2838 Repositories
Python neural-topic-models Libraries
Petastorm library enables single machine or distributed training and evaluation of deep learning models from datasets in Apache Parquet format. It supports ML frameworks such as Tensorflow, Pytorch, and PySpark and can be used from pure Python code.
Petastorm Contents Petastorm Installation Generating a dataset Plain Python API Tensorflow API Pytorch API Spark Dataset Converter API Analyzing petas
Distributed Deep learning with Keras & Spark
Elephas: Distributed Deep Learning with Keras & Spark Elephas is an extension of Keras, which allows you to run distributed deep learning models at sc
BigDL: Distributed Deep Learning Framework for Apache Spark
BigDL: Distributed Deep Learning on Apache Spark What is BigDL? BigDL is a distributed deep learning library for Apache Spark; with BigDL, users can w
AtsPy: Automated Time Series Models in Python (by @firmai)
Automated Time Series Models in Python (AtsPy) SSRN Report Easily develop state of the art time series models to forecast univariate data series. Simp
Automatically build ARIMA, SARIMAX, VAR, FB Prophet and XGBoost Models on Time Series data sets with a Single Line of Code. Now updated with Dask to handle millions of rows.
Auto_TS: Auto_TimeSeries Automatically build multiple Time Series models using a Single Line of Code. Now updated with Dask. Auto_timeseries is a comp
Time series forecasting with PyTorch
Our article on Towards Data Science introduces the package and provides background information. Pytorch Forecasting aims to ease state-of-the-art time
A python library for easy manipulation and forecasting of time series.
Time Series Made Easy in Python darts is a python library for easy manipulation and forecasting of time series. It contains a variety of models, from
Probabilistic time series modeling in Python
GluonTS - Probabilistic Time Series Modeling in Python GluonTS is a Python toolkit for probabilistic time series modeling, built around Apache MXNet (
A statistical library designed to fill the void in Python's time series analysis capabilities, including the equivalent of R's auto.arima function.
pmdarima Pmdarima (originally pyramid-arima, for the anagram of 'py' + 'arima') is a statistical library designed to fill the void in Python's time se
ARCH models in Python
arch Autoregressive Conditional Heteroskedasticity (ARCH) and other tools for financial econometrics, written in Python (with Cython and/or Numba used
Qlib is an AI-oriented quantitative investment platform, which aims to realize the potential, empower the research, and create the value of AI technologies in quantitative investment. With Qlib, you can easily try your ideas to create better Quant investment strategies.
Qlib is an AI-oriented quantitative investment platform, which aims to realize the potential, empower the research, and create the value of AI technol
An easier way to build neural search on the cloud
An easier way to build neural search on the cloud Jina is a deep learning-powered search framework for building cross-/multi-modal search systems (e.g
Source code, datasets and trained models for the paper Learning Advanced Mathematical Computations from Examples (ICLR 2021), by François Charton, Amaury Hayat (ENPC-Rutgers) and Guillaume Lample
Maths from examples - Learning advanced mathematical computations from examples This is the source code and data sets relevant to the paper Learning a
Diverse Image Captioning with Context-Object Split Latent Spaces (NeurIPS 2020)
Diverse Image Captioning with Context-Object Split Latent Spaces This repository is the PyTorch implementation of the paper: Diverse Image Captioning
Open-L2O: A Comprehensive and Reproducible Benchmark for Learning to Optimize Algorithms
Open-L2O This repository establishes the first comprehensive benchmark efforts of existing learning to optimize (L2O) approaches on a number of proble
BossNAS: Exploring Hybrid CNN-transformers with Block-wisely Self-supervised Neural Architecture Search
BossNAS This repository contains PyTorch evaluation code, retraining code and pretrained models of our paper: BossNAS: Exploring Hybrid CNN-transforme
CDIoU and CDIoU loss is like a convenient plug-in that can be used in multiple models. CDIoU and CDIoU loss have different excellent performances in several models such as Faster R-CNN, YOLOv4, RetinaNet and . There is a maximum AP improvement of 1.9% and an average AP of 0.8% improvement on MS COCO dataset, compared to traditional evaluation-feedback modules. Here we just use as an example to illustrate the code.
CDIoU-CDIoUloss CDIoU and CDIoU loss is like a convenient plug-in that can be used in multiple models. CDIoU and CDIoU loss have different excellent p
⚡ boost inference speed of T5 models by 5x & reduce the model size by 3x using fastT5.
Reduce T5 model size by 3X and increase the inference speed up to 5X. Install Usage Details Functionalities Benchmarks Onnx model Quantized onnx model
Optimising chemical reactions using machine learning
Summit Summit is a set of tools for optimising chemical processes. We’ve started by targeting reactions. What is Summit? Currently, reaction optimisat
An implementation of Geoffrey Hinton's paper "How to represent part-whole hierarchies in a neural network" in Pytorch.
GLOM An implementation of Geoffrey Hinton's paper "How to represent part-whole hierarchies in a neural network" for MNIST Dataset. To understand this
Code for our paper at ECCV 2020: Post-Training Piecewise Linear Quantization for Deep Neural Networks
PWLQ Updates 2020/07/16 - We are working on getting permission from our institution to release our source code. We will release it once we are granted
Code accompanying "Dynamic Neural Relational Inference" from CVPR 2020
Code accompanying "Dynamic Neural Relational Inference" This codebase accompanies the paper "Dynamic Neural Relational Inference" from CVPR 2020. This
Code release for NeX: Real-time View Synthesis with Neural Basis Expansion
NeX: Real-time View Synthesis with Neural Basis Expansion Project Page | Video | Paper | COLAB | Shiny Dataset We present NeX, a new approach to novel
Towards Ultra-Resolution Neural Style Transfer via Thumbnail Instance Normalization
Towards Ultra-Resolution Neural Style Transfer via Thumbnail Instance Normalization Official PyTorch implementation for our URST (Ultra-Resolution Sty
Monocular Depth Estimation - Weighted-average prediction from multiple pre-trained depth estimation models
merged_depth runs (1) AdaBins, (2) DiverseDepth, (3) MiDaS, (4) SGDepth, and (5) Monodepth2, and calculates a weighted-average per-pixel absolute dept
Repository providing a wide range of self-supervised pretrained models for computer vision tasks.
Hierarchical Pretraining: Research Repository This is a research repository for reproducing the results from the project "Self-supervised pretraining
Official code for the ICLR 2021 paper Neural ODE Processes
Neural ODE Processes Official code for the paper Neural ODE Processes (ICLR 2021). Abstract Neural Ordinary Differential Equations (NODEs) use a neura
Code release for NeX: Real-time View Synthesis with Neural Basis Expansion
NeX: Real-time View Synthesis with Neural Basis Expansion Project Page | Video | Paper | COLAB | Shiny Dataset We present NeX, a new approach to novel
We have implemented shaDow-GNN as a general and powerful pipeline for graph representation learning. For more details, please find our paper titled Deep Graph Neural Networks with Shallow Subgraph Samplers, available on arXiv (https//arxiv.org/abs/2012.01380).
Deep GNN, Shallow Sampling Hanqing Zeng, Muhan Zhang, Yinglong Xia, Ajitesh Srivastava, Andrey Malevich, Rajgopal Kannan, Viktor Prasanna, Long Jin, R
Style-based Point Generator with Adversarial Rendering for Point Cloud Completion (CVPR 2021)
Style-based Point Generator with Adversarial Rendering for Point Cloud Completion (CVPR 2021) An efficient PyTorch library for Point Cloud Completion.
Convolutional Recurrent Neural Networks(CRNN) for Scene Text Recognition
CRNN_Tensorflow This is a TensorFlow implementation of a Deep Neural Network for scene text recognition. It is mainly based on the paper "An End-to-En
A general list of resources to image text localization and recognition 场景文本位置感知与识别的论文资源与实现合集 シーンテキストの位置認識と識別のための論文リソースの要約
Scene Text Localization & Recognition Resources Read this institute-wise: English, 简体中文. Read this year-wise: English, 简体中文. Tags: [STL] (Scene Text L
OCR engine for all the languages
Description kraken is a turn-key OCR system optimized for historical and non-Latin script material. kraken's main features are: Fully trainable layout
This is the implementation of the paper "Gated Recurrent Convolution Neural Network for OCR"
Gated Recurrent Convolution Neural Network for OCR This project is an implementation of the GRCNN for OCR. For details, please refer to the paper: htt
Tensorflow-based CNN+LSTM trained with CTC-loss for OCR
Overview This collection demonstrates how to construct and train a deep, bidirectional stacked LSTM using CNN features as input with CTC loss to perfo
TextBoxes: A Fast Text Detector with a Single Deep Neural Network https://github.com/MhLiao/TextBoxes 基于SSD改进的文本检测算法,textBoxes_note记录了之前整理的笔记。
TextBoxes: A Fast Text Detector with a Single Deep Neural Network Introduction This paper presents an end-to-end trainable fast scene text detector, n
Packaged, Pytorch-based, easy to use, cross-platform version of the CRAFT text detector
CRAFT: Character-Region Awareness For Text detection Packaged, Pytorch-based, easy to use, cross-platform version of the CRAFT text detector | Paper |
Code for the paper STN-OCR: A single Neural Network for Text Detection and Text Recognition
STN-OCR: A single Neural Network for Text Detection and Text Recognition This repository contains the code for the paper: STN-OCR: A single Neural Net
The code of "Mask TextSpotter: An End-to-End Trainable Neural Network for Spotting Text with Arbitrary Shapes"
Mask TextSpotter A Pytorch implementation of Mask TextSpotter along with its extension can be find here Introduction This is the official implementati
Table recognition inside douments using neural networks
TableTrainNet A simple project for training and testing table recognition in documents. This project was developed to make a neural network which reco
Use Convolutional Recurrent Neural Network to recognize the Handwritten line text image without pre segmentation into words or characters. Use CTC loss Function to train.
Handwritten Line Text Recognition using Deep Learning with Tensorflow Description Use Convolutional Recurrent Neural Network to recognize the Handwrit
Handwritten Text Recognition (HTR) system implemented with TensorFlow.
Handwritten Text Recognition with TensorFlow Update 2021: more robust model, faster dataloader, word beam search decoder also available for Windows Up
Handwritten Number Recognition using CNN and Character Segmentation
Handwritten-Number-Recognition-With-Image-Segmentation Info About this repository This Repository is aimed at reading handwritten images of numbers an
Handwriting Recognition System based on a deep Convolutional Recurrent Neural Network architecture
Handwriting Recognition System This repository is the Tensorflow implementation of the Handwriting Recognition System described in Handwriting Recogni
This repository lets you train neural networks models for performing end-to-end full-page handwriting recognition using the Apache MXNet deep learning frameworks on the IAM Dataset.
Handwritten Text Recognition (OCR) with MXNet Gluon These notebooks have been created by Jonathan Chung, as part of his internship as Applied Scientis
Page to PAGE Layout Analysis Tool
P2PaLA Page to PAGE Layout Analysis (P2PaLA) is a toolkit for Document Layout Analysis based on Neural Networks. 💥 Try our new DEMO for online baseli
Supplementary code for the paper "Meta-Solver for Neural Ordinary Differential Equations" https://arxiv.org/abs/2103.08561
Meta-Solver for Neural Ordinary Differential Equations Towards robust neural ODEs using parametrized solvers. Main idea Each Runge-Kutta (RK) solver w
TensorFlow code for the neural network presented in the paper: "Structural Language Models of Code" (ICML'2020)
SLM: Structural Language Models of Code This is an official implementation of the model described in: "Structural Language Models of Code" [PDF] To ap
Code for "Neural Parts: Learning Expressive 3D Shape Abstractions with Invertible Neural Networks", CVPR 2021
Neural Parts: Learning Expressive 3D Shape Abstractions with Invertible Neural Networks This repository contains the code that accompanies our CVPR 20
中文语音识别系列,读者可以借助它快速训练属于自己的中文语音识别模型,或直接使用预训练模型测试效果。
MASR中文语音识别(pytorch版) 开箱即用 自行训练 使用与训练分离(增量训练) 识别率高 说明:因为每个人电脑机器不同,而且有些安装包安装起来比较麻烦,强烈建议直接用我编译好的docker环境跑 目前docker基础环境为ubuntu-cuda10.1-cudnn7-pytorch1.6.
CVPR 2021: "Generating Diverse Structure for Image Inpainting With Hierarchical VQ-VAE"
Diverse Structure Inpainting ArXiv | Papar | Supplementary Material | BibTex This repository is for the CVPR 2021 paper, "Generating Diverse Structure
[CIKM 2019] Code and dataset for "Fi-GNN: Modeling Feature Interactions via Graph Neural Networks for CTR Prediction"
FiGNN for CTR prediction The code and data for our paper in CIKM2019: Fi-GNN: Modeling Feature Interactions via Graph Neural Networks for CTR Predicti
(under submission) Bayesian Integration of a Generative Prior for Image Restoration
BIGPrior: Towards Decoupling Learned Prior Hallucination and Data Fidelity in Image Restoration Authors: Majed El Helou, and Sabine Süsstrunk {Note: p
A spherical CNN for weather forecasting
DeepSphere-Weather - Deep Learning on the sphere for weather/climate applications. The code in this repository provides a scalable and flexible framew
code for "AttentiveNAS Improving Neural Architecture Search via Attentive Sampling"
AttentiveNAS: Improving Neural Architecture Search via Attentive Sampling This repository contains PyTorch evaluation code, training code and pretrain
Implicit Graph Neural Networks
Implicit Graph Neural Networks This repository is the official PyTorch implementation of "Implicit Graph Neural Networks". Fangda Gu*, Heng Chang*, We
[ICLR 2021] HW-NAS-Bench: Hardware-Aware Neural Architecture Search Benchmark
HW-NAS-Bench: Hardware-Aware Neural Architecture Search Benchmark Accepted as a spotlight paper at ICLR 2021. Table of content File structure Prerequi
Simple command line tool for text to image generation using OpenAI's CLIP and Siren (Implicit neural representation network)
Simple command line tool for text to image generation using OpenAI's CLIP and Siren (Implicit neural representation network)
A curated list of neural network pruning resources.
A curated list of neural network pruning and related resources. Inspired by awesome-deep-vision, awesome-adversarial-machine-learning, awesome-deep-learning-papers and Awesome-NAS.
Learning Intents behind Interactions with Knowledge Graph for Recommendation, WWW2021
Learning Intents behind Interactions with Knowledge Graph for Recommendation This is our PyTorch implementation for the paper: Xiang Wang, Tinglin Hua
Deep generative modeling for time-stamped heterogeneous data, enabling high-fidelity models for a large variety of spatio-temporal domains.
Neural Spatio-Temporal Point Processes [arxiv] Ricky T. Q. Chen, Brandon Amos, Maximilian Nickel Abstract. We propose a new class of parameterizations
🦉Data Version Control | Git for Data & Models
Website • Docs • Blog • Twitter • Chat (Community & Support) • Tutorial • Mailing List Data Version Control or DVC is an open-source tool for data sci
Code for "Infinitely Deep Bayesian Neural Networks with Stochastic Differential Equations"
Infinitely Deep Bayesian Neural Networks with SDEs This library contains JAX and Pytorch implementations of neural ODEs and Bayesian layers for stocha
This project is the official implementation of our accepted ICLR 2021 paper BiPointNet: Binary Neural Network for Point Clouds.
BiPointNet: Binary Neural Network for Point Clouds Created by Haotong Qin, Zhongang Cai, Mingyuan Zhang, Yifu Ding, Haiyu Zhao, Shuai Yi, Xianglong Li
Official implementation of Self-supervised Graph Attention Networks (SuperGAT), ICLR 2021.
SuperGAT Official implementation of Self-supervised Graph Attention Networks (SuperGAT). This model is presented at How to Find Your Friendly Neighbor
Code for ICLR 2021 Paper, "Anytime Sampling for Autoregressive Models via Ordered Autoencoding"
Anytime Autoregressive Model Anytime Sampling for Autoregressive Models via Ordered Autoencoding , ICLR 21 Yilun Xu, Yang Song, Sahaj Gara, Linyuan Go
[CVPR 2021] Involution: Inverting the Inherence of Convolution for Visual Recognition, a brand new neural operator
involution Official implementation of a neural operator as described in Involution: Inverting the Inherence of Convolution for Visual Recognition (CVP
Beyond the Imitation Game collaborative benchmark for enormous language models
BIG-bench 🪑 The Beyond the Imitation Game Benchmark (BIG-bench) will be a collaborative benchmark intended to probe large language models, and extrap
A complete end-to-end demonstration in which we collect training data in Unity and use that data to train a deep neural network to predict the pose of a cube. This model is then deployed in a simulated robotic pick-and-place task.
Object Pose Estimation Demo This tutorial will go through the steps necessary to perform pose estimation with a UR3 robotic arm in Unity. You’ll gain
Scripts of Machine Learning Algorithms from Scratch. Implementations of machine learning models and algorithms using nothing but NumPy with a focus on accessibility. Aims to cover everything from basic to advance.
Algo-ScriptML Python implementations of some of the fundamental Machine Learning models and algorithms from scratch. The goal of this project is not t
Django project starter on steroids: quickly create a Django app AND generate source code for data models + REST/GraphQL APIs (the generated code is auto-linted and has 100% test coverage).
Create Django App 💛 We're a Django project starter on steroids! One-line command to create a Django app with all the dependencies auto-installed AND
Code associated with the "Data Augmentation using Pre-trained Transformer Models" paper
Data Augmentation using Pre-trained Transformer Models Code associated with the Data Augmentation using Pre-trained Transformer Models paper Code cont
MMdnn is a set of tools to help users inter-operate among different deep learning frameworks. E.g. model conversion and visualization. Convert models between Caffe, Keras, MXNet, Tensorflow, CNTK, PyTorch Onnx and CoreML.
MMdnn MMdnn is a comprehensive and cross-framework tool to convert, visualize and diagnose deep learning (DL) models. The "MM" stands for model manage
Open standard for machine learning interoperability
Open Neural Network Exchange (ONNX) is an open ecosystem that empowers AI developers to choose the right tools as their project evolves. ONNX provides
PArallel Distributed Deep LEarning: Machine Learning Framework from Industrial Practice (『飞桨』核心框架,深度学习&机器学习高性能单机、分布式训练和跨平台部署)
English | 简体中文 Welcome to the PaddlePaddle GitHub. PaddlePaddle, as the only independent R&D deep learning platform in China, has been officially open
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
A library for hidden semi-Markov models with explicit durations
hsmmlearn hsmmlearn is a library for unsupervised learning of hidden semi-Markov models with explicit durations. It is a port of the hsmm package for
A bare-bones TensorFlow framework for Bayesian deep learning and Gaussian process approximation
Aboleth A bare-bones TensorFlow framework for Bayesian deep learning and Gaussian process approximation [1] with stochastic gradient variational Bayes
Scikit-learn compatible estimation of general graphical models
skggm : Gaussian graphical models using the scikit-learn API In the last decade, learning networks that encode conditional independence relationships
Bayesian dessert for Lasagne
Gelato Bayesian dessert for Lasagne Recent results in Bayesian statistics for constructing robust neural networks have proved that it is one of the be
InferPy: Deep Probabilistic Modeling with Tensorflow Made Easy
InferPy: Deep Probabilistic Modeling Made Easy InferPy is a high-level API for probabilistic modeling written in Python and capable of running on top
Deep Reinforcement Learning for Keras.
Deep Reinforcement Learning for Keras What is it? keras-rl implements some state-of-the art deep reinforcement learning algorithms in Python and seaml
Visualizer for neural network, deep learning, and machine learning models
Netron is a viewer for neural network, deep learning and machine learning models. Netron supports ONNX (.onnx, .pb, .pbtxt), Keras (.h5, .keras), Tens
A collection of infrastructure and tools for research in neural network interpretability.
Lucid Lucid is a collection of infrastructure and tools for research in neural network interpretability. We're not currently supporting tensorflow 2!
Interpretability and explainability of data and machine learning models
AI Explainability 360 (v0.2.1) The AI Explainability 360 toolkit is an open-source library that supports interpretability and explainability of datase
Python Library for Model Interpretation/Explanations
Skater Skater is a unified framework to enable Model Interpretation for all forms of model to help one build an Interpretable machine learning system
FairML - is a python toolbox auditing the machine learning models for bias.
======== FairML: Auditing Black-Box Predictive Models FairML is a python toolbox auditing the machine learning models for bias. Description Predictive
Algorithms for monitoring and explaining machine learning models
Alibi is an open source Python library aimed at machine learning model inspection and interpretation. The focus of the library is to provide high-qual
Transfer Learning library for Deep Neural Networks.
Transfer and meta-learning in Python Each folder in this repository corresponds to a method or tool for transfer/meta-learning. xfer-ml is a standalon
Gluon CV Toolkit
Gluon CV Toolkit | Installation | Documentation | Tutorials | GluonCV provides implementations of the state-of-the-art (SOTA) deep learning models in
A clear, concise, simple yet powerful and efficient API for deep learning.
The Gluon API Specification The Gluon API specification is an effort to improve speed, flexibility, and accessibility of deep learning technology for
QKeras: a quantization deep learning library for Tensorflow Keras
QKeras github.com/google/qkeras QKeras 0.8 highlights: Automatic quantization using QKeras; Stochastic behavior (including stochastic rouding) is disa
Graph Neural Networks with Keras and Tensorflow 2.
Welcome to Spektral Spektral is a Python library for graph deep learning, based on the Keras API and TensorFlow 2. The main goal of this project is to
Distributed Deep learning with Keras & Spark
Elephas: Distributed Deep Learning with Keras & Spark Elephas is an extension of Keras, which allows you to run distributed deep learning models at sc
Keras community contributions
keras-contrib : Keras community contributions Keras-contrib is deprecated. Use TensorFlow Addons. The future of Keras-contrib: We're migrating to tens
Ludwig is a toolbox that allows to train and evaluate deep learning models without the need to write code.
Translated in 🇰🇷 Korean/ Ludwig is a toolbox that allows users to train and test deep learning models without the need to write code. It is built on
📝 Wrapper library for text generation / language models at char and word level with RNN in TensorFlow
tensorlm Generate Shakespeare poems with 4 lines of code. Installation tensorlm is written in / for Python 3.4+ and TensorFlow 1.1+ pip3 install tenso
A Neural Net Training Interface on TensorFlow, with focus on speed + flexibility
Tensorpack is a neural network training interface based on TensorFlow. Features: It's Yet Another TF high-level API, with speed, and flexibility built
Deep Learning and Reinforcement Learning Library for Scientists and Engineers 🔥
TensorLayer is a novel TensorFlow-based deep learning and reinforcement learning library designed for researchers and engineers. It provides an extens
Geometric Deep Learning Extension Library for PyTorch
Documentation | Paper | Colab Notebooks | External Resources | OGB Examples PyTorch Geometric (PyG) is a geometric deep learning extension library for