🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.

Deepfakes Software For All

Super Resolution for images using deep learning.

手写实现李航《统计学习方法》书中全部算法

Real-Time and Accurate Full-Body Multi-Person Pose Estimation&Tracking System

Replace OpenAI GPT with another LLM in your app by changing a single line of code. Xinference gives you the freedom to use any LLM you need. With Xinference, you're empowered to run inference with …

A Python framework for high performance GPU simulation and graphics

Learning in infinite dimension with neural operators.

Translate images to unseen domains in the test time with few example images.

A differentiable PDE solving framework for machine learning

Open-source deep-learning framework for building, training, and fine-tuning deep learning models using state-of-the-art Physics-ML methods

Graph Transformer Architecture. Source code for "A Generalization of Transformer Networks to Graphs", DLG-AAAI'21.

A Large-Scale Multimodal Car Dataset with Computational Fluid Dynamics Simulations and Deep Learning Benchmarks

[NeurIPS 2021] Galerkin Transformer: a linear attention without softmax for Partial Differential Equations

[ICLR 2023 Spotlight] Equiformer: Equivariant Graph Attention Transformer for 3D Atomistic Graphs

Implementation of Graph Transformer in Pytorch, for potential use in replicating Alphafold2

Source code of ICML'22 paper: FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecasting

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Repo to the paper "Message Passing Neural PDE Solvers"

NAGphormer: A Tokenized Graph Transformer for Node Classification in Large Graphs

A Spatio-temporal Transformer for 3D Human Motion Prediction

[ICML 2023] Learning Neural Constitutive Laws from Motion Observations for Generalizable PDE Dynamics

Official repository for the paper "Masksembles for Uncertainty Estimation" (CVPR 2021).

行人行为轨迹预测

PyTorch implementation of BGRL (https://arxiv.org/abs/2102.06514)

Pytorch implementation of "Large-Scale Representation Learning on Graphs via Bootstrapping"