Alex Wang's repositories
differentiable_t1d
Differentiable simulators for Type 1 Diabetes research in Jax and PyTorch
importance-weighting-interpolating-classifiers
Code for "Is Importance Weighting Incompatible with Interpolating Classifiers?"
interpretable-cgm-representations
Code for "Interpretable Mechanistic Representations for Meal-level Glycemic Control in the Wild" (ML4H 2023; selected for lightning talk)
scientific-visualization-exercises
Solutions to Rougier's Scientific Visualization, Python & Matplotlib book
fpp3-python-readalong
Python-centered read-along of Forecasting: Principles and Practice
ml_template
A template for prototyping machine learning ideas in PyTorch
bayesian_benchmarks
A community repository for benchmarking Bayesian methods
client
🔥 A tool for visualizing and tracking your machine learning experiments. This repo contains the CLI and Python API.
continuous-time-reading-group
Stanford reading group on continuous time models for machine learning
dynamax
State Space Models library in JAX
emmental
A deep learning framework for building multimodal multi-task learning systems.
firedup
Clone of OpenAI's Spinning Up in PyTorch
lower-the-entropy
A personal blog.
MLcites
This repository contains citation data for papers published in NeurIPS in 2014 - 2018, and ICML 2017, 2018. It also contains the code to collect this data.
MolecularNotes
My Obsidian Second Brain setup
mujoco-py
MuJoCo is a physics engine for detailed, efficient rigid body simulations with contacts. mujoco-py allows using MuJoCo from Python 3.
neuralforecast
Scalable and user friendly neural :brain: forecasting algorithms.
nn-template
Generic template to bootstrap your PyTorch project with PyTorch Lightning, Hydra, W&B, and DVC.
pyRdatasets
1300 datasets from various R packages packed as DataFrames through compressed pickle files
pytorch-normalizing-flows
Normalizing flows in PyTorch. Current intended use is education not production.
scipytorch
Scientific computation routines in PyTorch
ssm-jax
Bayesian learning and inference for state space models (SSMs) using Google Research's JAX as a backend
torch_cg
Preconditioned Conjugate Gradient in Pytorch
wilds
A machine learning benchmark of in-the-wild distribution shifts, with data loaders, evaluators, and default models.