alecwangcq

lightning

Deep learning framework to train, deploy, and ship AI products Lightning fast.

CLIP

CLIP (Contrastive Language-Image Pretraining), Predict the most relevant text snippet given an image

arxiv-latex-cleaner

arXiv LaTeX Cleaner: Easily clean the LaTeX code of your paper to submit to arXiv

sacred

Sacred is a tool to help you configure, organize, log and reproduce experiments developed at IDSIA.

annotated_latex_equations

Examples of how to create colorful, annotated equations in Latex using Tikz.

acme

A library of reinforcement learning components and agents

botorch

Bayesian optimization in PyTorch

matplotlib-cheatsheet

Matplotlib 3.1 cheat sheet.

Ax

Adaptive Experimentation Platform

neural-tangents

Fast and Easy Infinite Neural Networks in Python

brax

Massively parallel rigidbody physics simulation on accelerator hardware.

cvxpylayers

Differentiable convex optimization layers

submitit

Python 3.8+ toolbox for submitting jobs to Slurm

Efficient-Deep-Learning

Collection of recent methods on (deep) neural network compression and acceleration.

jaxton

100 exercises to learn JAX

omniboard

Web-based dashboard for Sacred

residual-flows

code for "Residual Flows for Invertible Generative Modeling".

BatchBALD

Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning.

nngp

Deep neural network kernel for Gaussian process

TransformersCanDoBayesianInference

Official Implementation of "Transformers Can Do Bayesian Inference", the PFN paper

jaxfg

Factor graphs and nonlinear least squares for JAX

snip

Pytorch implementation of the paper "SNIP: Single-shot Network Pruning based on Connection Sensitivity" by Lee et al.

backpack

This repository is no longer maintained. Check

FBNN

Code for "Functional variational Bayesian neural networks" (https://arxiv.org/abs/1903.05779)

MAX

Code for reproducing experiments in Model-Based Active Exploration, ICML 2019

NoisyNaturalGradient

TensorFlow implementation of "noisy K-FAC" and "noisy EK-FAC".

self-tuning-networks

Code for Self-Tuning Networks (ICLR 2019) https://arxiv.org/abs/1903.03088

Follow-the-Ridge

Minimax Optimization, Stackelberg Games, Generative Adversarial Networks

Weight-Decay

Regularization, Neural Network Training Dynamics

predictive-correlation-benchmark