Python Neural-Machine-Translation Resources

neurodsp is a collection of approaches for applying digital signal processing to neural time series, including algorithms that have been proposed for the analysis of neural time series. It also includes simulation tools for generating plausible simulations of neural time series.

Microsoft contributing libraries, tools, recipes, sample codes and workshop contents for machine learning & deep learning.

Deep Learning Visuals contains 215 unique images divided in 23 categories (some images may appear in more than one category). All the images were originally published in my book "Deep Learning with PyTorch Step-by-Step: A Beginner's Guide".

TensorFlow Decision Forests (TF-DF) is a collection of state-of-the-art algorithms for the training, serving and interpretation of Decision Forest models. The library is a collection of Keras models and supports classification, regression and ranking. TF-DF is a TensorFlow wrapper around the Yggdrasil Decision Forests C++ libraries. Models trained with TF-DF are compatible with Yggdrasil Decision Forests' models, and vice versa.

Mask-Align: Self-Supervised Neural Word Alignment This is the implementation of our work Mask-Align: Self-Supervised Neural Word Alignment. @inproceed

federated is the source code for the Bachelor's Thesis Privacy-Preserving Federated Learning Applied to Decentralized Data (Spring 2021, NTNU) Federat

Orchest is a browser based IDE for Data Science. It integrates your favorite Data Science tools out of the box, so you don’t have to. The application is easy to use and can run on your laptop as well as on a large scale cloud cluster.

Image Crop Analysis This is a repo for the code used for reproducing our Image Crop Analysis paper as shared on our blog post. If you plan to use this

BRIMs Bidirectional Recurrent Independent Mechanisms Implementation of the paper Learning to Combine Top-Down and Bottom-Up Signals in Recurrent Neura

Shapley Explanation Networks Implementation of the paper "Shapley Explanation Networks" at ICLR 2021. Note that this repo heavily uses the experimenta

LIBRAS-Image-Classifier This project demonstrates the use of neural networks and computer vision to create a classifier that interprets the Brazilian

LICS2021 MRC 1. 项目&任务介绍 本项目基于官方给定的baseline（DuReader-Checklist-BASELINE）进行二次改造，对整个代码框架做了简单的重构，对核心网络结构添加了注释，解耦了数据读取的模块，并添加了阈值确认的功能，一些小的细节也做了改进。 本次任务为202

Noronha DataOps Noronha is a Python framework designed to help you orchestrate and manage ML projects life-cycle. It hosts Machine Learning models ins

Summary This is the code for the paper Event-Stream Representation for Human Gaits Identification Using Deep Neural Networks by Yanxiang Wang, Xian Zh

Hierarchical Cross-Modal Agent for Robotics Vision-and-Language Navigation This repository is the pytorch implementation of our paper: Hierarchical Cr

LibAUC An end-to-end machine learning library for AUC optimization. Why LibAUC? Deep AUC Maximization (DAM) is a paradigm for learning a deep neural n

An Unsupervised Graph-based Toolbox for Fraud Detection Introduction: UGFraud is an unsupervised graph-based fraud detection toolbox that integrates s

Parallel Tacotron2 Pytorch Implementation of Google's Parallel Tacotron 2: A Non-Autoregressive Neural TTS Model with Differentiable Duration Modeling

Expressive Power of Invariant and Equivaraint Graph Neural Networks In this repository, we show how to use powerful GNN (2-FGNN) to solve a graph alig

NeuralTextures This is repository with inference code for paper "StylePeople: A Generative Model of Fullbody Human Avatars" (CVPR21). This code is for

Apache Liminals goal is to operationalise the machine learning process, allowing data scientists to quickly transition from a successful experiment to an automated pipeline of model training, validation, deployment and inference in production. Liminal provides a Domain Specific Language to build ML workflows on top of Apache Airflow.

Currently a Beta-Version lucidmode is an open-source, low-code and lightweight Python framework for transparent and interpretable machine learning mod

Python Streaming Anomaly Detection (PySAD) PySAD is an open-source python framework for anomaly detection on streaming multivariate data. Documentatio

XtremeDistilTransformers for Distilling Massive Multilingual Neural Networks ACL 2020 Microsoft Research [Paper] [Video] Releasing [XtremeDistilTransf

Deep-rPPG: Camera-based pulse estimation using deep learning tools Deep learning (neural network) based remote photoplethysmography: how to extract pu

This repository contains the code release for Mip-NeRF: A Multiscale Representation for Anti-Aliasing Neural Radiance Fields. This implementation is written in JAX, and is a fork of Google's JaxNeRF implementation. Contact Jon Barron if you encounter any issues.

🚀 Quick start An instance of ova-server is free and publicly available here: https://api.openvisionapi.com Checkout ova-client for a quick demo. Inst

Graphsignal Logger Overview Graphsignal is an observability platform for monitoring and troubleshooting production machine learning applications. It h

Vowpal Wabbit is a machine learning system which pushes the frontier of machine learning with techniques such as online, hashing, allreduce, reductions, learning2search, active, and interactive learning.

FNet: Mixing Tokens with Fourier Transforms Pytorch implementation of Fnet : Mixing Tokens with Fourier Transforms. Citation: @misc{leethorp2021fnet,

SapBERT: Self-alignment pretraining for BERT This repo holds code for the SapBERT model presented in our NAACL 2021 paper: Self-Alignment Pretraining

mlp-mixer-pytorch PyTorch implementation of "MLP-Mixer: An all-MLP Architecture for Vision" Tolstikhin et al. (2021) Usage import torch from mlp_mixer

DeepStack_ExDark This repository provides a custom DeepStack model that has been trained and can be used for creating a new object detection API for d

A curated list of programmatic weak supervision papers and resources

This repo includes the Pytorch-Geometric implementation of a series of Graph Neural Network (GNN) based fake news detection models. All GNN models are implemented and evaluated under the User Preference-aware Fake News Detection (UPFD) framework. The fake news detection problem is instantiated as a graph classification task under the UPFD framework.

NeRF Meta Learning With PyTorch nerf-meta is a PyTorch re-implementation of NeRF experiments from the paper "Learned Initializations for Optimizing Co

OSCAR This repository contains the source code of our ICML 2021 paper How could Neural Networks understand Programs?. Environment Run following comman

python_graphs This package is for computing graph representations of Python programs for machine learning applications. It includes the following modu

LightTrack: Finding Lightweight Neural Networks for Object Tracking via One-Shot Architecture Search The official implementation of the paper LightTra

👩‍✈️ Coqpit Simple, light-weight and no dependency config handling through python data classes with to/from JSON serialization/deserialization. Curre

30 Days Of Machine Learning Using Pytorch Objective of the repository is to learn and build machine learning models using Pytorch. List of Algorithms

ViT-V-Net: Vision Transformer for Volumetric Medical Image Registration keywords: vision transformer, convolutional neural networks, image registratio

MILES Multilingual Lexical Simplifier Explore the docs » Read LSBert Paper · Report Bug · Request Feature About The Project MILES is a multilingual te

MevonAI - Speech Emotion Recognition

TANL: Structured Prediction as Translation between Augmented Natural Languages Code for the paper "Structured Prediction as Translation between Augmen

This is a PyTorch based implementation for our NeurIPS 2020 paper on Neural Dynamic Policies for end-to-end sensorimotor learning.

A collection of ready-to-run Python* notebooks for learning and experimenting with OpenVINO developer tools. The notebooks are meant to provide an introduction to OpenVINO basics and teach developers how to leverage our APIs for optimized deep learning inference in their applications.

DeepSpeed is a deep learning optimization library that makes distributed training easy, efficient, and effective.

This repository represents Ultralytics open-source research into future object detection methods, and incorporates lessons learned and best practices evolved over thousands of hours of training and evolution on anonymized client datasets. All code and models are under active development, and are subject to modification or deletion without notice.

Reformer, the Efficient Transformer, in Pytorch This is a Pytorch implementation of Reformer https://openreview.net/pdf?id=rkgNKkHtvB It includes LSH

Espresso Espresso is an open-source, modular, extensible end-to-end neural automatic speech recognition (ASR) toolkit based on the deep learning libra

The PyTorch-Kaldi Speech Recognition Toolkit PyTorch-Kaldi is an open-source repository for developing state-of-the-art DNN/HMM speech recognition sys

Texar-PyTorch is a toolkit aiming to support a broad set of machine learning, especially natural language processing and text generation tasks. Texar

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

Unsupervised Machine Translation This repository contains the original implementation of the unsupervised PBSMT and NMT models presented in Phrase-Bas

ESPnet: end-to-end speech processing toolkit system/pytorch ver. 1.0.1 1.1.0 1.2.0 1.3.1 1.4.0 1.5.1 1.6.0 1.7.1 1.8.1 ubuntu18/python3.8/pip ubuntu18

⚠️ 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

nmtpytorch allows training of various end-to-end neural architectures including but not limited to neural machine translation, image captioning and au

PyMC3 is a Python package for Bayesian statistical modeling and Probabilistic Machine Learning focusing on advanced Markov chain Monte Carlo (MCMC) an

Karate Club is an unsupervised machine learning extension library for NetworkX. Please look at the Documentation, relevant Paper, Promo Video, and Ext

An open source framework that provides a simple, universal API for building distributed applications. Ray is packaged with RLlib, a scalable reinforcement learning library, and Tune, a scalable hyperparameter tuning library.

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.

Turns your machine learning code into microservices with web API, interactive GUI, and more.

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.

subject0_validation_poses.mp4 Left: SMPL-X human mesh registered with SMPLify-X, middle: SMPLpix render, right: ground truth video. SMPLpix: Neural Av

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.

FedGraphNN: A Federated Learning System and Benchmark for Graph Neural Networks A Research-oriented Federated Learning Library and Benchmark Platform

Implementation of different ML Algorithms from scratch, written in Python 3.x

A Neural Algorithm of Artistic Style implementation - Neural Style Transfer This is an implementation of the research paper "A Neural Algorithm of Art

[Unofficial code-base] NeRF--: Neural Radiance Fields Without Known Camera Parameters [ Project | Paper | Official code base ] ⬅️ Thanks the original

One Stop Anomaly Shop (OSAS) Quick start guide Step 1: Get/build the docker image Option 1: Use precompiled image (might not reflect latest changes):

GIRAFFE: Representing Scenes as Compositional Generative Neural Feature Fields Project Page | Paper | Supplementary | Video | Slides | Blog | Talk If

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 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 This repository containts code for the paper: "NAS-Bench-301 and the Case for Surrogate Benchmarks for Neural Architecture Search". The

Beyond Homophily in Graph Neural Networks: Current Limitations and Effective Designs Jiong Zhu, Yujun Yan, Lingxiao Zhao, Mark Heimann, Leman Akoglu,

GLOM PyTorch implementation of GLOM, Geoffrey Hinton's new idea that integrates concepts from neural fields, top-down-bottom-up processing, and attent

Spatially-Correlative Loss arXiv | website We provide the Pytorch implementation of "The Spatially-Correlative Loss for Various Image Translation Task

arXiv Dual Contrastive Learning Adversarial Generative Networks (DCLGAN) We provide our PyTorch implementation of DCLGAN, which is a simple yet powerf

PyKale is a PyTorch library for multimodal learning and transfer learning as well as deep learning and dimensionality reduction on graphs, images, texts, and videos. By adopting a unified pipeline-based API design, PyKale enforces standardization and minimalism, via reusing existing resources, reducing repetitions and redundancy, and recycling learning models across areas.

🏅 Collection of Kaggle Solutions and Ideas 🏅

This page contains a list of papers on multi-task learning for computer vision. Please create a pull request if you wish to add anything. If you are interested, consider reading our recent survey paper.

Cockpit is a visual and statistical debugger specifically designed for deep learning!

Human Attention for Text Classification Re-implementation of the paper Human Attention Maps for Text Classification: Do Humans and Neural Networks Foc

(Comet-) ATOMIC 2020: On Symbolic and Neural Commonsense Knowledge Graphs Paper Jena D. Hwang, Chandra Bhagavatula, Ronan Le Bras, Jeff Da, Keisuke Sa

Does Unsupervised Architecture Representation Learning Help Neural Architecture Search? Code for paper: Does Unsupervised Architecture Representation

Instantly turn your Python functions into production-ready microservices. Deploy and access your services via HTTP API or interactive UI. Seamlessly export your services into portable, shareable, and executable files or Docker images.

SAT Style Augmented Translation Introduction By collecting high-quality data, we were able to train a model that outperforms Google Translate on 6 dif

This repository contains code to train and test GNN models for recommendation, mainly using the Deep Graph Library (DGL).

OCTIS : Optimizing and Comparing Topic Models is Simple! OCTIS (Optimizing and Comparing Topic models Is Simple) aims at training, analyzing and compa

The dataTap Python library is the primary interface for using dataTap's rich data management tools. Create datasets, stream annotations, and analyze model performance all with one library.

NeRF--: Neural Radiance Fields Without Known Camera Parameters Project Page | Arxiv | Colab Notebook | Data Zirui Wang¹, Shangzhe Wu², Weidi Xie², Min

GraphMask This repository contains an implementation of GraphMask, the interpretability technique for graph neural networks presented in our ICLR 2021

Labelled data remains a scarce resource in many practical NLP scenarios. This is especially the case when working with resource-poor languages (or text domains), or when using task-specific labels without pre-existing datasets. The only available option is often to collect and annotate texts by hand, which is expensive and time-consuming.

Simple but maybe too simple config management through python data classes. We use it for machine learning.

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

Parallelized symbolic regression built on Julia, and interfaced by Python. Uses regularized evolution, simulated annealing, and gradient-free optimization.