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

Graph Neural Network Library for PyTorch

Python package built to ease deep learning on graph, on top of existing DL frameworks.

RWKV (pronounced RwaKuv) is an RNN with great LLM performance, which can also be directly trained like a GPT transformer (parallelizable). We are at RWKV-7 "Goose". So it's combining the best of RNN and transformer - great performance, linear time, constant space (no kv-cache), fast training, infinite ctx_len, and free sentence embedding.

Anomaly detection related books, papers, videos, and toolboxes

Code for "Training-free Graph Neural Networks and the Power of Labels as Features" (TMLR 2024)

Codes and datasets for NeurIPS21 paper “Towards Open-World Feature Extrapolation: An Inductive Graph Learning Approach”

Implementation / replication of DALL-E, OpenAI's Text to Image Transformer, in Pytorch

A concise but complete full-attention transformer with a set of promising experimental features from various papers

links to conference publications in graph-based deep learning