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The fundamental package for scientific computing with Python.
A PyTorch library entirely dedicated to neural differential equations, implicit models and related numerical methods
High-Performance Symbolic Regression in Python and Julia
Lightweight coding agent that runs in your terminal
Library for Jacobian descent with PyTorch. It enables the optimization of neural networks with multiple losses (e.g. multi-task learning).
Mastering Diverse Domains through World Models
TextGrad: Automatic ''Differentiation'' via Text -- using large language models to backpropagate textual gradients. Published in Nature.
Model interpretability and understanding for PyTorch
Differentiable ODE solvers with full GPU support and O(1)-memory backpropagation.
Official implementation of DeepLabCut: Markerless pose estimation of user-defined features with deep learning for all animals incl. humans
Unofficial implementation of Titans, SOTA memory for transformers, in Pytorch
High accuracy RAG for answering questions from scientific documents with citations
Learnable latent embeddings for joint behavioral and neural analysis - Official implementation of CEBRA
[ICLR2025] Halton Scheduler for Masked Generative Image Transformer
Vector (and Scalar) Quantization, in Pytorch
ImageBind One Embedding Space to Bind Them All
The repository provides code for running inference with the SegmentAnything Model (SAM), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.
Official Implementation of Semantic Image Synthesis via Diffusion Models
[NeurIPS2022] Mind Reader: Reconstructing complex images from brain activities
Simple image captioning model
High-Resolution Image Synthesis with Latent Diffusion Models
Implementation of Vision Transformer, a simple way to achieve SOTA in vision classification with only a single transformer encoder, in Pytorch
PyTorch implementation of VQ-VAE-2 from "Generating Diverse High-Fidelity Images with VQ-VAE-2"