Library implementing state-of-the-art Concept-based and Disentanglement Learning methods for Explainable AI
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Updated
Aug 17, 2022 - Python
Library implementing state-of-the-art Concept-based and Disentanglement Learning methods for Explainable AI
Awesome PrivEx: Privacy-Preserving Explainable AI (PPXAI)
Keras 101: A simple Neural Network for House Pricing regression
MEME: Generating RNN Model Explanations via Model Extraction
Model explanation provides the ability to interpret the effect of the predictors on the composition of an individual score.
The Codebase for Causal Proxy Model
A School for All Seasons on Trustworthy Machine Learning
Replacing a Black-box model by a Global Single Tree Approximation
Python implementation of the goldeneye algorithm to investigate how classifiers utilise the structure of a dataset.
💷 💵 💶 A ML project containing web scraping, NLP and some classification model evaluation
主要包含ModelHelper和NLPHelper,其中ModelHelper主要有特征选择、超参数搜索、模型解释和模型融合等,NLPHelper则是进一步封装了NLP一些常用的操作,常用的网络结构以及几个NLP的任务
Predicting Churn Probability for Telecom Customers and deploying the prediction model with Streamlit
Code for "High-Precision Model-Agnostic Explanations" paper. A follow up to LIME model.
A proof-of-concept on how to install and use Torchserve in various mode
Modeling Plasmodium falciparum Diagnostic Test Sensitivity using Machine Learning with Histidine-Rich Protein 2 Variants
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