Stars
Experimental modular probabilistic programming language in Rust, with modeling and inference separated by a generative function trait interface.
Curvature Corrected Moving Average: An accurate and model-free path smoothing algorithm.
A simple starter kit to prototype quickly your ideas with Three.js
A C++ library implementing linear algebra, text and file IO, UTF-N conversions, containers, image loading/saving, image quantization/filtering, command-line parsing, etc.
Stable Diffusion implemented from scratch in PyTorch
Productive, portable, and performant GPU programming in Python.
Examples on using web frameworks for making scientific applications.
Pitchfork is a Set of C++ Project Conventions
🚀 Kick-start your C++! A template for modern C++ projects using CMake, CI, code coverage, clang-format, reproducible dependency management and much more.
automatic differentiation made easier for C++
C++ template metaprogram driven tensor math library
A pytorch module to implement Bayesian neural networks with variational inference
Implementation of 'lightweight' GAN, proposed in ICLR 2021, in Pytorch. High resolution image generations that can be trained within a day or two
A quick and simple command line utility to create semantically-clustered image plots
Make runnable desktop apps from your python scripts more easily with pyvan!
Self-supervised learning for microscopy images
Detailed and tailored guide for undergraduate students or anybody want to dig deep into the field of AI with solid foundation.
CLASP - Contrastive Language-Aminoacid Sequence Pretraining
Type annotations and dynamic checking for a tensor's shape, dtype, names, etc.
Best Practices, code samples, and documentation for Computer Vision.
Pytorch Implementation of ClusterGAN (arXiv:1809.03627)
📜 A sane but flexible configuration framework inspired by Hydra config, with yaml and pythonic backends.
🧶 Modular VAE disentanglement framework for python built with PyTorch Lightning ▸ Including metrics and datasets ▸ With strongly supervised, weakly supervised and unsupervised methods ▸ Easily conf…
Deep learning via category theory and functional programming
A comprehensive list of awesome contrastive self-supervised learning papers.
An all-atom protein structure dataset for machine learning.