Rohith-Rongali

Rohith-Rongali's Stars

• amitrajaraman/notes

  Notes galore

  Language:TeX92

• lchizat/2023-BAFU

  Code for the paper L. Chizat, P. Netrapalli (2023). "Steering Deep Feature Learning with Backward Aligned Feature Updates".

  Language:Jupyter Notebook2

• aradha/lin-RFM

  Code for lin-RFM used for sparse recovery tasks

  Language:Python9

• bGhorbani/linearized_neural_networks

  The code for the paper "When do neural networks outperform kernel methods"

  Language:Python2

• modestyachts/neural_kernels_code

  Language:Python365

• tml-epfl/sgd-sparse-features

  SGD with large step sizes learns sparse features [ICML 2023]

  Language:Jupyter Notebook325

• LiyuanLucasLiu/RAdam

  On the Variance of the Adaptive Learning Rate and Beyond

  Language:Python2.5k333

• dit/dit

  Python package for information theory.

  Language:Python52390

• locuslab/edge-of-stability

  Language:Python6119

• djsutherland/html-talk

  Base for my talks using reveal.js, with bonus nice features (including browser/editor sync!)

  Language:JavaScript93

• steveazzolin/gdl_tutorial_turinginst

  Material for the hands-on tutorial on Graph Deep Learning held at the Alan Turing Institute

  Language:Jupyter Notebook5610

• LeoGrin/tabular-benchmark

  Language:Python46261

• pilancilab/convex_nn

  Language:Python133

• aleximmer/Laplace

  Laplace approximations for Deep Learning.

  Language:Python48075

• namlede/lm-evaluation-harness

  A framework for few-shot evaluation of autoregressive language models.

  Language:Python1

• eugeneyan/open-llms

  📋 A list of open LLMs available for commercial use.

  11.4k764

• state-spaces/mamba

  Mamba SSM architecture

  Language:Python13.6k1.2k

• mariuslindegaard/Intermediate_Neural_Collapse

  (ICML 2023) Feature learning in deep classifiers through Intermediate Neural Collapse: Accompanying code

  Language:Python131

• bobby-he/simplified_transformers

  Language:Python28425

• anthropics/toy-models-of-superposition

  Notebooks accompanying Anthropic's "Toy Models of Superposition" paper

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• google-research/jaxpruner

  Language:Python21214

• srush/GPU-Puzzles

  Solve puzzles. Learn CUDA.

  Language:Jupyter Notebook10.2k966

• TransformerLensOrg/TransformerLens

  A library for mechanistic interpretability of GPT-style language models

  Language:Python1.7k314

• EleutherAI/math-lm

  Language:Python1.1k85

• NVIDIA/TensorRT-LLM

  TensorRT-LLM provides users with an easy-to-use Python API to define Large Language Models (LLMs) and build TensorRT engines that contain state-of-the-art optimizations to perform inference efficiently on NVIDIA GPUs. TensorRT-LLM also contains components to create Python and C++ runtimes that execute those TensorRT engines.

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• stanfordnlp/dspy

  DSPy: The framework for programming—not prompting—language models

  Language:Python20.5k1.6k

• cleverhans-lab/cleverhans

  An adversarial example library for constructing attacks, building defenses, and benchmarking both

  Language:Jupyter Notebook6.2k1.4k

• ResearchDaniel/NeuralActivationPatterns

  Language:Python2

• princeton-nlp/LM-Kernel-FT

  A Kernel-Based View of Language Model Fine-Tuning https://arxiv.org/abs/2210.05643

  Language:Python725

• aradha/deep_neural_feature_ansatz

  Code for verifying deep neural feature ansatz

  Language:Python152