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Yifu Hu's starred repositories

transformers

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

Language:PythonApache-2.0131358 1117 15580

annotated_deep_learning_paper_implementations

🧑‍🏫 60+ Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, sophia, ...), gans(cyclegan, stylegan2, ...), 🎮 reinforcement learning (ppo, dqn), capsnet, distillation, ... 🧠

Language:PythonMIT53312 446 131

bert

TensorFlow code and pre-trained models for BERT

Language:PythonApache-2.037743 1000 1142

instant-ngp

Instant neural graphics primitives: lightning fast NeRF and more

Language:CudaNOASSERTION15755 205 1015

sentence-transformers

Multilingual Sentence & Image Embeddings with BERT

Language:PythonApache-2.014750 138 2110

nerf

Code release for NeRF (Neural Radiance Fields)

Language:Jupyter NotebookMIT9738 142 193

attention-is-all-you-need-pytorch

A PyTorch implementation of the Transformer model in "Attention is All You Need".

Language:PythonMIT8699 95 181

nerf-pytorch

A PyTorch implementation of NeRF (Neural Radiance Fields) that reproduces the results.

Language:PythonMIT5336 53 114

dcgan_code

Deep Convolutional Generative Adversarial Networks

Language:PythonMIT3428 158 33

trimesh

Python library for loading and using triangular meshes.

Language:PythonMIT2923 48 1549

language

Shared repository for open-sourced projects from the Google AI Language team.

Language:PythonApache-2.01597 61 116

MeshCNN

Convolutional Neural Network for 3D meshes in PyTorch

Language:PythonMIT1570 45 157

beir

A Heterogeneous Benchmark for Information Retrieval. Easy to use, evaluate your models across 15+ diverse IR datasets.

Language:PythonApache-2.01536 23 136

occupancy_networks

This repository contains the code for the paper "Occupancy Networks - Learning 3D Reconstruction in Function Space"

Language:PythonMIT1500 33 129

natural-questions

Natural Questions (NQ) contains real user questions issued to Google search, and answers found from Wikipedia by annotators. NQ is designed for the training and evaluation of automatic question answering systems.

Language:PythonApache-2.0916 35 20

LSTM-FCN

Codebase for the paper LSTM Fully Convolutional Networks for Time Series Classification

Language:Python739 27 28

PointSetGeneration

Code for ``A Point Set Generation Network for 3D Object Reconstruction from a Single Image''

Language:Python544 22 35

densenet-pytorch

A PyTorch Implementation for Densely Connected Convolutional Networks (DenseNets)

Language:PythonBSD-3-Clause455 4 9

TS-TCC

[IJCAI-21] "Time-Series Representation Learning via Temporal and Contextual Contrasting"

Language:PythonMIT346 5 33

tacto

Simulator of vision-based tactile sensors.

Language:PythonMIT328 20 37

ndf

Neural Unsigned Distance Fields (NDF) - Codebase

Language:Python187 7 24

VirtualCapsuleEndoscopy

VR-Caps: A Virtual Environment for Active Capsule Endoscopy

Language:C#148 7 16

ObjectFolder

ObjectFolder Dataset

Language:PythonCC-BY-4.0147 6 5

MSMARCO-Conversational-Search

Truly Conversational Search is the next logic step in the journey to generate intelligent and useful AI. To understand what this may mean, researchers have voiced a continuous desire to study how people currently converse with search engines. Traditionally, the desire to produce such a comprehensive dataset has been limited because those who have this data (Search Engines) have a responsibility to their users to maintain their privacy and cannot share the data publicly in a way that upholds the trusts users have in the Search Engines. Given these two powerful forces we believe we have a dataset and paradigm that meets both sets of needs: A artificial public dataset that approximates the true data and an ability to evaluate model performance on the real user behavior. What this means is we released a public dataset which is generated by creating artificial sessions using embedding similarity and will test on the original data. To say this again: we are not releasing any private user data but are releasing what we believe to be a good representation of true user interactions.

Language:PythonMIT107 12 4

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