Contrastive Variational Autoencoders

Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.

Using Deep Learning to predict gene annotations

A latent text-to-image diffusion model

Official home of genome aligner based upon notion of Cactus graphs

TensorFlow documentation

Implementation of Vision Transformer, a simple way to achieve SOTA in vision classification with only a single transformer encoder, in Pytorch

Code release for "Masked-attention Mask Transformer for Universal Image Segmentation"

End-to-End Object Detection with Transformers

Novelty-inclusive microbial (and now dsDNA phage) community profiling of shotgun metagenomes

A Python package for generating virtual cells using diffusion models from single-cell RNA sequencing data.

An extremely fast Python package and project manager, written in Rust.

gReLU is a python library to train, interpret, and apply deep learning models to DNA sequences.

Pretrain, finetune ANY AI model of ANY size on 1 or 10,000+ GPUs with zero code changes.

Evolutionary Scale Modeling (esm): Pretrained language models for proteins

Simple Online Realtime Tracking with a Deep Association Metric

A high-throughput and memory-efficient inference and serving engine for LLMs

The best ChatGPT that $100 can buy.

CAMISIM: Simulating metagenomes and microbial communities

Fast genome-wide functional annotation through orthology assignment

Taxonomic annotation tool using a two-tier approach.

Transposable Element Repeat Result classifIER

HErbarium Specimen sheet PIpeline

Code for running RFdiffusion

A Hierarchical Softmax Framework for PyTorch

Ultra-fast and memory-efficient (meta-)genome assembler

High-Performance Scientific Modeling with Julia and SciML

A scalable manifold learning (SUDE) method that can cope with large-scale and high-dimensional data in an efficient manner

Bin Chicken - recovery of low abundance and taxonomically targeted metagenome assembled genomes (MAGs) through strategic coassembly

PyTorch code for Vision Transformers training with the Self-Supervised learning method DINO