD-01500 |
Probabilistic Generative Model |
概率生成模型 |
|
[1] |
|
AITD-01501 |
Probabilistic Graphical Model |
概率图模型 |
PGM |
[1] |
|
AITD-01502 |
Probabilistic Latent Semantic Analysis |
概率潜在语义分析 |
PLSA |
[1] |
|
AITD-01503 |
Probabilistic Latent Semantic Indexing |
概率潜在语义索引 |
PLSI |
[1] |
|
AITD-01504 |
Probabilistic Model |
概率模型 |
|
[1] |
|
AITD-01505 |
Probabilistic PCA |
概率PCA |
|
[1] |
|
AITD-01506 |
Probabilistic Undirected Graphical Model |
概率无向图模型 |
|
[1] |
|
AITD-01507 |
Probability |
概率 |
|
[1] |
|
AITD-01508 |
Probability Density Function |
概率密度函数 |
PDF |
[1] |
|
AITD-01509 |
Probability Distribution |
概率分布 |
|
[1] |
统计 |
AITD-01510 |
Probability Mass Function |
概率质量函数 |
|
[1] |
|
AITD-01511 |
Probability Model Estimation |
概率模型估计 |
|
[1] |
|
AITD-01512 |
Probably Approximately Correct |
概率近似正确 |
PAC |
[1] |
|
AITD-01513 |
Product of Expert |
专家之积 |
|
[1] |
|
AITD-01514 |
Product Rule |
乘法法则 |
|
[1] |
|
AITD-01515 |
Properly PAC Learnable |
恰PAC可学习 |
|
[1] |
|
AITD-01516 |
Proportional |
成比例 |
|
[1] |
|
AITD-01517 |
Proposal Distribution |
提议分布 |
|
[1] |
|
AITD-01518 |
Propositional Atom |
原子命题 |
|
[1] |
|
AITD-01519 |
Propositional Rule |
命题规则 |
|
[1] |
|
AITD-01520 |
Prototype-Based Clustering |
原型聚类 |
|
[1] |
|
AITD-01521 |
Proximal Gradient Descent |
近端梯度下降 |
PGD |
[1] |
|
AITD-01522 |
Pruning |
剪枝 |
|
[1] |
|
AITD-01523 |
Pseudo-Label |
伪标记 |
|
[1] |
|
AITD-01524 |
Pseudolikelihood |
伪似然 |
|
[1] |
|
AITD-01525 |
Q Function |
Q函数 |
|
[1] |
|
AITD-01526 |
Q-Learning |
Q学习 |
|
[1] |
|
AITD-01527 |
Q-Network |
Q网络 |
|
[1] |
|
AITD-01528 |
Quadratic Loss Function |
平方损失函数 |
|
[1] |
|
AITD-01529 |
Quadratic Programming |
二次规划 |
|
[1] |
|
AITD-01530 |
Quadrature Pair |
象限对 |
|
[1] |
|
AITD-01531 |
Quantized Neural Network |
量子化神经网络 |
QNN |
[1] |
|
AITD-01532 |
Quantum Computer |
量子计算机 |
|
[1][2][3] |
|
AITD-01533 |
Quantum Computing |
量子计算 |
|
[1][2][3] |
|
AITD-01534 |
Quantum Machine Learning |
量子机器学习 |
|
[1] |
|
AITD-01535 |
Quantum Mechanics |
量子力学 |
|
[1] |
物理 |
AITD-01536 |
Quasi Newton Method |
拟牛顿法 |
|
[1] |
|
AITD-01537 |
Quasi-Concave |
拟凹 |
|
[1] |
|
AITD-01538 |
Query |
查询 |
|
[1] |
|
AITD-01539 |
Query Vector |
查询向量 |
|
[1] |
|
AITD-01540 |
Query-Key-Value |
查询-键-值 |
QKV |
[1] |
|
AITD-01541 |
Radial Basis Function |
径向基函数 |
RBF |
[1][2] |
|
AITD-01542 |
Random Access Memory |
随机访问存储 |
RAM |
[1] |
|
AITD-01543 |
Random Field |
随机场 |
|
[1] |
|
AITD-01544 |
Random Forest Algorithm |
随机森林算法 |
|
[1] |
|
AITD-01545 |
Random Forest |
随机森林 |
RF、RFS |
[1][2][3][4][5] |
统计 |
AITD-01546 |
Random Initialization |
随机初始化 |
|
[1] |
|
AITD-01547 |
Random Sampling |
随机采样 |
|
[1][2] |
统计 |
AITD-01548 |
Random Search |
随机搜索 |
|
[1] |
|
AITD-01549 |
Random Subspace |
随机子空间 |
|
[1] |
|
AITD-01550 |
Random Variable |
随机变量 |
|
[1] |
|
AITD-01551 |
Random Walk |
随机游走 |
|
[1] |
|
AITD-01552 |
Range |
值域 |
|
[1] |
|
AITD-01553 |
Rank |
秩 |
|
[1] |
|
AITD-01554 |
Ratio Matching |
比率匹配 |
|
[1] |
|
AITD-01555 |
Raw Feature |
原始特征 |
|
[1] |
|
AITD-01556 |
Re-Balance |
再平衡 |
|
[1] |
|
AITD-01557 |
Re-Sampling |
重采样 |
|
[1] |
|
AITD-01558 |
Re-Weighting |
重赋权 |
|
[1] |
|
AITD-01559 |
Readout Function |
读出函数 |
|
[1] |
|
AITD-01560 |
Real-Time Recurrent Learning |
实时循环学习 |
RTRL |
[1] |
|
AITD-01561 |
Recall |
查全率/召回率 |
|
[1] |
|
AITD-01562 |
Recall-Oriented Understudy For Gisting Evaluation |
ROUGE |
|
[1] |
|
AITD-01563 |
Receiver Operating Characteristic |
受试者工作特征 |
ROC |
[1] |
|
AITD-01564 |
Receptive Field |
感受野 |
|
[1] |
|
AITD-01565 |
Recirculation |
再循环 |
|
[1] |
|
AITD-01566 |
Recognition Weight |
认知权重 |
|
[1] |
|
AITD-01567 |
Recommender System |
推荐系统 |
|
[1] |
|
AITD-01568 |
Reconstruction |
重构 |
|
[1] |
|
AITD-01569 |
Reconstruction Error |
重构误差 |
|
[1] |
|
AITD-01570 |
Rectangular Diagonal Matrix |
矩形对角矩阵 |
|
[1] |
|
AITD-01571 |
Rectified Linear |
整流线性 |
|
[1] |
|
AITD-01572 |
Rectified Linear Transformation |
整流线性变换 |
|
[1] |
|
AITD-01573 |
Rectified Linear Unit |
修正线性单元/整流线性单元 |
ReLU |
[1][2] |
CHAPTER 2 |
AITD-01574 |
Rectifier Network |
整流网络 |
|
[1] |
|
AITD-01575 |
Recurrence |
循环 |
|
[1] |
|
AITD-01576 |
Recurrent Convolutional Network |
循环卷积网络 |
|
[1] |
|
AITD-01577 |
Recurrent Multi-Layer Perceptron |
循环多层感知器 |
RMLP |
[1] |
|
AITD-01578 |
Recurrent Network |
循环网络 |
|
[1] |
|
AITD-01579 |
Recurrent Neural Network |
循环神经网络 |
RNN |
[1][2][3][4][5][6] |
机器学习 |
AITD-01580 |
Recursive Neural Network |
递归神经网络 |
RecNN |
[1] |
|
AITD-01581 |
Reducible |
可约的 |
|
[1] |
|
AITD-01582 |
Redundant Feature |
冗余特征 |
|
[1] |
|
AITD-01583 |
Reference Model |
参考模型 |
|
[1] |
|
AITD-01584 |
Region |
区域 |
|
[1] |
|
AITD-01585 |
Regression |
回归 |
|
[1][2][3] |
统计 |
AITD-01586 |
Regularization |
正则化 |
|
[1] |
|
AITD-01587 |
Regularizer |
正则化项 |
|
[1] |
|
AITD-01588 |
Reinforcement Learning |
强化学习 |
RL |
[1][2][3][4][5] |
机器学习 |
AITD-01589 |
Rejection Sampling |
拒绝采样 |
|
[1] |
|
AITD-01590 |
Relation |
关系 |
|
[1] |
|
AITD-01591 |
Relational Database |
关系型数据库 |
|
[1] |
|
AITD-01592 |
Relative Entropy |
相对熵 |
|
[1] |
|
AITD-01593 |
Relevant Feature |
相关特征 |
|
[1] |
|
AITD-01594 |
Reparameterization |
再参数化/重参数化 |
|
[1] |
|
AITD-01595 |
Reparametrization Trick |
重参数化技巧 |
|
[1] |
|
AITD-01596 |
Replay Buffer |
经验池 |
|
[1] |
|
AITD-01597 |
Representation |
表示 |
|
[1] |
|
AITD-01598 |
Representation Learning |
表示学习 |
|
[1] |
|
AITD-01599 |
Representational Capacity |
表示容量 |
|
[1] |
|
AITD-01600 |
Representer Theorem |
表示定理 |
|
[1] |
|
AITD-01601 |
Reproducing Kernel Hilbert Space |
再生核希尔伯特空间 |
RKHS |
[1] |
|
AITD-01602 |
Rescaling |
再缩放 |
|
[1] |
|
AITD-01603 |
Reservoir Computing |
储层计算 |
|
[1] |
|
AITD-01604 |
Reset Gate |
重置门 |
|
[1] |
|
AITD-01605 |
Residual Blocks |
残差块 |
|
[1] |
|
AITD-01606 |
Residual Connection |
残差连接 |
|
[1] |
|
AITD-01607 |
Residual Mapping |
残差映射 |
|
[1] |
|
AITD-01608 |
Residual Network |
残差网络 |
ResNet |
[1] |
|
AITD-01609 |
Residual Unit |
残差单元 |
|
[1] |
|
AITD-01610 |
Residue Function |
残差函数 |
|
[1] |
|
AITD-01611 |
Resolution Quotient |
归结商 |
|
[1] |
|
AITD-01612 |
Restricted Boltzmann Machine |
受限玻尔兹曼机 |
RBM |
[1] |
|
AITD-01613 |
Restricted Isometry Property |
限定等距性 |
RIP |
[1] |
|
AITD-01614 |
Return |
总回报 |
|
[1] |
|
AITD-01615 |
Reverse Correlation |
反向相关 |
|
[1] |
|
AITD-01616 |
Reverse KL Divergence |
逆向KL散度 |
|
[1] |
|
AITD-01617 |
Reverse Mode Accumulation |
反向模式累加 |
|
[1] |
|
AITD-01618 |
Reversible Markov Chain |
可逆马尔可夫链 |
|
[1] |
|
AITD-01619 |
Reward |
奖励 |
|
[1] |
|
AITD-01620 |
Reward Function |
奖励函数 |
|
[1] |
|
AITD-01621 |
Ridge Regression |
岭回归 |
|
[1] |
|
AITD-01622 |
Riemann Integral |
黎曼积分 |
|
[1] |
|
AITD-01623 |
Right Eigenvector |
右特征向量 |
|
[1] |
|
AITD-01624 |
Right Singular Vector |
右奇异向量 |
|
[1] |
|
AITD-01625 |
Risk |
风险 |
|
[1] |
|
AITD-01626 |
Risk Function |
风险函数 |
|
[1] |
|
AITD-01627 |
Robustness |
稳健性 |
|
[1] |
计算机、机器学习 |
AITD-01628 |
Root Node |
根结点 |
|
[1] |
|
AITD-01629 |
Round-Off Error |
舍入误差 |
|
[1] |
|
AITD-01630 |
Row |
行 |
|
[1] |
|
AITD-01631 |
Rule Engine |
规则引擎 |
|
[1] |
|
AITD-01632 |
Rule Learning |
规则学习 |
|
[1] |
|
AITD-01633 |
S-Fold Cross Validation |
S 折交叉验证 |
|
[1] |
|
AITD-01634 |
Saccade |
扫视 |
|
[1] |
|
AITD-01635 |
Saddle Point |
鞍点 |
|
[1] |
|
AITD-01636 |
Saddle-Free Newton Method |
无鞍牛顿法 |
|
[1] |
|
AITD-01637 |
Saliency Map |
显著图 |
|
[1] |
|
AITD-01638 |
Saliency-Based Attention |
基于显著性的注意力 |
|
[1] |
|
AITD-01639 |
Same |
相同 |
|
[1] |
|
AITD-01640 |
Sample |
样本 |
|
[1] |
|
AITD-01641 |
Sample Complexity |
样本复杂度 |
|
[1] |
|
AITD-01642 |
Sample Mean |
样本均值 |
|
[1] |
|
AITD-01643 |
Sample Space |
样本空间 |
|
[1] |
|
AITD-01644 |
Sample Variance |
样本方差 |
|
[1] |
|
AITD-01645 |
Sampling |
采样 |
|
[1] |
|
AITD-01646 |
Sampling Method |
采样法 |
|
[1] |
|
AITD-01647 |
Saturate |
饱和 |
|
[1] |
|
AITD-01648 |
Saturating Function |
饱和函数 |
|
[1] |
|
AITD-01649 |
Scalar |
标量 |
|
[1] |
|
AITD-01650 |
Scale Invariance |
尺度不变性 |
|
[1] |
|
AITD-01651 |
Scatter Matrix |
散布矩阵 |
|
[1] |
|
AITD-01652 |
Scheduled Sampling |
计划采样 |
|
[1] |
|
AITD-01653 |
Score |
得分 |
|
[1] |
|
AITD-01654 |
Score Function |
评分函数 |
|
[1] |
|
AITD-01655 |
Score Matching |
分数匹配 |
|
[1] |
|
AITD-01656 |
Second Derivative |
二阶导数 |
|
[1] |
|
AITD-01657 |
Second Derivative Test |
二阶导数测试 |
|
[1] |
|
AITD-01658 |
Second Layer |
第二层 |
|
[1] |
|
AITD-01659 |
Second-Order Method |
二阶方法 |
|
[1] |
|
AITD-01660 |
Selective Attention |
选择性注意力 |
|
[1] |
|
AITD-01661 |
Selective Ensemble |
选择性集成 |
|
[1] |
|
AITD-01662 |
Self Information |
自信息 |
|
[1] |
|
AITD-01663 |
Self-Attention |
自注意力 |
|
[1] |
|
AITD-01664 |
Self-Attention Model |
自注意力模型 |
|
[1] |
|
AITD-01665 |
Self-Contrastive Estimation |
自对比估计 |
|
[1] |
|
AITD-01666 |
Self-Driving |
自动驾驶 |
|
[1][2][3] |
|
AITD-01667 |
Self-Gated |
自门控 |
|
[1] |
|
AITD-01668 |
Self-Organizing Map |
自组织映射网 |
SOM |
[1] |
|
AITD-01669 |
Self-Taught Learning |
自学习 |
|
[1] |
|
AITD-01670 |
Self-Training |
自训练 |
|
[1] |
|
AITD-01671 |
Semantic Gap |
语义鸿沟 |
|
[1] |
|
AITD-01672 |
Semantic Hashing |
语义哈希 |
|
[1] |
|
AITD-01673 |
Semantic Segmentation |
语义分割 |
|
[1] |
|
AITD-01674 |
Semantic Similarity |
语义相似度 |
|
[1] |
|
AITD-01675 |
Semi-Definite Programming |
半正定规划 |
|
[1] |
|
AITD-01676 |
Semi-Naive Bayes Classifiers |
半朴素贝叶斯分类器 |
|
[1] |
|
AITD-01677 |
Semi-Restricted Boltzmann Machine |
半受限玻尔兹曼机 |
|
[1] |
|
AITD-01678 |
Semi-Supervised |
半监督 |
|
[1] |
|
AITD-01679 |
Semi-Supervised Clustering |
半监督聚类 |
|
[1] |
|
AITD-01680 |
Semi-Supervised Learning |
半监督学习 |
|
[1][2][3] |
|
AITD-01681 |
Semi-Supervised Support Vector Machine |
半监督支持向量机 |
S3VM |
[1] |
|
AITD-01682 |
Sentiment Analysis |
情感分析 |
|
[1] |
|
AITD-01683 |
Separable |
可分离的 |
|
[1] |
|
AITD-01684 |
Separate |
分离的 |
|
[1] |
|
AITD-01685 |
Separating Hyperplane |
分离超平面 |
|
[1] |
|
AITD-01686 |
Separation |
分离 |
|
[1] |
|
AITD-01687 |
Sequence Labeling |
序列标注 |
|
[1] |
|
AITD-01688 |
Sequence To Sequence Learning |
序列到序列学习 |
Seq2Seq |
[1] |
|
AITD-01689 |
Sequence-To-Sequence |
序列到序列 |
Seq2Seq |
[1] |
|
AITD-01690 |
Sequential Covering |
序贯覆盖 |
|
[1] |
|
AITD-01691 |
Sequential Minimal Optimization |
序列最小最优化 |
SMO |
[1] |
|
AITD-01692 |
Sequential Model-Based Optimization |
时序模型优化 |
SMBO |
[1] |
|
AITD-01693 |
Sequential Partitioning |
顺序分区 |
|
[1] |
|
AITD-01694 |
Setting |
情景 |
|
[1] |
|
AITD-01695 |
Shadow Circuit |
浅度回路 |
|
[1] |
|
AITD-01696 |
Shallow Learning |
浅层学习 |
|
[1] |
|
AITD-01697 |
Shannon Entropy |
香农熵 |
|
[1] |
|
AITD-01698 |
Shannons |
香农 |
|
[1] |
|
AITD-01699 |
Shaping |
塑造 |
|
[1] |
|
AITD-01700 |
Sharp Minima |
尖锐最小值 |
|
[1] |
|
AITD-01701 |
Shattering |
打散 |
|
[1] |
|
AITD-01702 |
Shift Invariance |
平移不变性 |
|
[1] |
|
AITD-01703 |
Short-Term Memory |
短期记忆 |
|
[1] |
|
AITD-01704 |
Shortcut Connection |
直连边 |
|
[1] |
|
AITD-01705 |
Shortlist |
短列表 |
|
[1] |
|
AITD-01706 |
Siamese Network |
孪生网络 |
|
[1] |
|
AITD-01707 |
Sigmoid |
Sigmoid(一种激活函数) |
|
[1] |
统计 |
AITD-01708 |
Sigmoid Belief Network |
Sigmoid信念网络 |
SBN |
[1] |
|
AITD-01709 |
Sigmoid Curve |
S 形曲线 |
|
[1] |
|
AITD-01710 |
Sigmoid Function |
Sigmoid函数 |
|
[1] |
|
AITD-01711 |
Sign Function |
符号函数 |
|
[1] |
|
AITD-01712 |
Signed Distance |
带符号距离 |
|
[1] |
|
AITD-01713 |
Similarity |
相似度 |
|
[1] |
|
AITD-01714 |
Similarity Measure |
相似度度量 |
|
[1] |
|
AITD-01715 |
Simple Cell |
简单细胞 |
|
[1] |
|
AITD-01716 |
Simple Recurrent Network |
简单循环网络 |
SRN |
[1] |
|
AITD-01717 |
Simple Recurrent Neural Network |
简单循环神经网络 |
S-RNN |
[1] |
|
AITD-01718 |
Simplex |
单纯形 |
|
[1] |
|
AITD-01719 |
Simulated Annealing |
模拟退火 |
|
[1] |
统计、机器学习 |
AITD-01720 |
Simultaneous Localization And Mapping |
即时定位与地图构建 |
SLAM |
[1] |
|
AITD-01721 |
Single Component Metropolis-Hastings |
单分量Metropolis-Hastings |
|
[1] |
|
AITD-01722 |
Single Linkage |
单连接 |
|
[1] |
|
AITD-01723 |
Singular |
奇异的 |
|
[1] |
|
AITD-01724 |
Singular Value |
奇异值 |
|
[1] |
|
AITD-01725 |
Singular Value Decomposition |
奇异值分解 |
SVD |
[1] |
|
AITD-01726 |
Singular Vector |
奇异向量 |
|
[1] |
|
AITD-01727 |
Size |
大小 |
|
[1] |
|
AITD-01728 |
Skip Connection |
跳跃连接 |
|
[1] |
|
AITD-01729 |
Skip-Gram Model |
跳元模型 |
|
[1] |
|
AITD-01730 |
Skip-Gram Model With Negative Sampling |
跳元模型加负采样 |
|
[1] |
|
AITD-01731 |
Slack Variable |
松弛变量 |
|
[1] |
|
AITD-01732 |
Slow Feature Analysis |
慢特征分析 |
|
[1] |
|
AITD-01733 |
Slowness Principle |
慢性原则 |
|
[1] |
|
AITD-01734 |
Smoothing |
平滑 |
|
[1] |
|
AITD-01735 |
Smoothness Prior |
平滑先验 |
|
[1] |
|
AITD-01736 |
Soft Attention Mechanism |
软性注意力机制 |
|
[1] |
|
AITD-01737 |
Soft Clustering |
软聚类 |
|
[1] |
|
AITD-01738 |
Soft Margin |
软间隔 |
|
[1] |
|
AITD-01739 |
Soft Margin Maximization |
软间隔最大化 |
|
[1] |
|
AITD-01740 |
Soft Target |
软目标 |
|
[1] |
|
AITD-01741 |
Soft Voting |
软投票 |
|
[1] |
|
AITD-01742 |
Softmax |
Softmax/软最大化 |
|
[1] |
|
AITD-01743 |
Softmax Function |
Softmax函数/软最大化函数 |
|
[1] |
统计、机器学习 |
AITD-01744 |
Softmax Regression |
Softmax回归/软最大化回归 |
|
[1] |
|
AITD-01745 |
Softmax Unit |
Softmax单元/软最大化单元 |
|
[1] |
|
AITD-01746 |
Softplus |
Softplus |
|
[1] |
|
AITD-01747 |
Softplus Function |
Softplus函数 |
|
[1] |
|
AITD-01748 |
Source Domain |
源领域 |
|
[1] |
|
AITD-01749 |
Span |
张成子空间 |
|
[1] |
|
AITD-01750 |
Sparse |
稀疏 |
|
[1] |
|
AITD-01751 |
Sparse Activation |
稀疏激活 |
|
[1] |
|
AITD-01752 |
Sparse Auto-Encoder |
稀疏自编码器 |
|
[1] |
|
AITD-01753 |
Sparse Coding |
稀疏编码 |
|
[1] |
|
AITD-01754 |
Sparse Connectivity |
稀疏连接 |
|
[1] |
|
AITD-01755 |
Sparse Initialization |
稀疏初始化 |
|
[1] |
|
AITD-01756 |
Sparse Interactions |
稀疏交互 |
|
[1] |
|
AITD-01757 |
Sparse Representation |
稀疏表示 |
|
[1] |
|
AITD-01758 |
Sparse Weights |
稀疏权重 |
|
[1] |
|
AITD-01759 |
Sparsity |
稀疏性 |
|
[1] |
|
AITD-01760 |
Specialization |
特化 |
|
[1] |
|
AITD-01761 |
Spectral Clustering |
谱聚类 |
|
[1] |
|
AITD-01762 |
Spectral Radius |
谱半径 |
|
[1] |
|
AITD-01763 |
Speech Recognition |
语音识别 |
|
[1][2][3][4][5][6] |
|
AITD-01764 |
Sphering |
Sphering |
|
[1] |
|
AITD-01765 |
Spike And Slab |
尖峰和平板 |
|
[1] |
|
AITD-01766 |
Spike And Slab RBM |
尖峰和平板RBM |
|
[1] |
|
AITD-01767 |
Spiking Neural Nets |
脉冲神经网络 |
|
[1] |
|
AITD-01768 |
Splitting Point |
切分点 |
|
[1] |
|
AITD-01769 |
Splitting Variable |
切分变量 |
|
[1] |
|
AITD-01770 |
Spurious Modes |
虚假模态 |
|
[1] |
|
AITD-01771 |
Square |
方阵 |
|
[1] |
|
AITD-01772 |
Square Loss |
平方损失 |
|
[1] |
|
AITD-01773 |
Squared Euclidean Distance |
欧氏距离平方 |
|
[1] |
|
AITD-01774 |
Squared Exponential |
平方指数 |
|
[1] |
|
AITD-01775 |
Squashing Function |
挤压函数 |
|
[1] |
|
AITD-01776 |
Stability |
稳定性 |
|
[1] |
|
AITD-01777 |
Stability-Plasticity Dilemma |
可塑性-稳定性窘境 |
|
[1] |
|
AITD-01778 |
Stable Base Learner |
稳定基学习器 |
|
[1] |
|
AITD-01779 |
Stacked Auto-Encoder |
堆叠自编码器 |
SAE |
[1] |
|
AITD-01780 |
Stacked Deconvolutional Network |
堆叠解卷积网络 |
SDN |
[1] |
|
AITD-01781 |
Stacked Recurrent Neural Network |
堆叠循环神经网络 |
SRNN |
[1] |
|
AITD-01782 |
Standard Basis |
标准基 |
|
[1] |
|
AITD-01783 |
Standard Deviation |
标准差 |
|
[1] |
|
AITD-01784 |
Standard Error |
标准差 |
|
[1] |
|
AITD-01785 |
Standard Normal Distribution |
标准正态分布 |
|
[1] |
|
AITD-01786 |
Standardization |
标准化 |
|
[1] |
|
AITD-01787 |
State |
状态 |
|
[1] |
|
AITD-01788 |
State Action Reward State Action |
SARSA算法 |
SARSA |
[1] |
|
AITD-01789 |
State Sequence |
状态序列 |
|
[1] |
|
AITD-01790 |
State Space |
状态空间 |
|
[1] |
|
AITD-01791 |
State Value Function |
状态值函数 |
|
[1] |
|
AITD-01792 |
State-Action Value Function |
状态-动作值函数 |
|
[1] |
|
AITD-01793 |
Statement |
声明 |
|
[1] |
|
AITD-01794 |
Static Computational Graph |
静态计算图 |
|
[1] |
|
AITD-01795 |
Static Game |
静态博弈 |
|
[1] |
|
AITD-01796 |
Stationary |
平稳的 |
|
[1] |
|
AITD-01797 |
Stationary Distribution |
平稳分布 |
|
[1] |
|
AITD-01798 |
Stationary Point |
驻点 |
|
[1] |
|
AITD-01799 |
Statistic Efficiency |
统计效率 |
|
[1] |
|
AITD-01800 |
Statistical Learning |
统计学习 |
|
[1][2] |
|
AITD-01801 |
Statistical Learning Theory |
统计学习理论 |
|
[1] |
|
AITD-01802 |
Statistical Machine Learning |
统计机器学习 |
|
[1] |
|
AITD-01803 |
Statistical Relational Learning |
统计关系学习 |
|
[1] |
|
AITD-01804 |
Statistical Simulation Method |
统计模拟方法 |
|
[1] |
|
AITD-01805 |
Statistics |
统计量 |
|
[1] |
|
AITD-01806 |
Status Feature Function |
状态特征函数 |
|
[1] |
|
AITD-01807 |
Steepest Descent |
最速下降法 |
|
[1] |
|
AITD-01808 |
Step Decay |
阶梯衰减 |
|
[1] |
|
AITD-01809 |
Stochastic |
随机 |
|
[1] |
|
AITD-01810 |
Stochastic Curriculum |
随机课程 |
|
[1] |
|
AITD-01811 |
Stochastic Dynamical System |
随机动力系统 |
|
[1] |
|
AITD-01812 |
Stochastic Gradient Ascent |
随机梯度上升 |
|
[1] |
|
AITD-01813 |
Stochastic Gradient Descent |
随机梯度下降 |
|
[1] |
|
AITD-01814 |
Stochastic Gradient Descent With Warm Restarts |
带热重启的随机梯度下降 |
SGDR |
[1] |
|
AITD-01815 |
Stochastic Matrix |
随机矩阵 |
|
[1] |
|
AITD-01816 |
Stochastic Maximum Likelihood |
随机最大似然 |
|
[1] |
|
AITD-01817 |
Stochastic Neighbor Embedding |
随机近邻嵌入 |
|
[1] |
|
AITD-01818 |
Stochastic Neural Network |
随机神经网络 |
SNN |
[1] |
|
AITD-01819 |
Stochastic Policy |
随机性策略 |
|
[1] |
|
AITD-01820 |
Stochastic Process |
随机过程 |
|
[1] |
|
AITD-01821 |
Stop Words |
停用词 |
|
[1] |
|
AITD-01822 |
Stratified Sampling |
分层采样 |
|
[1] |
|
AITD-01823 |
Stream |
流 |
|
[1] |
|
AITD-01824 |
Stride |
步幅 |
|
[1] |
|
AITD-01825 |
String Kernel Function |
字符串核函数 |
|
[1] |
|
AITD-01826 |
Strong Classifier |
强分类器 |
|
[1] |
|
AITD-01827 |
Strong Duality |
强对偶性 |
|
[1] |
|
AITD-01828 |
Strongly Connected Graph |
强连通图 |
|
[1] |
|
AITD-01829 |
Strongly Learnable |
强可学习 |
|
[1] |
|
AITD-01830 |
Structural Risk |
结构风险 |
|
[1] |
|
AITD-01831 |
Structural Risk Minimization |
结构风险最小化 |
SRM |
[1] |
|
AITD-01832 |
Structure Learning |
结构学习 |
|
[1] |
|
AITD-01833 |
Structured Learning |
结构化学习 |
|
[1] |
|
AITD-01834 |
Structured Probabilistic Model |
结构化概率模型 |
|
[1] |
|
AITD-01835 |
Structured Variational Inference |
结构化变分推断 |
|
[1] |
|
AITD-01836 |
Student Network |
学生网络 |
|
[1] |
|
AITD-01837 |
Sub-Optimal |
次最优 |
|
[1] |
|
AITD-01838 |
Subatomic |
亚原子 |
|
[1] |
|
AITD-01839 |
Subsample |
子采样 |
|
[1] |
|
AITD-01840 |
Subsampling |
下采样 |
|
[1] |
|
AITD-01841 |
Subsampling Layer |
子采样层 |
|
[1] |
|
AITD-01842 |
Subset Evaluation |
子集评价 |
|
[1] |
|
AITD-01843 |
Subset Search |
子集搜索 |
|
[1] |
|
AITD-01844 |
Subspace |
子空间 |
|
[1] |
|
AITD-01845 |
Substitution |
置换 |
|
[1] |
|
AITD-01846 |
Successive Halving |
逐次减半 |
|
[1] |
|
AITD-01847 |
Sum Rule |
求和法则 |
|
[1] |
|
AITD-01848 |
Sum-Product |
和积 |
|
[1] |
|
AITD-01849 |
Sum-Product Network |
和-积网络 |
|
[1] |
|
AITD-01850 |
Super-Parent |
超父 |
|
[1] |
|
AITD-01851 |
Supervised |
监督 |
|
[1] |
|
AITD-01852 |
Supervised Learning |
监督学习 |
|
[1][2][3] |
机器学习 |
AITD-01853 |
Supervised Learning Algorithm |
监督学习算法 |
|
[1] |
|
AITD-01854 |
Supervised Model |
监督模型 |
|
[1] |
|
AITD-01855 |
Supervised Pretraining |
监督预训练 |
|
[1] |
|
AITD-01856 |
Support Vector |
支持向量 |
|
[1] |
统计、机器学习 |
AITD-01857 |
Support Vector Expansion |
支持向量展式 |
|
[1] |
|
AITD-01858 |
Support Vector Machine |
支持向量机 |
SVM |
[1][2][3][4] |
统计、机器学习 |
AITD-01859 |
Support Vector Regression |
支持向量回归 |
SVR |
[1][2][3] |
统计、机器学习 |
AITD-01860 |
Surrogat Loss |
替代损失 |
|
[1] |
|
AITD-01861 |
Surrogate Function |
替代函数 |
|
[1] |
|
AITD-01862 |
Surrogate Loss Function |
代理损失函数 |
|
[1] |
|
AITD-01863 |
Symbol |
符号 |
|
[1] |
|
AITD-01864 |
Symbolic Differentiation |
符号微分 |
|
[1] |
|
AITD-01865 |
Symbolic Learning |
符号学习 |
|
[1] |
|
AITD-01866 |
Symbolic Representation |
符号表示 |
|
[1] |
|
AITD-01867 |
Symbolism |
符号主义 |
|
[1] |
|
AITD-01868 |
Symmetric |
对称 |
|
[1] |
|
AITD-01869 |
Symmetric Matrix |
对称矩阵 |
|
[1] |
|
AITD-01870 |
Synonymy |
多词一义性 |
|
[1] |
|
AITD-01871 |
Synset |
同义词集 |
|
[1] |
|
AITD-01872 |
Synthetic Feature |
合成特征 |
|
[1] |
|
AITD-01873 |
T-Distribution Stochastic Neighbour Embedding |
T分布随机近邻嵌入 |
T-SNE |
[1] |
|
AITD-01874 |
Tabular Value Function |
表格值函数 |
|
[1] |
|
AITD-01875 |
Tagging |
标注 |
|
[1] |
|
AITD-01876 |
Tangent Distance |
切面距离 |
|
[1] |
|
AITD-01877 |
Tangent Plane |
切平面 |
|
[1] |
|
AITD-01878 |
Tangent Propagation |
正切传播 |
|
[1] |
|
AITD-01879 |
Target |
目标 |
|
[1] |
|
AITD-01880 |
Target Domain |
目标领域 |
|
[1] |
|
AITD-01881 |
Taylor |
泰勒 |
|
[1] |
|
AITD-01882 |
Taylor's Formula |
泰勒公式 |
|
[1] |
|
AITD-01883 |
Teacher Forcing |
强制教学 |
|
[1] |
|
AITD-01884 |
Teacher Network |
教师网络 |
|
[1] |
|
AITD-01885 |
Temperature |
温度 |
|
[1] |
|
AITD-01886 |
Tempered Transition |
回火转移 |
|
[1] |
|
AITD-01887 |
Tempering |
回火 |
|
[1] |
|
AITD-01888 |
Temporal-Difference Learning |
时序差分学习 |
|
[1] |
|
AITD-01889 |
Tensor |
张量 |
|
[1] |
|
AITD-01890 |
Tensor Processing Units |
张量处理单元 |
TPU |
[1] |
|
AITD-01891 |
Term Frequency-Inverse Document Frequency |
单词频率-逆文本频率 |
TF-IDF |
[1] |
|
AITD-01892 |
Terminal State |
终止状态 |
|
[1] |
|
AITD-01893 |
Test Data |
测试数据 |
|
[1] |
|
AITD-01894 |
Test Error |
测试误差 |
|
[1] |
|
AITD-01895 |
Test Sample |
测试样本 |
|
[1] |
|
AITD-01896 |
Test Set |
测试集 |
|
[1][2][3] |
机器学习 |
AITD-01897 |
The Collider Case |
碰撞情况 |
|
[1] |
|
AITD-01898 |
Threshold |
阈值 |
|
[1] |
数学 |
AITD-01899 |
Threshold Logic Unit |
阈值逻辑单元 |
|
[1] |
|
AITD-01900 |
Threshold-Moving |
阈值移动 |
|
[1] |
|
AITD-01901 |
Tied Weight |
捆绑权重 |
|
[1] |
|
AITD-01902 |
Tikhonov Regularization |
Tikhonov正则化 |
|
[1] |
|
AITD-01903 |
Tiled Convolution |
平铺卷积 |
|
[1] |
|
AITD-01904 |
Time Delay Neural Network |
时延神经网络 |
TDNN |
[1] |
|
AITD-01905 |
Time Homogenous Markov Chain |
时间齐次马尔可夫链 |
|
[1] |
|
AITD-01906 |
Time Step |
时间步 |
|
[1] |
|
AITD-01907 |
Toeplitz Matrix |
Toeplitz矩阵 |
|
[1] |
|
AITD-01908 |
Token |
词元 |
|
[1] |
|
AITD-01909 |
Tokenize |
词元化 |
|
[1] |
|
AITD-01910 |
Tokenization |
词元化 |
|
[1] |
|
AITD-01911 |
Tokenizer |
词元分析器 |
|
[1] |
|
AITD-01912 |
Tolerance |
容差 |
|
[1] |
|
AITD-01913 |
Top-Down |
自顶向下 |
|
[1] |
|
AITD-01914 |
Topic |
话题 |
|
[1] |
|
AITD-01915 |
Topic Model |
话题模型 |
|
[1] |
|
AITD-01916 |
Topic Modeling |
话题分析 |
|
[1] |
|
AITD-01917 |
Topic Vector Space |
话题向量空间 |
|
[1] |
|
AITD-01918 |
Topic Vector Space Model |
话题向量空间模型 |
|
[1] |
|
AITD-01919 |
Topic-Document Matrix |
话题-文本矩阵 |
|
[1] |
|
AITD-01920 |
Topographic ICA |
地质ICA |
|
[1] |
|
AITD-01921 |
Total Cost |
总体代价 |
|
[1] |
|
AITD-01922 |
Trace |
迹 |
|
[1] |
|
AITD-01923 |
Tractable |
易处理的 |
|
[1] |
|
AITD-01924 |
Training |
训练 |
|
[1] |
|
AITD-01925 |
Training Data |
训练数据 |
|
[1] |
|
AITD-01926 |
Training Error |
训练误差 |
|
[1] |
|
AITD-01927 |
Training Instance |
训练实例 |
|
[1] |
|
AITD-01928 |
Training Sample |
训练样本 |
|
[1] |
机器学习 |
AITD-01929 |
Training Set |
训练集 |
|
[1][2] |
机器学习 |
AITD-01930 |
Trajectory |
轨迹 |
|
[1] |
|
AITD-01931 |
Transcribe |
转录 |
|
[1] |
|
AITD-01932 |
Transcription System |
转录系统 |
|
[1] |
|
AITD-01933 |
Transductive Learning |
直推学习 |
|
[1] |
|
AITD-01934 |
Transductive Transfer Learning |
直推迁移学习 |
|
[1] |
|
AITD-01935 |
Transfer Learning |
迁移学习 |
|
[1][2][3][4] |
|
AITD-01936 |
Transform |
变换 |
|
[1] |
|
AITD-01937 |
Transformer |
Transformer |
|
[1] |
|
AITD-01938 |
Transformer Model |
Transformer模型 |
|
[1] |
|
AITD-01939 |
Transition |
转移 |
|
[1] |
|
AITD-01940 |
Transition Kernel |
转移核 |
|
[1] |
|
AITD-01941 |
Transition Matrix |
状态转移矩阵 |
|
[1] |
|
AITD-01942 |
Transition Probability |
转移概率 |
|
[1] |
|
AITD-01943 |
Transpose |
转置 |
|
[1] |
|
AITD-01944 |
Transposed Convolution |
转置卷积 |
|
[1] |
|
AITD-01945 |
Tree-Structured LSTM |
树结构的长短期记忆模型 |
|
[1] |
|
AITD-01946 |
Treebank |
树库 |
|
[1] |
|
AITD-01947 |
Trial |
试验 |
|
[1] |
|
AITD-01948 |
Trial And Error |
试错 |
|
[1] |
|
AITD-01949 |
Triangle Inequality |
三角不等式 |
|
[1] |
|
AITD-01950 |
Triangular Cyclic Learning Rate |
三角循环学习率 |
|
[1] |
|
AITD-01951 |
Triangulate |
三角形化 |
|
[1] |
|
AITD-01952 |
Triangulated Graph |
三角形化图 |
|
[1] |
|
AITD-01953 |
Trigram |
三元语法 |
|
[1] |
|
AITD-01954 |
True Negative |
真负例 |
TN |
[1] |
统计 |
AITD-01955 |
True Positive |
真正例 |
TP |
[1] |
统计 |
AITD-01956 |
True Positive Rate |
真正例率 |
TPR |
[1] |
统计 |
AITD-01957 |
Truncated Singular Value Decomposition |
截断奇异值分解 |
|
[1] |
|
AITD-01958 |
Truncation Error |
截断误差 |
|
[1] |
|
AITD-01959 |
Turing Completeness |
图灵完备 |
|
[1] |
|
AITD-01960 |
Turing Machine |
图灵机 |
|
[1] |
|
AITD-01961 |
Twice-Learning |
二次学习 |
|
[1] |
|
AITD-01962 |
Two-Dimensional Array |
二维数组 |
|
[1] |
|
AITD-01963 |
Ugly Duckling Theorem |
丑小鸭定理 |
|
[1] |
|
AITD-01964 |
Unbiased |
无偏 |
|
[1] |
|
AITD-01965 |
Unbiased Estimate |
无偏估计 |
|
[1] |
|
AITD-01966 |
Unbiased Sample Variance |
无偏样本方差 |
|
[1] |
|
AITD-01967 |
Unconstrained Optimization |
无约束优化 |
|
[1] |
|
AITD-01968 |
Undercomplete |
欠完备 |
|
[1] |
|
AITD-01969 |
Underdetermined |
欠定的 |
|
[1] |
|
AITD-01970 |
Underestimation |
欠估计 |
|
[1] |
|
AITD-01971 |
Underfitting |
欠拟合 |
|
[1] |
机器学习 |
AITD-01972 |
Underfitting Regime |
欠拟合机制 |
|
[1] |
|
AITD-01973 |
Underflow |
下溢 |
|
[1] |
|
AITD-01974 |
Underlying |
潜在 |
|
[1] |
|
AITD-01975 |
Underlying Cause |
潜在成因 |
|
[1] |
|
AITD-01976 |
Undersampling |
欠采样 |
|
[1] |
|
AITD-01977 |
Understandability |
可理解性 |
|
[1] |
|
AITD-01978 |
Undirected |
无向 |
|
[1] |
|
AITD-01979 |
Undirected Graph |
无向图 |
|
[1] |
|
AITD-01980 |
Undirected Graphical Model |
无向图模型 |
|
[1] |
|
AITD-01981 |
Undirected Model |
无向模型 |
|
[1] |
|
AITD-01982 |
Unequal Cost |
非均等代价 |
|
[1] |
|
AITD-01983 |
Unfolded Graph |
展开图 |
|
[1] |
|
AITD-01984 |
Unfolding |
展开 |
|
[1] |
|
AITD-01985 |
Unidirectional Language Model |
单向语言模型 |
|
[1] |
|
AITD-01986 |
Unification |
合一 |
|
[1] |
|
AITD-01987 |
Uniform Distribution |
均匀分布 |
|
[1] |
|
AITD-01988 |
Uniform Sampling |
均匀采样 |
|
[1] |
|
AITD-01989 |
Uniform Stability |
均匀稳定性 |
|
[1] |
|
AITD-01990 |
Unigram |
一元语法 |
|
[1] |
|
AITD-01991 |
Unimodal |
单峰值 |
|
[1] |
|
AITD-01992 |
Unit |
单元 |
|
[1] |
|
AITD-01993 |
Unit Norm |
单位范数 |
|
[1] |
|
AITD-01994 |
Unit Test |
单元测试 |
|
[1] |
|
AITD-01995 |
Unit Variance |
单位方差 |
|
[1] |
|
AITD-01996 |
Unit Vector |
单位向量 |
|
[1] |
|
AITD-01997 |
Unit-Step Function |
单位阶跃函数 |
|
[1] |
|
AITD-01998 |
Unitary Matrix |
酉矩阵 |
|
[1] |
|
AITD-01999 |
Univariate Decision Tree |
单变量决策树 |
|
[1] |
|
AITD-02000 |
Universal Approximation Theorem |
通用近似定理 |
|
[1] |
|
AITD-02001 |
Universal Approximator |
通用近似器 |
|
[1] |
|
AITD-02002 |
Universal Function Approximator |
通用函数近似器 |
|
[1] |
|
AITD-02003 |
Unknown Token |
未知词元 |
|
[1] |
|
AITD-02004 |
Unlabeled |
未标记 |
|
[1] |
|
AITD-02005 |
Unnormalized Probability Function |
未规范化概率函数 |
|
[1] |
|
AITD-02006 |
Unprojection |
反投影 |
|
[1] |
|
AITD-02007 |
Unshared Convolution |
非共享卷积 |
|
[1] |
|
AITD-02008 |
Unsupervised |
无监督 |
|
[1] |
|
AITD-02009 |
Unsupervised Feature Learning |
无监督特征学习 |
|
[1] |
|
AITD-02010 |
Unsupervised Layer-Wise Training |
无监督逐层训练 |
|
[1] |
|
AITD-02011 |
Unsupervised Learning Algorithm |
无监督学习算法 |
|
[1] |
|
AITD-02012 |
Unsupervised Learning |
无监督学习 |
UL |
[1][2][3] |
|
AITD-02013 |
Unsupervised Pretraining |
无监督预训练 |
|
[1] |
|
AITD-02014 |
Update Gate |
更新门 |
|
[1] |
|
AITD-02015 |
Update Model Parameter |
迭代模型参数 |
|
[1] |
|
AITD-02016 |
Upper Confidence Bounds |
上置信界限 |
|
[1] |
|
AITD-02017 |
Upsampling |
上采样 |
|
[1] |
|
AITD-02018 |
V-Structure |
V型结构 |
|
[1] |
|
AITD-02019 |
Valid |
有效 |
|
[1] |
|
AITD-02020 |
Validation Set |
验证集 |
|
[1] |
|
AITD-02021 |
Validity Index |
有效性指标 |
|
[1] |
|
AITD-02022 |
Value Function |
价值函数 |
|
[1] |
|
AITD-02023 |
Value Function Approximation |
值函数近似 |
|
[1] |
|
AITD-02024 |
Value Iteration |
值迭代 |
|
[1] |
|
AITD-02025 |
Vanishing And Exploding Gradient Problem |
梯度消失与爆炸问题 |
|
[1] |
|
AITD-02026 |
Vanishing Gradient |
梯度消失 |
|
[1] |
|
AITD-02027 |
Vanishing Gradient Problem |
梯度消失问题 |
|
[1] |
|
AITD-02028 |
Vapnik-Chervonenkis Dimension |
VC维 |
|
[1] |
|
AITD-02029 |
Variable Elimination |
变量消去 |
|
[1] |
|
AITD-02030 |
Variance |
方差 |
|
[1] |
|
AITD-02031 |
Variance Reduction |
方差减小 |
|
[1] |
|
AITD-02032 |
Variance Scaling |
方差缩放 |
|
[1] |
|
AITD-02033 |
Variational Autoencoder |
变分自编码器 |
VAE |
[1][2] |
|
AITD-02034 |
Variational Bayesian |
变分贝叶斯 |
|
[1] |
|
AITD-02035 |
Variational Derivative |
变分导数 |
|
[1] |
|
AITD-02036 |
Variational Distribution |
变分分布 |
|
[1] |
|
AITD-02037 |
Variational Dropout |
变分暂退法 |
|
[1] |
|
AITD-02038 |
Variational EM Algorithm |
变分EM算法 |
|
[1] |
|
AITD-02039 |
Variational Free Energy |
变分自由能 |
|
[1] |
|
AITD-02040 |
Variational Inference |
变分推断 |
|
[1] |
|
AITD-02041 |
Vector |
向量 |
|
[1] |
|
AITD-02042 |
Vector Space |
向量空间 |
|
[1] |
|
AITD-02043 |
Vector Space Model |
向量空间模型 |
VSM |
[1] |
|
AITD-02044 |
Vectorization |
向量化 |
|
[1] |
|
AITD-02045 |
Version Space |
版本空间 |
|
[1] |
|
AITD-02046 |
Virtual Adversarial Example |
虚拟对抗样本 |
|
[1] |
|
AITD-02047 |
Virtual Adversarial Training |
虚拟对抗训练 |
|
[1] |
|
AITD-02048 |
Visible Layer |
可见层 |
|
[1] |
|
AITD-02049 |
Visible Variable |
可见变量 |
|
[1] |
|
AITD-02050 |
Viterbi Algorithm |
维特比算法 |
|
[1] |
|
AITD-02051 |
Vocabulary |
词表 |
|
[1] |
|
AITD-02052 |
Von Neumann Architecture |
冯 · 诺伊曼架构 |
|
[1] |
|
AITD-02053 |
Voted Perceptron |
投票感知器 |
|
[1] |
|
AITD-02054 |
Wake Sleep |
醒眠 |
|
[1] |
|
AITD-02055 |
Warp |
线程束 |
|
[1] |
|
AITD-02056 |
Wasserstein Distance |
Wasserstein距离 |
|
[1] |
|
AITD-02057 |
Wasserstein GAN |
Wasserstein生成对抗网络 |
WGAN |
[1] |
|
AITD-02058 |
Weak Classifier |
弱分类器 |
|
[1] |
|
AITD-02059 |
Weak Duality |
弱对偶性 |
|
[1] |
|
AITD-02060 |
Weak Learner |
弱学习器 |
|
[1] |
|
AITD-02061 |
Weakly Learnable |
弱可学习 |
|
[1] |
|
AITD-02062 |
Weakly Supervised Learning |
弱监督学习 |
|
[1] |
|
AITD-02063 |
Weight |
权重 |
|
[1][2][3] |
|
AITD-02064 |
Weight Decay |
权重衰减 |
|
[1] |
|
AITD-02065 |
Weight Normalization |
权重规范化 |
|
[1] |
|
AITD-02066 |
Weight Scaling Inference Rule |
权重比例推断规则 |
|
[1] |
|
AITD-02067 |
Weight Sharing |
权共享 |
|
[1] |
|
AITD-02068 |
Weight Space Symmetry |
权重空间对称性 |
|
[1] |
|
AITD-02069 |
Weight Vector |
权值向量 |
|
[1] |
|
AITD-02070 |
Weighted Distance |
加权距离 |
|
[1] |
|
AITD-02071 |
Weighted Voting |
加权投票 |
|
[1] |
|
AITD-02072 |
Whitening |
白化 |
|
[1] |
|
AITD-02073 |
Wide Convolution |
宽卷积 |
|
[1] |
|
AITD-02074 |
Width |
宽度 |
|
[1] |
|
AITD-02075 |
Winner-Take-All |
胜者通吃 |
|
[1] |
|
AITD-02076 |
Within-Class Scatter Matrix |
类内散度矩阵 |
|
[1] |
|
AITD-02077 |
Word Embedding |
词嵌入 |
|
[1][2] |
|
AITD-02078 |
Word Sense Disambiguation |
词义消歧 |
|
[1] |
|
AITD-02079 |
Word Vector |
词向量 |
|
[1] |
|
AITD-02080 |
Word Vector Space Model |
单词向量空间模型 |
|
[1] |
|
AITD-02081 |
Word-Document Matrix |
单词-文本矩阵 |
|
[1] |
|
AITD-02082 |
Word-Topic Matrix |
单词-话题矩阵 |
|
[1] |
|
AITD-02083 |
Working Memory |
工作记忆 |
|
[1] |
|
AITD-02084 |
Wrapper Method |
包裹式方法 |
|
[1] |
|
AITD-02085 |
Z-Score Normalization |
Z值规范化 |
|
[1] |
|
AITD-02086 |
Zero Mean |
零均值 |
|
[1] |
|
AITD-02087 |
Zero Padding |
零填充 |
|
[1] |
|
AITD-02088 |
Zero Tensor |
零张量 |
|
[1] |
|
AITD-02089 |
Zero-Centered |
零中心化的 |
|
[1] |
|
AITD-02090 |
Zero-Data Learning |
零数据学习 |
|
[1] |
|
AITD-02091 |
Zero-Shot Learning |
零试学习 |
|
[1] |
|
AITD-02092 |
Zipf's Law |
齐普夫定律 |
|
[1] |
|
AITD-02093 |
ε-Greedy Method |
ε-贪心法 |
|
[1] |
|
AITD-02094 |
2D Qsar Models |
二维定量构效关系模型 |
|
[1] |
化学 |
AITD-02095 |
3D Cartesian |
三维笛卡尔(坐标) |
|
[1] |
数学 |
AITD-02096 |
3D Conformation |
三维构象 |
|
[1] |
化学、生化 |
AITD-02097 |
3D Grids |
三维(坐标)网格 |
|
[1] |
|
AITD-02098 |
3D Qsar Models |
三维定量构效关系模型 |
|
[1] |
化学 |
AITD-02099 |
Aberration-Corrected |
像差矫正 |
|
[1] |
物理 |
AITD-02100 |
Active Machine Learning |
主动机器学习 |
|
[1] |
机器学习 |
AITD-02101 |
Adaptive Fuzzy Neural Network |
自适应模糊神经网络 |
|
[1] |
机器学习 |
AITD-02102 |
Adaptive Sampling |
自适应采样 |
|
[1] |
机器学习 |
AITD-02103 |
Admet Evaluation |
毒性评估 |
|
[1] |
化学 |
AITD-02104 |
Alexnet |
AlexNet |
|
[1] |
机器学习 |
AITD-02105 |
Alphago |
阿尔法狗 |
|
[1][2] |
机器学习 |
AITD-02106 |
Adaptive Neuro Fuzzy Inference System |
自适应神经模糊推理系统 |
ANFIS |
[1] |
机器学习 |
AITD-02107 |
Approximate Probabilistic Models |
近似概率模型 |
|
[1] |
机器学习 |
AITD-02108 |
Artificial Neurons |
人工神经元 |
|
[1] |
机器学习 |
AITD-02109 |
Artificial Synapses |
人工突触 |
|
[1] |
机器学习 |
AITD-02110 |
Attention-Based |
基于注意力(机制)的 |
|
[1] |
机器学习 |
AITD-02111 |
Automating Synthetic Planning |
自动化综合规划 |
|
[1] |
机器学习 |
AITD-02112 |
Automation |
自动化 |
|
[1] |
机器学习 |
AITD-02113 |
Autonomous Decision-Making |
自主决策 |
|
[1] |
机器学习 |
AITD-02114 |
B-Clustering Algorithms |
B树聚类算法 |
|
[1] |
机器学习 |
AITD-02115 |
Balanced Accuracy |
平衡精度 |
|
[1] |
机器学习 |
AITD-02116 |
Bandgap Energy |
带隙能量 |
|
[1] |
物理 |
AITD-02117 |
Baseline Test |
基准测试 |
|
[1] |
机器学习 |
AITD-02118 |
Basin Hopping |
盆地跳跃(算法) |
|
[1] |
机器学习 |
AITD-02119 |
Bayesian Approach |
贝叶斯方法 |
|
[1] |
统计,机器学习 |
AITD-02120 |
Bayesian Induction |
贝叶斯归纳 |
|
[1] |
统计,机器学习 |
AITD-02121 |
Bayesian Mcmc Methods |
贝叶斯马尔可夫链蒙特卡洛方法 |
|
[1] |
统计,机器学习 |
AITD-02122 |
Bayesian Methods |
贝叶斯方法 |
|
[1] |
统计,机器学习 |
AITD-02123 |
Bayesian Molecular |
贝叶斯分子(设计方法) |
|
[1] |
统计,机器学习,化学 |
AITD-02124 |
Bayesian Prior |
贝叶斯先验 |
|
[1] |
统计,机器学习 |
AITD-02125 |
Bayesian Program Learning |
贝叶斯程序学习 |
BPL |
[1] |
统计,机器学习 |
AITD-02126 |
Bayesian Regularized Neural Network |
贝叶斯正则化神经网络 |
|
[1] |
统计,机器学习 |
AITD-02127 |
Beam-Scanning |
波束扫描 |
|
[1] |
物理 |
AITD-02128 |
Best Separates |
最优分离 |
|
[1] |
机器学习 |
AITD-02129 |
Biased Dataset |
有偏数据集 |
|
[1] |
机器学习 |
AITD-02130 |
Bit Collisions |
字节碰撞/冲突 |
|
[1] |
数据库 |
AITD-02131 |
Black Box |
黑盒子 |
|
[1] |
|
AITD-02132 |
Black-Box Attack |
黑盒攻击 |
|
[1] |
|
AITD-02133 |
Bonding Environments |
成键环境 |
|
[1] |
|
AITD-02134 |
Bonferroni Correction |
邦弗朗尼校正 |
|
[1] |
统计 |
AITD-02135 |
Bootstrap Aggregation |
引导聚合 |
bagging |
[1] |
机器学习 |
AITD-02136 |
Broyden–Fletcher–Goldfarb–Shanno |
BFGS(算法) |
BFGS |
[1] |
一种拟牛顿法,数学计算 |
AITD-02137 |
Buchwald−Hartwig Cross-Coupling |
Buchwald–Hartwig 偶联(反应) |
|
[1] |
化学 |
AITD-02138 |
C4.5 Algorithm |
C4.5 算法 |
|
[1] |
一种决策树算法,数据挖掘 |
AITD-02139 |
Calculation Uncertainties |
计算不确定性 |
|
[1] |
|
AITD-02140 |
Canonical Ml Methods |
经典机器学习方法 |
|
[1] |
|
AITD-02141 |
Cartesian Distance Vector |
笛卡尔距离向量 |
|
[1] |
|
AITD-02142 |
CASP |
国际蛋白质结构预测竞赛 |
|
[1] |
生物 |
AITD-02143 |
Categorical Data |
分类数据 |
|
[1] |
|
AITD-02144 |
Categorization Algorithms |
分类算法 |
|
[1] |
|
AITD-02145 |
ChemDataExtractor |
化学数据提取器 |
CDE |
[1] |
|
AITD-02146 |
Chi-Squared |
卡方(分布) |
|
[1] |
|
AITD-02147 |
Classification Model |
分类模型 |
|
[1][2] |
|
AITD-02148 |
Cluster Resolution Feature Selection |
聚类分辨率特征选择 |
CR-FS |
[1] |
|
AITD-02149 |
Cluster-Based Splitting |
基于聚类的分离方法 |
|
[1] |
|
AITD-02150 |
Clustering Methods |
聚类方法 |
|
[1] |
|
AITD-02151 |
Code Pipeline |
代码流水线 |
|
[1] |
|
AITD-02152 |
Coefficient of Determination |
决定系数 |
r^2 or R^2 |
[1] |
统计 |
AITD-02153 |
Combined Gradient |
组合梯度(算法) |
|
[1] |
机器学习 |
AITD-02154 |
Complex Data |
复合数据 |
|
[1] |
|
AITD-02155 |
Computational Cost |
计算成本 |
|
[1] |
|
AITD-02156 |
Computational Optimisation |
计算优化 |
|
[1] |
|
AITD-02157 |
Computational Science |
计算科学 |
|
[1] |
|
AITD-02158 |
Computational Toxicology |
计算毒理学 |
|
[1] |
|
AITD-02159 |
Computer Science |
计算机科学 |
|
[1] |
|
AITD-02160 |
Computer Simulations |
计算机模拟 |
|
[1] |
|
AITD-02161 |
Computer-Aided |
计算机辅助 |
|
[1] |
|
AITD-02162 |
Constraint |
约束 |
|
[1] |
|
AITD-02163 |
Core-Loss Spectrum |
(电子能量损失谱中的)高能区域 |
|
[1] |
|
AITD-02164 |
Coulomb Matrix |
库仑矩阵 |
|
[1] |
|
AITD-02165 |
Coupled-Cluster Predictions |
耦合簇预测 |
|
[1] |
|
AITD-02166 |
Cross-Validated Coefficient of Determination |
交叉验证的决定系数 |
q^2 or Q^2 |
[1] |
|
AITD-02167 |
Cross-Validation |
交叉验证 |
CV |
[1][2][3] |
|
AITD-02168 |
Crowd-Sourcing |
众包 |
|
[1] |
商业模式 |
AITD-02169 |
Cut-Points |
切点 |
|
[1] |
|
AITD-02170 |
Cutoff Radial Function |
截断径向函数 |
|
[1] |
|
AITD-02171 |
Data Availability |
数据可用性 |
|
[1][2] |
|
AITD-02172 |
Data Cleaning |
数据清洗 |
|
[1][2] |
|
AITD-02173 |
Data Collection |
数据采集 |
|
[1][2][3] |
|
AITD-02174 |
Data Considerations |
数据注意事项 |
|
[1] |
|
AITD-02175 |
Data Curation |
数据监管 |
|
[1] |
|
AITD-02176 |
Data Disparity |
数据差异 |
|
[1] |
|
AITD-02177 |
Data Dredging |
数据挖掘 |
|
[1] |
|
AITD-02178 |
Data Imputation |
数据填补 |
|
[1] |
|
AITD-02179 |
Data Labels |
数据标签 |
|
[1] |
|
AITD-02180 |
Data Leakage |
数据泄露 |
|
[1] |
|
AITD-02181 |
Data Pre-Processing |
数据预处理 |
|
[1] |
|
AITD-02182 |
Data Processing |
数据处理 |
|
[1] |
|
AITD-02183 |
Data Quality |
数据质量 |
|
[1][2] |
|
AITD-02184 |
Data Reduction |
数据缩减 |
|
[1][2] |
|
AITD-02185 |
Data Representation |
数据表示 |
|
[1][2] |
|
AITD-02186 |
Data Selection |
数据选择 |
|
[1] |
|
AITD-02187 |
Data Sources |
数据源 |
|
[1] |
|
AITD-02188 |
Data Splitting |
数据拆分 |
|
[1] |
|
AITD-02189 |
Data Transformation |
数据转换 |
|
[1] |
|
AITD-02190 |
Data-Driven |
数据驱动 |
|
[1][2] |
|
AITD-02191 |
Data-Driven Decision-Making |
数据驱动的决策 |
|
[1] |
|
AITD-02192 |
Data-Driven Methods |
数据驱动的方法 |
|
[1] |
|
AITD-02193 |
Data-Driven Spectral Analysis |
数据驱动的光谱分析 |
|
[1] |
|
AITD-02194 |
Data-Mining |
数据挖掘 |
|
[1] |
|
AITD-02195 |
Database |
数据库 |
|
[1] |
|
AITD-02196 |
DE Algorithm |
差分进化算法 |
|
[1] |
|
AITD-02197 |
Deeplift |
DeepLift模型 |
|
[1] |
|
AITD-02198 |
Dendrogram |
树状图 |
|
[1] |
|
AITD-02199 |
Density Functional Theory |
密度泛函理论 |
DFT |
[1][2][3][4][5][6] |
|
AITD-02200 |
Density-Based Spatial Clustering Of Applications With Noise |
DBSCAN密度聚类 |
DBSCAN |
[1] |
|
AITD-02201 |
Descriptor |
描述符 |
|
[1] |
|
AITD-02202 |
DFT Calculations |
DFT计算 |
|
[1] |
|
AITD-02203 |
Dice Similarity |
戴斯相似度 |
|
[1] |
|
AITD-02204 |
Differential Evolution |
差分进化 |
DE |
[1] |
|
AITD-02205 |
Dimensionality Reduction |
降维 |
|
[1] |
|
AITD-02206 |
Direct Neural Network Modeling |
正向神经网络建模 |
|
[1] |
|
AITD-02207 |
Discrete Manner |
离散方式 |
|
[1] |
|
AITD-02208 |
Discrete Quanta |
离散量子 |
|
[1] |
|
AITD-02209 |
Discretization |
离散化 |
|
[1] |
|
AITD-02210 |
Distillation |
蒸馏 |
|
[1] |
|
AITD-02211 |
Dynamic Datasets |
动态数据集 |
|
[1] |
|
AITD-02212 |
Dynamic Filter Networks |
动态过滤网络 |
|
[1] |
|
AITD-02213 |
Dynamic Sampling |
动态采样 |
|
[1] |
|
AITD-02214 |
Dynamics Simulations |
动力学模拟 |
|
[1] |
|
AITD-02215 |
Eigenfunction |
特征函数 |
|
[1] |
|
AITD-02216 |
Electronegativity |
电负性 |
|
[1] |
|
AITD-02217 |
Elman |
埃尔曼 |
|
[1] |
|
AITD-02218 |
Empirical Models |
经验模型 |
|
[1] |
|
AITD-02219 |
Energy Derivatives |
能源衍生品 |
|
[1] |
在DP模型中:能量的导数 |
AITD-02220 |
Energy Potentials |
能量潜力 |
|
[1] |
|
AITD-02221 |
Ensemble Methods |
集成方法 |
|
[1][2] |
|
AITD-02222 |
Entity Normalisation |
实体规范化 |
|
[1] |
|
AITD-02223 |
Ethical Considerations |
道德考虑 |
|
[1] |
|
AITD-02224 |
Euclidean Distances |
欧几里得距离 |
|
[1] |
|
AITD-02225 |
Evolutionary Algorithms |
进化算法 |
EA |
[1][2] |
|
AITD-02226 |
Evolutionary Method |
进化方法 |
|
[1] |
|
AITD-02227 |
Exchange–Correlation |
交换关联(的能量/泛函) |
|
[1] |
|
AITD-02228 |
Excited-State Potentials |
激发态能量 |
|
[1] |
|
AITD-02229 |
Expected Reduction In Distortion |
符合预期的失真减少 |
ERD |
[1] |
|
AITD-02230 |
Experimental Validation Data |
实验验证数据 |
|
[1] |
|
AITD-02231 |
Expert Systems |
专家系统 |
ESS |
[1] |
|
AITD-02232 |
Extended-Connectivity Circular Fingerprint |
扩展连接环形指纹 |
ECFP |
[1] |
|
AITD-02233 |
Extraction Techniques |
提取技术 |
|
[1] |
|
AITD-02234 |
Faber-Christensen-Huang-Lilienfeld |
Faber-Christensen-Huang-Lilienfeld |
FCHL |
[1] |
四个人提出的化学结构量子机器学习方法 |
AITD-02235 |
Facial Recognition |
面部识别 |
|
[1] |
|
AITD-02236 |
FAIR Data Principles |
FAIR数据原则 |
|
[1] |
Findability可找寻 Accessibility可访问 Interoperability可交互 Reuse可再用 |
AITD-02237 |
False Negatives |
假阴性 |
FNs |
[1] |
|
AITD-02238 |
False Positives |
假阳性 |
FPs |
[1] |
|
AITD-02239 |
Fchl Representation |
Fchl 表示 |
|
[1] |
|
AITD-02240 |
Feature Binarization |
特征二值化 |
|
[1] |
|
AITD-02241 |
Feature Transform |
特征变换 |
|
[1] |
|
AITD-02242 |
Feature Vectors |
特征向量 |
|
[1] |
|
AITD-02243 |
Features |
特征 |
|
[1] |
|
AITD-02244 |
Feed Back |
反馈 |
|
[1] |
|
AITD-02245 |
Feed-Forward Neural Networks |
前馈神经网络 |
FFNN |
[1][2][3] |
|
AITD-02246 |
Feedback Structure |
反馈结构 |
|
[1] |
|
AITD-02247 |
Final Evaluation |
最终评估 |
|
[1] |
|
AITD-02248 |
Findable, Accessible, Interoperable, Reusable |
可查找、可访问、可互操作、可重用 |
FAIR |
[1] |
|
AITD-02249 |
First-Principles |
第一性原理 |
|
[1] |
|
AITD-02250 |
Flow Rate |
流速 |
|
[1] |
|
AITD-02251 |
Forward Cross-Validation |
前向交叉验证 |
|
[1] |
|
AITD-02252 |
Forward Prediction |
前向预测 |
|
[1] |
|
AITD-02253 |
Forward Reaction Prediction |
前向反应预测 |
|
[1] |
|
AITD-02254 |
Fuzzy Logic |
模糊逻辑 |
FL |
[1] |
|
AITD-02255 |
Fuzzy Neural Networks |
模糊神经网络 |
FNN |
[1] |
|
AITD-02256 |
Ga-Based Approaches |
基于遗传算法的方法 |
|
[1] |
|
AITD-02257 |
Garbage In, Garbage Out |
无用数据入、无用数据出 |
GIGO |
[1] |
|
AITD-02258 |
Gas-Phase Networks |
气相网络 |
|
[1] |
|
AITD-02259 |
Gaussian Kernels |
高斯核 |
|
[1] |
|
AITD-02260 |
Gaussian-Type Structure Descriptors |
高斯型结构描述符 |
GTSD |
[1] |
|
AITD-02261 |
General Intelligence |
通用智能 |
GI |
[1] |
|
AITD-02262 |
Generalized Gradient Approximation |
广义梯度近似 |
GGA |
[1] |
|
AITD-02263 |
Generative Adversarial Networks |
生成对抗网络 |
GAN |
[1][2] |
机器学习 |
AITD-02264 |
Gradient Boosting Decision Tree |
梯度提升决策树 |
GBDT |
[1] |
|
AITD-02265 |
Gradient-Based |
基于梯度的 |
|
[1] |
|
AITD-02266 |
Grain-Surface Networks |
粒面网络 |
|
[1] |
|
AITD-02267 |
Graph Convolutional |
图卷积 |
GC |
[1] |
|
AITD-02268 |
Graph Models |
图模型 |
|
[1] |
|
AITD-02269 |
Graph Neural Networks |
图神经网络 |
GNNS |
[1] |
|
AITD-02270 |
Graph-Based |
基于图形 |
|
[1] |
|
AITD-02271 |
Graph-Based Models |
基于图的模型 |
|
[1] |
|
AITD-02272 |
Graph-Based Neural Networks |
基于图的神经网络 |
|
[1] |
|
AITD-02273 |
Graph-Based Representation |
基于图的表示 |
GB-GA |
[1][2] |
|
AITD-02274 |
Graph-Convolutional Neural Network |
图卷积神经网络 |
|
[1][2] |
|
AITD-02275 |
Graphics Processing Units |
图形处理器 |
|
[1] |
|
AITD-02276 |
Gravimetric Polymerization Degree |
比重聚合度 |
|
[1] |
|
AITD-02277 |
Hamiltonian Matrix |
哈密顿矩阵 |
|
[1] |
物理 |
AITD-02278 |
Hamiltonian Operator |
哈密顿算符 |
|
[1] |
物理 |
AITD-02279 |
Heterogeneous Data |
异构数据 |
|
[1][2] |
|
AITD-02280 |
Hidden Layers |
隐藏层 |
|
[1] |
|
AITD-02281 |
High Data Throughput |
高数据吞吐量 |
|
[1] |
|
AITD-02282 |
High Throughput |
高通量 |
HT |
[1] |
|
AITD-02283 |
High Throughput Screening |
高通量筛选 |
HTS |
[1] |
|
AITD-02284 |
High Variance Models |
高方差模型 |
|
[1] |
|
AITD-02285 |
High-Dimensional Data |
高维数据 |
|
[1] |
|
AITD-02286 |
High-Dimensional NN |
高维神经网络 |
HDNN |
[1] |
|
AITD-02287 |
High-Dimensional Objects |
高维对象 |
|
[1] |
|
AITD-02288 |
High-Throughput |
高通量 |
|
[1] |
|
AITD-02289 |
Higher-Dimensional Space |
高维空间 |
|
[1] |
数学 |
AITD-02290 |
Higher-Dimensional Spectral Space |
高维光谱空间 |
|
[1] |
|
AITD-02291 |
Homogenization |
同质化 |
|
[1] |
|
AITD-02292 |
Homomorphic Encryption |
同态加密 |
|
[1] |
|
AITD-02293 |
Human Face Recognition |
人脸识别 |
|
[1] |
机器学习 |
AITD-02294 |
Human-Encoded |
人工编码的 |
|
[1] |
|
AITD-02295 |
Hybrid Model |
混合模型 |
|
[1] |
|
AITD-02296 |
Hybrid Technique |
混合技术 |
HM |
[1] |
|
AITD-02297 |
Hybrid-Neural Model |
混合神经模型 |
|
[1] |
|
AITD-02298 |
Hyperparameter Opimization |
超参数优化 |
|
[1] |
|
AITD-02299 |
Hyperparameters |
超参数 |
|
[1][2][3] |
机器学习 |
AITD-02300 |
Hyperplanes Separate |
超平面分离 |
|
[1] |
|
AITD-02301 |
Id3 Algorithm |
Id3 算法 |
|
[1] |
|
AITD-02302 |
Image And Speech Recognition |
图像和语音识别 |
|
[1] |
|
AITD-02303 |
Image Classification |
图像分类 |
|
[1] |
|
AITD-02304 |
Image Classifier |
图像分类器 |
|
[1] |
|
AITD-02305 |
Image Recognition |
图像识别 |
|
[1] |
机器学习 |
AITD-02306 |
Informative Priors |
信息先验 |
|
[1] |
|
AITD-02307 |
Input-Output Pairs |
输入输出对 |
|
[1] |
|
AITD-02308 |
Instance-Based |
基于实例的 |
|
[1] |
|
AITD-02309 |
Intelligent Machine |
智能机器 |
|
[1] |
|
AITD-02310 |
Intermediate Neurons |
中间神经元 |
|
[1] |
机器学习 |
AITD-02311 |
Internet Of Things |
物联网 |
IoT |
[1] |
|
AITD-02312 |
Interpolation Coordinate |
插值坐标 |
|
[1] |
|
AITD-02313 |
Interpretability |
可解释性 |
|
[1] |
|
AITD-02314 |
Inverse Neural Modeling |
逆神经建模 |
INN |
[1] |
|
AITD-02315 |
Inverse Neural Network Modeling |
逆神经网络建模 |
|
[1] |
|
AITD-02316 |
Iterative Learning |
迭代学习 |
|
[1] |
|
AITD-02317 |
Joint Distribution |
联合分布 |
|
[1] |
|
AITD-02318 |
Jordan-Elman Neural Networks |
Jordan-Elman 神经网络 |
|
[1] |
|
AITD-02319 |
K Clusters |
K聚类 |
|
[1] |
|
AITD-02320 |
K Nearest Points |
K 最近点 |
|
[1] |
统计 |
AITD-02321 |
K-1 Folds |
K-1 折 |
|
[1] |
|
AITD-02322 |
K-Edge (O-K Edge) |
K-边缘(O-K 边缘) |
|
[1] |
|
AITD-02323 |
K-Means |
K-均值 |
|
[1] |
统计 |
AITD-02324 |
Kendall’S Tau |
肯德尔等级相关系数 |
|
[1] |
|
AITD-02325 |
Kernel Ridge Regression |
核岭回归 |
KRR |
[1][2][3] |
|
AITD-02326 |
Kernels |
内核 |
|
[1] |
|
AITD-02327 |
Kinetic Curve |
动力学曲线 |
|
[1] |
|
AITD-02328 |
KNN Model |
K 近邻模型 |
|
[1] |
|
AITD-02329 |
Knowledge Extraction |
知识提取 |
|
[1] |
|
AITD-02330 |
Knowledge Gradient |
知识梯度 |
KG |
[1] |
|
AITD-02331 |
L1 And L2 Regularization |
L1与L2正则化 |
|
[1] |
|
AITD-02332 |
Laboratory Level |
实验室级别 |
|
[1] |
|
AITD-02333 |
Language Processing |
语言处理 |
|
[1] |
|
AITD-02334 |
Laplacian Prior |
拉普拉斯先验 |
|
[1] |
|
AITD-02335 |
Large-Scale Data Storage |
大规模数据存储 |
|
[1] |
|
AITD-02336 |
Lasers |
激光器 |
|
[1] |
|
AITD-02337 |
Lasso Regression |
拉索回归 |
|
[1] |
|
AITD-02338 |
LBP |
局部二值模式 |
|
[1] |
|
AITD-02339 |
Least Absolute Shrinkage And Selection Operator |
Lasso回归 |
LASSO |
[1] |
|
AITD-02340 |
Least Square Support Vector Machine |
最小二乘支持向量机 |
LSSVM |
[1] |
|
AITD-02341 |
Ligand-Field |
配位场 |
|
[1] |
|
AITD-02342 |
Linear |
线性的 |
|
[1][2] |
数学 |
AITD-02343 |
Linear Dimension Reduction Methods |
线性降维方法 |
|
[1] |
|
AITD-02344 |
Linear Vibronic Coupling Model |
线性振子耦合模型 |
|
[1] |
|
AITD-02345 |
Local Recurrent |
本地卷积 |
|
[1] |
|
AITD-02346 |
Logic And Heuristics Applied To Synthetic Analysis |
LHASA 程序 |
LHASA |
[1] |
|
AITD-02347 |
Long-Range Prediction |
长期预测 |
|
[1] |
|
AITD-02348 |
Long-Range Prediction Models |
长期预测模型 |
|
[1] |
|
AITD-02349 |
Long-Term Planning |
长期规划 |
|
[1] |
|
AITD-02350 |
Long-Term Reward |
长期回报 |
|
[1] |
|
AITD-02351 |
Machine-Readable Data |
机器可读的数据 |
|
[1] |
|
AITD-02352 |
Mae |
平均绝对误差 |
MAE |
[1] |
|
AITD-02353 |
Mahalanobis Distances |
马氏距离 |
|
[1] |
统计 |
AITD-02354 |
Matrices |
矩阵 |
|
[1] |
数学 |
AITD-02355 |
Matthews Correlation Coefficient |
马修斯相关系数 |
MCC |
[1] |
|
AITD-02356 |
Maximum Likelihood Methods |
最大似然法 |
|
[1] |
统计 |
AITD-02357 |
Maximum Likelihood Procedures |
最大似然估计法 |
|
[1] |
统计 |
AITD-02358 |
MCTS Method |
蒙特卡洛树搜索方法 |
|
[1] |
|
AITD-02359 |
Mean-Squared Error |
均方误差 |
|
[1] |
统计、机器学习 |
AITD-02360 |
Mechanical Sympathy |
机械同感,软硬件协同编程 |
|
[1] |
|
AITD-02361 |
Merging |
合并 |
|
[1] |
|
AITD-02362 |
Message Passing Neural Networks |
消息传递神经网络 |
MPNNS |
[1] |
|
AITD-02363 |
Microarray Data |
微阵列数据 |
|
[1] |
|
AITD-02364 |
Mini Batch |
小批次 |
|
[1] |
|
AITD-02365 |
Mining |
挖掘 |
|
[1] |
|
AITD-02366 |
Mining Out |
挖掘 |
|
[1] |
|
AITD-02367 |
Missing Values |
缺失值 |
|
[1] |
统计 |
AITD-02368 |
ML Algorithm |
机器学习算法 |
|
[1] |
|
AITD-02369 |
ML Modelling |
机器学习建模 |
|
[1] |
|
AITD-02370 |
ML Potentials |
机器学习势能 |
|
[1] |
|
AITD-02371 |
ML-Driven |
机器学习驱动的 |
|
[1] |
|
AITD-02372 |
ML-Driven Optimization |
机器学习驱动的最优化 |
|
[1] |
|
AITD-02373 |
MLP Neural Model |
多层感知机神经模型 |
|
[1] |
|
AITD-02374 |
Model Construction |
模型构建 |
|
[1] |
|
AITD-02375 |
Model Evaluation |
模型评估 |
|
[1] |
|
AITD-02376 |
Model Performance |
模型性能 |
|
[1] |
|
AITD-02377 |
Model Statistics |
模型统计 |
|
[1] |
|
AITD-02378 |
Model Training |
模型训练 |
|
[1] |
机器学习 |
AITD-02379 |
Model Validation |
模型验证 |
|
[1] |
|
AITD-02380 |
Model-Based Iterative Reconstruction |
基于模型的迭代重建 |
MBIR |
[1] |
|
AITD-02381 |
Model-Construction |
模型构建 |
|
[1] |
|
AITD-02382 |
Modelling Scenario |
建模场景 |
|
[1] |
|
AITD-02383 |
Molecular Graph Theory |
分子图论 |
|
[1] |
|
AITD-02384 |
Molecular Modelling |
分子建模 |
|
[1] |
|
AITD-02385 |
Monte Carlo Tree Search |
蒙特卡洛树搜索 |
MCTS |
[1][2][3] |
数学 |
AITD-02386 |
Moore’S Law |
摩尔定律 |
|
[1] |
计算机 |
AITD-02387 |
ms-QSBER-EL Model |
基于人工神经网络组合的结构生物学效应定量关系多尺度模型 |
|
[1] |
|
AITD-02388 |
Multi-Agent Control System |
多智能体控制系统 |
|
[1] |
|
AITD-02389 |
Multi-Core Desktop Computer |
多核台式计算机 |
|
[1] |
计算机 |
AITD-02390 |
Multi-Dimensional Big Data Analysis |
多维度大数据分析 |
|
[1] |
|
AITD-02391 |
Multi-Layer Feed-Forward |
多层前馈 |
MLFF |
[1] |
|
AITD-02392 |
Multi-Objective Genetic Algorithm |
多目标遗传算法 |
MOGA |
[1] |
|
AITD-02393 |
Multi-Objective Optimization |
多目标优化 |
|
[1] |
机器学习 |
AITD-02394 |
Multi-Reaction Synthesis |
多反应合成 |
|
[1] |
|
AITD-02395 |
Multilayer Perceptron |
多层感知机 |
|
[1] |
|
AITD-02396 |
Multivariate Regression |
多变量回归 |
|
[1] |
|
AITD-02397 |
N-Dimensional Space |
N维空间 |
|
[1] |
|
AITD-02398 |
Naive Bayesian |
朴素贝叶斯 |
|
[1] |
统计 |
AITD-02399 |
Naive Bayesian Methods |
朴素贝叶斯方法 |
|
[1] |
统计 |
AITD-02400 |
Named Entity Recognition,NER |
命名实体识别 |
NER |
[1] |
|
AITD-02401 |
Nearest Neighbors |
近邻 |
|
[1] |
|
AITD-02402 |
Nearest Neighbour Model |
近邻模型 |
|
[1] |
|
AITD-02403 |
Negative Predictive Value |
阴性预测值 |
NPV |
[1] |
|
AITD-02404 |
Network Architecture |
网络结构 |
|
[1] |
机器学习 |
AITD-02405 |
Network Geometry |
网络几何 |
|
[1] |
|
AITD-02406 |
Neural Turing Machines |
神经图灵机 |
NTM |
[1][2] |
|
AITD-02407 |
Neural-Network-Based Function |
基于神经网络的函数 |
|
[1] |
|
AITD-02408 |
Neurons |
神经元 |
|
[1] |
机器学习 |
AITD-02409 |
Nuclear Magnetic Resonance |
核磁共振 |
NMR |
[1] |
|
AITD-02410 |
Noise Filters |
噪声过滤器 |
|
[1] |
|
AITD-02411 |
Noise-Free |
无噪的 |
|
[1] |
|
AITD-02412 |
Non-Linear |
非线性 |
|
[1] |
数学、统计 |
AITD-02413 |
Non-Linear Correlation |
非线性相关 |
|
[1] |
统计 |
AITD-02414 |
Non-Linearity |
非线性 |
|
[1] |
|
AITD-02415 |
Non-Parametric Algorithm |
非参数化学习算法 |
|
[1] |
|
AITD-02416 |
Non-Safety-Critical Applications |
非安全关键型应用 |
|
[1] |
|
AITD-02417 |
Non-Steady-State |
非稳态 |
|
[1] |
|
AITD-02418 |
Non-Stochastic |
非随机的 |
|
[1] |
|
AITD-02419 |
Non-Template |
非模板 |
|
[1] |
|
AITD-02420 |
Non-Template Methods |
非模板方法 |
|
[1] |
|
AITD-02421 |
Non-Zero Weight |
非零权重 |
|
[1] |
|
AITD-02422 |
On-The-Fly Optimization |
运行中优化 |
|
[1] |
计算机 |
AITD-02423 |
One-Hot Vector |
独热向量 |
|
[1] |
整个矢量中之后一个数为1 其余为0 |
AITD-02424 |
Open-Source |
开源 |
|
[1] |
软件工程 |
AITD-02425 |
Open-Source Dataset |
开源数据集 |
|
[1] |
机器学习 |
AITD-02426 |
Predicted Label |
预测值 |
|
[1] |
机器学习 |
AITD-02427 |
Prediction |
预测 |
|
[1] |
机器学习 |
AITD-02428 |
Prediction Accuracy |
预测准确率 |
|
[1] |
机器学习 |
AITD-02429 |
Predictor |
预测器/决策函数 |
|
[1] |
机器学习 |
AITD-02430 |
Protein Folding |
蛋白折叠 |
|
[1][2] |
生物 |
AITD-02431 |
Quantum Chemistry |
量子化学 |
|
[1] |
化学 |
AITD-02432 |
Quantum Theory |
量子理论 |
|
[1] |
物理 |
AITD-02433 |
Random Selection |
随机选择 |
|
[1] |
统计 |
AITD-02434 |
Raw Datasets |
原始数据集 |
|
[1] |
机器学习 |
AITD-02435 |
Root Mean Square Errors |
均方根 |
RMSE |
[1] |
统计 |
AITD-02436 |
Scaling |
缩放 |
|
[1] |
图像处理 |
AITD-02437 |
Simulation |
仿真 |
|
[1] |
|
AITD-02438 |
The Global Minimum |
全局最小值 |
|
[1] |
机器学习 |
AITD-02439 |
Turing Test |
图灵测试 |
|
[1] |
AI,CS |
AITD-02440 |
Version Control |
版本控制 |
|
[1] |
|
AITD-02441 |
Workflow |
工作流 |
|
[1] |
|
AITD-02442 |
Sequence-Function |
序列-功能 |
|
[1] |
|
AITD-02443 |
Hugging Face |
Hugging Face |
|
|
|
AITD-02444 |
Mask-filling |
完形填空 |
|
|
|
AITD-02444 |
mask token |
掩码标记 |
|
|
|