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MLCommons Algorithmic Efficiency is a benchmark and competition measuring neural network training speedups due to algorithmic improvements in both training algorithms and models.
Quelques exemples et outils pour exploiter les données mise à disposition sur le site data.assemblee-nationale.fr
Materials for "best practices for collaborative latex" course for PhD students at CREST
Implementation of Vision Transformer, a simple way to achieve SOTA in vision classification with only a single transformer encoder, in Pytorch
⏰ Agenticly track worldwide conference deadlines (Website, Python Cli, Wechat Applet)
[JMLR 2023] Quantus is an eXplainable AI toolkit for responsible evaluation of neural network explanations
Flexible and powerful tensor operations for readable and reliable code (for pytorch, jax, TF and others)
A collection of research materials on explainable AI/ML
Detect mislabeled examples in machine learning datasets
A Python Library for Biquality Learning
The Python library of the Khiops AutoML suite
Khiops is an AutoML suite for supervised and unsupervised learning
Calibration library and code for the paper: Verified Uncertainty Calibration. Ananya Kumar, Percy Liang, Tengyu Ma. NeurIPS 2019 (Spotlight).
Stable Diffusion web UI
A curated list of resources for Learning with Noisy Labels
A framework for reproducible, comparable benchmarks
Corruption and Perturbation Robustness (ICLR 2019)
Pelican static site generator config for Lawler.io
Repository containing Pytorch code for EKFAC and K-FAC perconditioners.
Probabilistic line search algorithm for stochastic optimization with a TensorFlow interface.
Pytorch Implementation of paper "Noisy Natural Gradient as Variational Inference"
A curated list of deep learning resources for computer vision
Recurrent Neural Network - A curated list of resources dedicated to RNN