[NeurIPS 2023] Multi-fidelity hyperparameter optimization with deep power laws that achieves state-of-the-art results across diverse benchmarks.
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Updated
Oct 16, 2024 - Python
[NeurIPS 2023] Multi-fidelity hyperparameter optimization with deep power laws that achieves state-of-the-art results across diverse benchmarks.
Scaling laws web calculator to get a model's training compute flops, costs and energy utilization.
🌹[ICML 2024] Selecting Large Language Model to Fine-tune via Rectified Scaling Law
Code for CoNLL BabyLM workshop Mini Minds: Exploring Bebeshka and Zlata Baby Models
First temporal graph foundation model dataset and benchmark
A method for calculating scaling laws for LLMs from publicly available models
code for Scaling Laws for Language Transfer Learning
Official code for the paper, "Scaling Offline Model-Based RL via Jointly-Optimized World-Action Model Pretraining"
[ICML 2023] "Data Efficient Neural Scaling Law via Model Reusing" by Peihao Wang, Rameswar Panda, Zhangyang Wang
[NeurIPS 2023] Multi-fidelity hyperparameter optimization with deep power laws that achieves state-of-the-art results across diverse benchmarks.
Code for reproducing the experiments on large-scale pre-training and transfer learning for the paper "Effect of large-scale pre-training on full and few-shot transfer learning for natural and medical images" (https://arxiv.org/abs/2106.00116)
Dimensionless learning codes for our paper called "Data-driven discovery of dimensionless numbers and governing laws from scarce measurements".
[NeurIPS'24 Spotlight] Observational Scaling Laws
A toolkit for scaling law research ⚖
Reproducible scaling laws for contrastive language-image learning (https://arxiv.org/abs/2212.07143)
Scaling Data-Constrained Language Models
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