Code for Concrete Dropout as presented in https://arxiv.org/abs/1705.07832

Low rank adaptation for Vision Transformer

PyTorch implementation of SwAV https//arxiv.org/abs/2006.09882

Prov-GigaPath: A whole-slide foundation model for digital pathology from real-world data

Diffusion model papers, survey, and taxonomy

My implementation of the original GAT paper (Veličković et al.). I've additionally included the playground.py file for visualizing the Cora dataset, GAT embeddings, an attention mechanism, and entr…

A pytorch implementation of the vector quantized variational autoencoder (https://arxiv.org/abs/1711.00937)

DiagSet: a dataset for prostate cancer histopathological image classification

PyTorch codes for "Real-World Blind Super-Resolution via Feature Matching with Implicit High-Resolution Priors", ACM MM2022 (Oral)

BertViz: Visualize Attention in NLP Models (BERT, GPT2, BART, etc.)

Simple and efficient pytorch-native transformer text generation in <1000 LOC of python.

Segment Anything in Medical Images

Awesome papers about machine learning (deep learning) on dynamic (temporal) graphs (networks / knowledge graphs).

Evaluation of Methods for Temporal Knowledge Graph Forecasting

Read and write life sciences file formats

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 PyTorch Implementation of "Scalable Diffusion Models with Transformers"

Hierarchical Image Pyramid Transformer - CVPR 2022 (Oral)

A PyTorch implementation of MAGE: MAsked Generative Encoder to Unify Representation Learning and Image Synthesis

PyTorch implementation of MoCo v3 https//arxiv.org/abs/2104.02057

Official DeiT repository

Simultaneous Nuclear Instance Segmentation and Classification in H&E Histology Images.

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

The pytest framework makes it easy to write small tests, yet scales to support complex functional testing

Course webpage for UCLA Biostat 203B (Intro. to Data Science)

WaveNILM as published at ICASSP 2019

Official Implementation of Paella https://arxiv.org/abs/2211.07292v2

Pretrain, finetune ANY AI model of ANY size on multiple GPUs, TPUs with zero code changes.

The repository contains a simple pipeline for training Nuclei Segmentation Datasets of Histopathology Images.