2277 Repositories
Python physics-informed-neural-networks Libraries
Code for ICML 2021 paper: How could Neural Networks understand Programs?
OSCAR This repository contains the source code of our ICML 2021 paper How could Neural Networks understand Programs?. Environment Run following comman
A static analysis library for computing graph representations of Python programs suitable for use with graph neural networks.
python_graphs This package is for computing graph representations of Python programs for machine learning applications. It includes the following modu
[CVPR21] LightTrack: Finding Lightweight Neural Network for Object Tracking via One-Shot Architecture Search
LightTrack: Finding Lightweight Neural Networks for Object Tracking via One-Shot Architecture Search The official implementation of the paper LightTra
Vision Transformer for 3D medical image registration (Pytorch).
ViT-V-Net: Vision Transformer for Volumetric Medical Image Registration keywords: vision transformer, convolutional neural networks, image registratio
Identify the emotion of multiple speakers in an Audio Segment
MevonAI - Speech Emotion Recognition
Predicting Semantic Map Representations from Images with Pyramid Occupancy Networks
This is the code associated with the paper Predicting Semantic Map Representations from Images with Pyramid Occupancy Networks, published at CVPR 2020.
Neural Dynamic Policies for End-to-End Sensorimotor Learning
This is a PyTorch based implementation for our NeurIPS 2020 paper on Neural Dynamic Policies for end-to-end sensorimotor learning.
The pytorch implementation of DG-Font: Deformable Generative Networks for Unsupervised Font Generation
DG-Font: Deformable Generative Networks for Unsupervised Font Generation The source code for 'DG-Font: Deformable Generative Networks for Unsupervised
Espresso: A Fast End-to-End Neural Speech Recognition Toolkit
Espresso Espresso is an open-source, modular, extensible end-to-end neural automatic speech recognition (ASR) toolkit based on the deep learning libra
pytorch-kaldi is a project for developing state-of-the-art DNN/RNN hybrid speech recognition systems. The DNN part is managed by pytorch, while feature extraction, label computation, and decoding are performed with the kaldi toolkit.
The PyTorch-Kaldi Speech Recognition Toolkit PyTorch-Kaldi is an open-source repository for developing state-of-the-art DNN/HMM speech recognition sys
This is the library for the Unbounded Interleaved-State Recurrent Neural Network (UIS-RNN) algorithm, corresponding to the paper Fully Supervised Speaker Diarization.
UIS-RNN Overview This is the library for the Unbounded Interleaved-State Recurrent Neural Network (UIS-RNN) algorithm. UIS-RNN solves the problem of s
Phrase-Based & Neural Unsupervised Machine Translation
Unsupervised Machine Translation This repository contains the original implementation of the unsupervised PBSMT and NMT models presented in Phrase-Bas
Neural building blocks for speaker diarization: speech activity detection, speaker change detection, overlapped speech detection, speaker embedding
⚠️ Checkout develop branch to see what is coming in pyannote.audio 2.0: a much smaller and cleaner codebase Python-first API (the good old pyannote-au
Sequence-to-Sequence Framework in PyTorch
nmtpytorch allows training of various end-to-end neural architectures including but not limited to neural machine translation, image captioning and au
A simple command line tool for text to image generation, using OpenAI's CLIP and a BigGAN.
Ryan Murdock has done it again, combining OpenAI's CLIP and the generator from a BigGAN! This repository wraps up his work so it is easily accessible to anyone who owns a GPU.
An easier way to build neural search on the cloud
Jina is geared towards building search systems for any kind of data, including text, images, audio, video and many more. With the modular design & multi-layer abstraction, you can leverage the efficient patterns to build the system by parts, or chaining them into a Flow for an end-to-end experience.
A large-scale dataset of both raw MRI measurements and clinical MRI images
fastMRI is a collaborative research project from Facebook AI Research (FAIR) and NYU Langone Health to investigate the use of AI to make MRI scans faster. NYU Langone Health has released fully anonymized knee and brain MRI datasets that can be downloaded from the fastMRI dataset page. Publications associated with the fastMRI project can be found at the end of this README.
SkipGNN: Predicting Molecular Interactions with Skip-Graph Networks (Scientific Reports)
SkipGNN: Predicting Molecular Interactions with Skip-Graph Networks Molecular interaction networks are powerful resources for the discovery. While dee
SMPLpix: Neural Avatars from 3D Human Models
subject0_validation_poses.mp4 Left: SMPL-X human mesh registered with SMPLify-X, middle: SMPLpix render, right: ground truth video. SMPLpix: Neural Av
HiFi-GAN: High Fidelity Denoising and Dereverberation Based on Speech Deep Features in Adversarial Networks
HiFiGAN Denoiser This is a Unofficial Pytorch implementation of the paper HiFi-GAN: High Fidelity Denoising and Dereverberation Based on Speech Deep F
Learning Skeletal Articulations with Neural Blend Shapes
This repository provides an end-to-end library for automatic character rigging and blend shapes generation as well as a visualization tool. It is based on our work Learning Skeletal Articulations with Neural Blend Shapes that is published in SIGGRAPH 2021.
QuickAI is a Python library that makes it extremely easy to experiment with state-of-the-art Machine Learning models.
QuickAI is a Python library that makes it extremely easy to experiment with state-of-the-art Machine Learning models.
Physics-Aware Training (PAT) is a method to train real physical systems with backpropagation.
Physics-Aware Training (PAT) is a method to train real physical systems with backpropagation. It was introduced in Wright, Logan G. & Onodera, Tatsuhiro et al. (2021)1 to train Physical Neural Networks (PNNs) - neural networks whose building blocks are physical systems.
QuickAI is a Python library that makes it extremely easy to experiment with state-of-the-art Machine Learning models.
QuickAI is a Python library that makes it extremely easy to experiment with state-of-the-art Machine Learning models.
A Research-oriented Federated Learning Library and Benchmark Platform for Graph Neural Networks. Accepted to ICLR'2021 - DPML and MLSys'21 - GNNSys workshops.
FedGraphNN: A Federated Learning System and Benchmark for Graph Neural Networks A Research-oriented Federated Learning Library and Benchmark Platform
PyDy, short for Python Dynamics, is a tool kit written in the Python
PyDy, short for Python Dynamics, is a tool kit written in the Python programming language that utilizes an array of scientific programs to enable the study of multibody dynamics. The goal is to have a modular framework and eventually a physics abstraction layer which utilizes a variety of backends that can provide the user with their desired workflow
An implementation of the paper "A Neural Algorithm of Artistic Style"
A Neural Algorithm of Artistic Style implementation - Neural Style Transfer This is an implementation of the research paper "A Neural Algorithm of Art
Unofficial & improved implementation of NeRF--: Neural Radiance Fields Without Known Camera Parameters
[Unofficial code-base] NeRF--: Neural Radiance Fields Without Known Camera Parameters [ Project | Paper | Official code base ] ⬅️ Thanks the original
This repository will contain the code for the CVPR 2021 paper "GIRAFFE: Representing Scenes as Compositional Generative Neural Feature Fields"
GIRAFFE: Representing Scenes as Compositional Generative Neural Feature Fields Project Page | Paper | Supplementary | Video | Slides | Blog | Talk If
Code to use Augmented Shapiro Wilks Stopping, as well as code for the paper "Statistically Signifigant Stopping of Neural Network Training"
This codebase is being actively maintained, please create and issue if you have issues using it Basics All data files are included under losses and ea
TorchFlare is a simple, beginner-friendly, and easy-to-use PyTorch Framework train your models effortlessly.
TorchFlare TorchFlare is a simple, beginner-friendly and an easy-to-use PyTorch Framework train your models without much effort. It provides an almost
"NAS-Bench-301 and the Case for Surrogate Benchmarks for Neural Architecture Search".
NAS-Bench-301 This repository containts code for the paper: "NAS-Bench-301 and the Case for Surrogate Benchmarks for Neural Architecture Search". The
Boost learning for GNNs from the graph structure under challenging heterophily settings. (NeurIPS'20)
Beyond Homophily in Graph Neural Networks: Current Limitations and Effective Designs Jiong Zhu, Yujun Yan, Lingxiao Zhao, Mark Heimann, Leman Akoglu,
Code and datasets for the paper "Combining Events and Frames using Recurrent Asynchronous Multimodal Networks for Monocular Depth Prediction" (RA-L, 2021)
Combining Events and Frames using Recurrent Asynchronous Multimodal Networks for Monocular Depth Prediction This is the code for the paper Combining E
PyTorch implementation of GLOM
GLOM PyTorch implementation of GLOM, Geoffrey Hinton's new idea that integrates concepts from neural fields, top-down-bottom-up processing, and attent
CorNet Correlation Networks for Extreme Multi-label Text Classification
CorNet Correlation Networks for Extreme Multi-label Text Classification Prerequisites python==3.6.3 pytorch==1.2.0 torchgpipe==0.0.5 click==7.0 ruamel
A Practical Debugging Tool for Training Deep Neural Networks
Cockpit is a visual and statistical debugger specifically designed for deep learning!
Reimplementation of the paper `Human Attention Maps for Text Classification: Do Humans and Neural Networks Focus on the Same Words? (ACL2020)`
Human Attention for Text Classification Re-implementation of the paper Human Attention Maps for Text Classification: Do Humans and Neural Networks Foc
ATOMIC 2020: On Symbolic and Neural Commonsense Knowledge Graphs
(Comet-) ATOMIC 2020: On Symbolic and Neural Commonsense Knowledge Graphs Paper Jena D. Hwang, Chandra Bhagavatula, Ronan Le Bras, Jeff Da, Keisuke Sa
code for paper "Does Unsupervised Architecture Representation Learning Help Neural Architecture Search?"
Does Unsupervised Architecture Representation Learning Help Neural Architecture Search? Code for paper: Does Unsupervised Architecture Representation
Graph Neural Networks for Recommender Systems
This repository contains code to train and test GNN models for recommendation, mainly using the Deep Graph Library (DGL).
OCTIS: Comparing Topic Models is Simple! A python package to optimize and evaluate topic models (accepted at EACL2021 demo track)
OCTIS : Optimizing and Comparing Topic Models is Simple! OCTIS (Optimizing and Comparing Topic models Is Simple) aims at training, analyzing and compa
(Arxiv 2021) NeRF--: Neural Radiance Fields Without Known Camera Parameters
NeRF--: Neural Radiance Fields Without Known Camera Parameters Project Page | Arxiv | Colab Notebook | Data Zirui Wang¹, Shangzhe Wu², Weidi Xie², Min
Official implementation of GraphMask as presented in our paper Interpreting Graph Neural Networks for NLP With Differentiable Edge Masking.
GraphMask This repository contains an implementation of GraphMask, the interpretability technique for graph neural networks presented in our ICLR 2021
Profil3r is an OSINT tool that allows you to find potential profiles of a person on social networks, as well as their email addresses 🕵️
Profil3r is an OSINT tool that allows you to find potential profiles of a person on social networks, as well as their email addresses. This program also alerts you to the presence of a data leak for the found emails.
A complete guide to start and improve in machine learning (ML)
A complete guide to start and improve in machine learning (ML), artificial intelligence (AI) in 2021 without ANY background in the field and stay up-to-date with the latest news and state-of-the-art techniques!
Sequence-to-sequence framework with a focus on Neural Machine Translation based on Apache MXNet
Sequence-to-sequence framework with a focus on Neural Machine Translation based on Apache MXNet
PyTorch implementation of neural style randomization for data augmentation
README Augment training images for deep neural networks by randomizing their visual style, as described in our paper: https://arxiv.org/abs/1809.05375
Scalable Graph Neural Networks for Heterogeneous Graphs
Neighbor Averaging over Relation Subgraphs (NARS) NARS is an algorithm for node classification on heterogeneous graphs, based on scalable neighbor ave
[CVPRW 21] "BNN - BN = ? Training Binary Neural Networks without Batch Normalization", Tianlong Chen, Zhenyu Zhang, Xu Ouyang, Zechun Liu, Zhiqiang Shen, Zhangyang Wang
BNN - BN = ? Training Binary Neural Networks without Batch Normalization Codes for this paper BNN - BN = ? Training Binary Neural Networks without Bat
Vector Neurons: A General Framework for SO(3)-Equivariant Networks
Vector Neurons: A General Framework for SO(3)-Equivariant Networks Created by Congyue Deng, Or Litany, Yueqi Duan, Adrien Poulenard, Andrea Tagliasacc
Bayesian-Torch is a library of neural network layers and utilities extending the core of PyTorch to enable the user to perform stochastic variational inference in Bayesian deep neural networks
Bayesian-Torch is a library of neural network layers and utilities extending the core of PyTorch to enable the user to perform stochastic variational inference in Bayesian deep neural networks. Bayesian-Torch is designed to be flexible and seamless in extending a deterministic deep neural network architecture to corresponding Bayesian form by simply replacing the deterministic layers with Bayesian layers.
Implementation of Perceiver, General Perception with Iterative Attention in TensorFlow
Perceiver This Python package implements Perceiver: General Perception with Iterative Attention by Andrew Jaegle in TensorFlow. This model builds on t
Visualization Toolbox for Long Short Term Memory networks (LSTMs)
Visualization Toolbox for Long Short Term Memory networks (LSTMs)
GrammarTagger — A Neural Multilingual Grammar Profiler for Language Learning
GrammarTagger — A Neural Multilingual Grammar Profiler for Language Learning GrammarTagger is an open-source toolkit for grammatical profiling for lan
Code related to "Have Your Text and Use It Too! End-to-End Neural Data-to-Text Generation with Semantic Fidelity" paper
DataTuner You have just found the DataTuner. This repository provides tools for fine-tuning language models for a task. See LICENSE.txt for license de
Exact Pareto Optimal solutions for preference based Multi-Objective Optimization
Exact Pareto Optimal solutions for preference based Multi-Objective Optimization
Visualization toolkit for neural networks in PyTorch! Demo --
FlashTorch A Python visualization toolkit, built with PyTorch, for neural networks in PyTorch. Neural networks are often described as "black box". The
Sandbox for training deep learning networks
Deep learning networks This repo is used to research convolutional networks primarily for computer vision tasks. For this purpose, the repo contains (
Image augmentation library in Python for machine learning.
Augmentor is an image augmentation library in Python for machine learning. It aims to be a standalone library that is platform and framework independe
Fine-tune pretrained Convolutional Neural Networks with PyTorch
Fine-tune pretrained Convolutional Neural Networks with PyTorch. Features Gives access to the most popular CNN architectures pretrained on ImageNet. A
PyTorch for Semantic Segmentation
PyTorch for Semantic Segmentation This repository contains some models for semantic segmentation and the pipeline of training and testing models, impl
Submanifold sparse convolutional networks
Submanifold Sparse Convolutional Networks This is the PyTorch library for training Submanifold Sparse Convolutional Networks. Spatial sparsity This li
Neural style transfer as a class in PyTorch
pt-styletransfer Neural style transfer as a class in PyTorch Based on: https://github.com/alexis-jacq/Pytorch-Tutorials Adds: StyleTransferNet as a cl
Python library containing BART query generation and BERT-based Siamese models for neural retrieval.
Neural Retrieval Embedding-based Zero-shot Retrieval through Query Generation leverages query synthesis over large corpuses of unlabeled text (such as
This project is a loose implementation of paper "Algorithmic Financial Trading with Deep Convolutional Neural Networks: Time Series to Image Conversion Approach"
Stock Market Buy/Sell/Hold prediction Using convolutional Neural Network This repo is an attempt to implement the research paper titled "Algorithmic F
TransNet V2: Shot Boundary Detection Neural Network
TransNet V2: Shot Boundary Detection Neural Network This repository contains code for TransNet V2: An effective deep network architecture for fast sho
QA-GNN: Question Answering using Language Models and Knowledge Graphs
QA-GNN: Question Answering using Language Models and Knowledge Graphs This repo provides the source code & data of our paper: QA-GNN: Reasoning with L
Try out deep learning models online on Google Colab
Try out deep learning models online on Google Colab
I-BERT: Integer-only BERT Quantization
I-BERT: Integer-only BERT Quantization HuggingFace Implementation I-BERT is also available in the master branch of HuggingFace! Visit the following li
Pytorch implementation of COIN, a framework for compression with implicit neural representations 🌸
COIN 🌟 This repo contains a Pytorch implementation of COIN: COmpression with Implicit Neural representations, including code to reproduce all experim
Code for Iso-Points: Optimizing Neural Implicit Surfaces with Hybrid Representations
Implementation for Iso-Points (CVPR 2021) Official code for paper Iso-Points: Optimizing Neural Implicit Surfaces with Hybrid Representations paper |
Neural Oblivious Decision Ensembles
Neural Oblivious Decision Ensembles A supplementary code for anonymous ICLR 2020 submission. What does it do? It learns deep ensembles of oblivious di
CondenseNet V2: Sparse Feature Reactivation for Deep Networks
CondenseNetV2 This repository is the official Pytorch implementation for "CondenseNet V2: Sparse Feature Reactivation for Deep Networks" paper by Le Y
Elliot is a comprehensive recommendation framework that analyzes the recommendation problem from the researcher's perspective.
Comprehensive and Rigorous Framework for Reproducible Recommender Systems Evaluation
Deep Reinforcement Learning based Trading Agent for Bitcoin
Deep Trading Agent Deep Reinforcement Learning based Trading Agent for Bitcoin using DeepSense Network for Q function approximation. For complete deta
Using multidimensional LSTM neural networks to create a forecast for Bitcoin price
Multidimensional LSTM BitCoin Time Series Using multidimensional LSTM neural networks to create a forecast for Bitcoin price. For notes around this co
A resource for learning about deep learning techniques from regression to LSTM and Reinforcement Learning using financial data and the fitness functions of algorithmic trading
A tour through tensorflow with financial data I present several models ranging in complexity from simple regression to LSTM and policy networks. The s
Predict stock movement with Machine Learning and Deep Learning algorithms
Project Overview Stock market movement prediction using LSTM Deep Neural Networks and machine learning algorithms Software and Library Requirements Th
Reinforcement Learning for Portfolio Management
qtrader Reinforcement Learning for Portfolio Management Why Reinforcement Learning? Learns the optimal action, rather than models the market. Adaptive
OHLC Average Prediction of Apple Inc. Using LSTM Recurrent Neural Network
Stock Price Prediction of Apple Inc. Using Recurrent Neural Network OHLC Average Prediction of Apple Inc. Using LSTM Recurrent Neural Network Dataset:
Introducing neural networks to predict stock prices
IntroNeuralNetworks in Python: A Template Project IntroNeuralNetworks is a project that introduces neural networks and illustrates an example of how o
Use unsupervised and supervised learning to predict stocks
AIAlpha: Multilayer neural network architecture for stock return prediction This project is meant to be an advanced implementation of stacked neural n
Tools for the extraction of OpenStreetMap street network data
OSMnet Tools for the extraction of OpenStreetMap (OSM) street network data. Intended to be used in tandem with Pandana and UrbanAccess libraries to ex
OSMnx: Python for street networks. Retrieve, model, analyze, and visualize street networks and other spatial data from OpenStreetMap.
OSMnx OSMnx is a Python package that lets you download geospatial data from OpenStreetMap and model, project, visualize, and analyze real-world street
Net2Vis automatically generates abstract visualizations for convolutional neural networks from Keras code.
Automatic neural network visualizations generated in your browser!
Tool for visualizing attention in the Transformer model (BERT, GPT-2, Albert, XLNet, RoBERTa, CTRL, etc.)
Tool for visualizing attention in the Transformer model (BERT, GPT-2, Albert, XLNet, RoBERTa, CTRL, etc.)
PyTorch code for the paper "Curriculum Graph Co-Teaching for Multi-target Domain Adaptation" (CVPR2021)
PyTorch code for the paper "Curriculum Graph Co-Teaching for Multi-target Domain Adaptation" (CVPR2021) This repo presents PyTorch implementation of M
Neural Reprojection Error: Merging Feature Learning and Camera Pose Estimation
Neural Reprojection Error: Merging Feature Learning and Camera Pose Estimation This is the official repository for our paper Neural Reprojection Error
Official Pytorch implementation of 'GOCor: Bringing Globally Optimized Correspondence Volumes into Your Neural Network' (NeurIPS 2020)
Official implementation of GOCor This is the official implementation of our paper : GOCor: Bringing Globally Optimized Correspondence Volumes into You
Official Implementation for "ReStyle: A Residual-Based StyleGAN Encoder via Iterative Refinement" https://arxiv.org/abs/2104.02699
ReStyle: A Residual-Based StyleGAN Encoder via Iterative Refinement Recently, the power of unconditional image synthesis has significantly advanced th
Implementation of TransGanFormer, an all-attention GAN that combines the finding from the recent GanFormer and TransGan paper
TransGanFormer (wip) Implementation of TransGanFormer, an all-attention GAN that combines the finding from the recent GansFormer and TransGan paper. I
Dynamic Slimmable Network (CVPR 2021, Oral)
Dynamic Slimmable Network (DS-Net) This repository contains PyTorch code of our paper: Dynamic Slimmable Network (CVPR 2021 Oral). Architecture of DS-
⚡ Automatically decrypt encryptions without knowing the key or cipher, decode encodings, and crack hashes ⚡
⚡ Automatically decrypt encryptions without knowing the key or cipher, decode encodings, and crack hashes ⚡
Spontaneous Facial Micro Expression Recognition using 3D Spatio-Temporal Convolutional Neural Networks
Spontaneous Facial Micro Expression Recognition using 3D Spatio-Temporal Convolutional Neural Networks Abstract Facial expression recognition in video
STYLER: Style Factor Modeling with Rapidity and Robustness via Speech Decomposition for Expressive and Controllable Neural Text to Speech
STYLER: Style Factor Modeling with Rapidity and Robustness via Speech Decomposition for Expressive and Controllable Neural Text to Speech Keon Lee, Ky
Official implementation for NIPS'17 paper: PredRNN: Recurrent Neural Networks for Predictive Learning Using Spatiotemporal LSTMs.
PredRNN: A Recurrent Neural Network for Spatiotemporal Predictive Learning The predictive learning of spatiotemporal sequences aims to generate future
NetworkX is a Python package for the creation, manipulation, and study of the structure, dynamics, and functions of complex networks.
NetworkX is a Python package for the creation, manipulation, and study of the structure, dynamics, and functions of complex networks.
Semi-supervised Learning for Sentiment Analysis
Neural-Semi-supervised-Learning-for-Text-Classification-Under-Large-Scale-Pretraining Code, models and Datasets for《Neural Semi-supervised Learning fo
《A-CNN: Annularly Convolutional Neural Networks on Point Clouds》(2019)
A-CNN: Annularly Convolutional Neural Networks on Point Clouds Created by Artem Komarichev, Zichun Zhong, Jing Hua from Department of Computer Science