9397 Repositories
Python Sentiment-analysis-using-XLnet-Deep-learning-in-natural-language-processing- Libraries
CrayLabs and user contibuted examples of using SmartSim for various simulation and machine learning applications.
SmartSim Example Zoo This repository contains CrayLabs and user contibuted examples of using SmartSim for various simulation and machine learning appl
Composing methods for ML training efficiency
MosaicML Composer contains a library of methods, and ways to compose them together for more efficient ML training.
A collection of papers about Transformer in the field of medical image analysis.
A collection of papers about Transformer in the field of medical image analysis.
Unofficial Implementation of MLP-Mixer, gMLP, resMLP, Vision Permutator, S2MLPv2, RaftMLP, ConvMLP, ConvMixer in Jittor and PyTorch.
Unofficial Implementation of MLP-Mixer, gMLP, resMLP, Vision Permutator, S2MLPv2, RaftMLP, ConvMLP, ConvMixer in Jittor and PyTorch! Now, Rearrange and Reduce in einops.layers.jittor are support!!
GUI for a Vocal Remover that uses Deep Neural Networks.
GUI for a Vocal Remover that uses Deep Neural Networks.
A style-based Quantum Generative Adversarial Network
Style-qGAN A style based Quantum Generative Adversarial Network (style-qGAN) model for Monte Carlo event generation. Tutorial We have prepared a noteb
SoGCN: Second-Order Graph Convolutional Networks
SoGCN: Second-Order Graph Convolutional Networks This is the authors' implementation of paper "SoGCN: Second-Order Graph Convolutional Networks" in Py
Implementation of "DeepOrder: Deep Learning for Test Case Prioritization in Continuous Integration Testing".
DeepOrder Implementation of DeepOrder for the paper "DeepOrder: Deep Learning for Test Case Prioritization in Continuous Integration Testing". Project
Using contrastive learning and OpenAI's CLIP to find good embeddings for images with lossy transformations
Creating Robust Representations from Pre-Trained Image Encoders using Contrastive Learning Sriram Ravula, Georgios Smyrnis This is the code for our pr
Human4D Dataset tools for processing and visualization
HUMAN4D: A Human-Centric Multimodal Dataset for Motions & Immersive Media HUMAN4D constitutes a large and multimodal 4D dataset that contains a variet
Analysis of rationale selection in neural rationale models
Neural Rationale Interpretability Analysis We analyze the neural rationale models proposed by Lei et al. (2016) and Bastings et al. (2019), as impleme
novel deep learning research works with PaddlePaddle
Research 发布基于飞桨的前沿研究工作,包括CV、NLP、KG、STDM等领域的顶会论文和比赛冠军模型。 目录 计算机视觉(Computer Vision) 自然语言处理(Natrual Language Processing) 知识图谱(Knowledge Graph) 时空数据挖掘(Spa
DeepMoCap: Deep Optical Motion Capture using multiple Depth Sensors and Retro-reflectors
DeepMoCap: Deep Optical Motion Capture using multiple Depth Sensors and Retro-reflectors By Anargyros Chatzitofis, Dimitris Zarpalas, Stefanos Kollias
Semi-supervised Transfer Learning for Image Rain Removal. In CVPR 2019.
Semi-supervised Transfer Learning for Image Rain Removal This package contains the Python implementation of "Semi-supervised Transfer Learning for Ima
PyTorch version of the paper 'Enhanced Deep Residual Networks for Single Image Super-Resolution' (CVPRW 2017)
About PyTorch 1.2.0 Now the master branch supports PyTorch 1.2.0 by default. Due to the serious version problem (especially torch.utils.data.dataloade
Code repo for "Cross-Scale Internal Graph Neural Network for Image Super-Resolution" (NeurIPS'20)
IGNN Code repo for "Cross-Scale Internal Graph Neural Network for Image Super-Resolution" [paper] [supp] Prepare datasets 1 Download training dataset
Python implementation of "Single Image Haze Removal Using Dark Channel Prior"
##Dependencies pillow(~2.6.0) Numpy(~1.9.0) If the scripts throw AttributeError: __float__, make sure your pillow has jpeg support e.g. try: $ sudo ap
Pytorch implementation of the paper: "A Unified Framework for Separating Superimposed Images", in CVPR 2020.
Deep Adversarial Decomposition PDF | Supp | 1min-DemoVideo Pytorch implementation of the paper: "Deep Adversarial Decomposition: A Unified Framework f
Density-aware Single Image De-raining using a Multi-stream Dense Network (CVPR 2018)
DID-MDN Density-aware Single Image De-raining using a Multi-stream Dense Network He Zhang, Vishal M. Patel [Paper Link] (CVPR'18) We present a novel d
Single Image Deraining Using Bilateral Recurrent Network (TIP 2020)
Single Image Deraining Using Bilateral Recurrent Network Introduction Single image deraining has received considerable progress based on deep convolut
Benchmark datasets, data loaders, and evaluators for graph machine learning
Overview The Open Graph Benchmark (OGB) is a collection of benchmark datasets, data loaders, and evaluators for graph machine learning. Datasets cover
official implementation for the paper "Simplifying Graph Convolutional Networks"
Simplifying Graph Convolutional Networks Updates As pointed out by #23, there was a subtle bug in our preprocessing code for the reddit dataset. After
Attention over nodes in Graph Neural Networks using PyTorch (NeurIPS 2019)
Intro This repository contains code to generate data and reproduce experiments from our NeurIPS 2019 paper: Boris Knyazev, Graham W. Taylor, Mohamed R
IsoGCN code for ICLR2021
IsoGCN The official implementation of IsoGCN, presented in the ICLR2021 paper Isometric Transformation Invariant and Equivariant Graph Convolutional N
PyTorch implementation of "Simple and Deep Graph Convolutional Networks"
Simple and Deep Graph Convolutional Networks This repository contains a PyTorch implementation of "Simple and Deep Graph Convolutional Networks".(http
Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks
Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks This repository contains a TensorFlow implementation of "
[ICML 2020] "When Does Self-Supervision Help Graph Convolutional Networks?" by Yuning You, Tianlong Chen, Zhangyang Wang, Yang Shen
When Does Self-Supervision Help Graph Convolutional Networks? PyTorch implementation for When Does Self-Supervision Help Graph Convolutional Networks?
This is a Pytorch implementation of paper: DropEdge: Towards Deep Graph Convolutional Networks on Node Classification
DropEdge: Towards Deep Graph Convolutional Networks on Node Classification This is a Pytorch implementation of paper: DropEdge: Towards Deep Graph Con
Repository for benchmarking graph neural networks
Benchmarking Graph Neural Networks Updates Nov 2, 2020 Project based on DGL 0.4.2. See the relevant dependencies defined in the environment yml files
Tensorflow Repo for "DeepGCNs: Can GCNs Go as Deep as CNNs?"
DeepGCNs: Can GCNs Go as Deep as CNNs? In this work, we present new ways to successfully train very deep GCNs. We borrow concepts from CNNs, mainly re
Densely Connected Convolutional Networks, In CVPR 2017 (Best Paper Award).
Densely Connected Convolutional Networks (DenseNets) This repository contains the code for DenseNet introduced in the following paper Densely Connecte
Deep Residual Networks with 1K Layers
Deep Residual Networks with 1K Layers By Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun. Microsoft Research Asia (MSRA). Table of Contents Introduc
A simple tool for searching images inside a local folder with text/image input using CLIP
clip-search (WIP) A simple tool for searching images inside a local folder with text/image input using CLIP 10 results for "a blonde woman" in a folde
A torch.Tensor-like DataFrame library supporting multiple execution runtimes and Arrow as a common memory format
TorchArrow (Warning: Unstable Prototype) This is a prototype library currently under heavy development. It does not currently have stable releases, an
Supervision Exists Everywhere: A Data Efficient Contrastive Language-Image Pre-training Paradigm
DeCLIP Supervision Exists Everywhere: A Data Efficient Contrastive Language-Image Pre-training Paradigm. Our paper is available in arxiv Updates ** Ou
Python and OpenCV-based scene cut/transition detection program & library.
Video Scene Cut Detection and Analysis Tool Latest Release: v0.5.6.1 (October 11, 2021) Main Webpage: py.scenedetect.com Documentation: manual.scenede
Collection of Docker images for ML/DL and video processing projects
Collection of Docker images for ML/DL and video processing projects. Overview of images Three types of images differ by tag postfix: base: Python with
A python program which converts images and video into excel spreadsheets.
image2excel A program which converts images and video into Excel spreadsheets. Usage examples can be found in examples Videos can take a long time to
An implementation of Video Frame Interpolation via Adaptive Separable Convolution using PyTorch
This work has now been superseded by: https://github.com/sniklaus/revisiting-sepconv sepconv-slomo This is a reference implementation of Video Frame I
RIFE: Real-Time Intermediate Flow Estimation for Video Frame Interpolation
RIFE - Real Time Video Interpolation arXiv | YouTube | Colab | Tutorial | Demo Table of Contents Introduction Collection Usage Evaluation Training and
AutoSub is a CLI application to generate subtitle files (.srt, .vtt, and .txt transcript) for any video file using Mozilla DeepSpeech.
AutoSub About Motivation Installation Docker How-to example How it works TO-DO Contributing References About AutoSub is a CLI application to generate
Youtube Video Downloader Using Python Gui Appliction with progress Bar
Youtube-Video-Downloader Youtube Video Downloader Using Python Gui Appliction with progress Bar Module Used Pytube Tkinter Pil Urllib Bytes Io LICENSE
This Bot Can Upload Video from Link Of Pdisk to Pdisk using its API. @PredatorHackerzZ
𝐏𝐝𝐢𝐬𝐤 𝐂𝐨𝐧𝐯𝐞𝐫𝐭𝐞𝐫 𝐁𝐨𝐭 Make short link by using 𝐏𝐝𝐢𝐬𝐤 API key Installation 𝐓𝐡𝐞 𝐄𝐚𝐬𝐲 𝐖𝐚𝐲 𝐑𝐞𝐪𝐮𝐢𝐫𝐞𝐝 𝐕𝐚𝐫𝐢𝐚𝐛𝐥𝐞
Youtube Downloader is a Graphic User Interface(GUI) that lets users download a Youtube Video or Audio through a URL
Youtube Downloader This Python and Tkinter based GUI allows users to directly download the Best Resolution Videos and Audios from Youtube. Pa-fy Insta
Face Mask Detection on Image and Video using tensorflow and keras
Face-Mask-Detection Face Mask Detection on Image and Video using tensorflow and keras Train Neural Network on face-mask dataset using tensorflow and k
an implementation of Revisiting Adaptive Convolutions for Video Frame Interpolation using PyTorch
revisiting-sepconv This is a reference implementation of Revisiting Adaptive Convolutions for Video Frame Interpolation [1] using PyTorch. Given two f
Sudoku solver using backtracking
Sudoku solver Sudoku solver using backtracking Basically in sudoku, we want to be able to solve a sudoku puzzle given an input like this, which repres
Bittensor - an open, decentralized, peer-to-peer network that functions as a market system for the development of artificial intelligence
At Bittensor, we are creating an open, decentralized, peer-to-peer network that functions as a market system for the development of artificial intelligence.
DataPrep — The easiest way to prepare data in Python
DataPrep — The easiest way to prepare data in Python
Solutions of Reinforcement Learning 2nd Edition
Solutions of Reinforcement Learning, An Introduction
LibreLingo🐢 🌎 📚 a community-owned language-learning platform
LibreLingo's mission is to create a modern language-learning platform that is owned by the community of its users. All software is licensed under AGPLv3, which guarantees the freedom to run, study, share, and modify the software. Course authors are encouraged to release their courses with free licenses.
A JAX implementation of Broaden Your Views for Self-Supervised Video Learning, or BraVe for short.
BraVe This is a JAX implementation of Broaden Your Views for Self-Supervised Video Learning, or BraVe for short. The model provided in this package wa
Python Library for Signal/Image Data Analysis with Transport Methods
PyTransKit Python Transport Based Signal Processing Toolkit Website and documentation: https://pytranskit.readthedocs.io/ Installation The library cou
SynNet - synthetic tree generation using neural networks
SynNet This repo contains the code and analysis scripts for our amortized approach to synthetic tree generation using neural networks. Our model can s
BLEURT is a metric for Natural Language Generation based on transfer learning.
BLEURT: a Transfer Learning-Based Metric for Natural Language Generation BLEURT is an evaluation metric for Natural Language Generation. It takes a pa
Self-Supervised Speech Pre-training and Representation Learning Toolkit.
What's New Sep 2021: We host a challenge in AAAI workshop: The 2nd Self-supervised Learning for Audio and Speech Processing! See SUPERB official site
UniSpeech - Large Scale Self-Supervised Learning for Speech
UniSpeech The family of UniSpeech: UniSpeech (ICML 2021): Unified Pre-training for Self-Supervised Learning and Supervised Learning for ASR UniSpeech-
GraPE is a Rust/Python library for high-performance Graph Processing and Embedding.
GraPE GraPE (Graph Processing and Embedding) is a fast graph processing and embedding library, designed to scale with big graphs and to run on both of
PSML: A Multi-scale Time-series Dataset for Machine Learning in Decarbonized Energy Grids
PSML: A Multi-scale Time-series Dataset for Machine Learning in Decarbonized Energy Grids The electric grid is a key enabling infrastructure for the a
Code for WSDM 2022 paper, Contrastive Learning for Representation Degeneration Problem in Sequential Recommendation.
DuoRec Code for WSDM 2022 paper, Contrastive Learning for Representation Degeneration Problem in Sequential Recommendation. Usage Download datasets fr
The Rich Get Richer: Disparate Impact of Semi-Supervised Learning
The Rich Get Richer: Disparate Impact of Semi-Supervised Learning Preprocess file of the dataset used in implicit sub-populations: (Demographic groups
Reducing Information Bottleneck for Weakly Supervised Semantic Segmentation (NeurIPS 2021)
Reducing Information Bottleneck for Weakly Supervised Semantic Segmentation (NeurIPS 2021) The implementation of Reducing Infromation Bottleneck for W
Code for the preprint "Well-classified Examples are Underestimated in Classification with Deep Neural Networks"
This is a repository for the paper of "Well-classified Examples are Underestimated in Classification with Deep Neural Networks" The implementation and
Source code for paper "Deep Superpixel-based Network for Blind Image Quality Assessment"
DSN-IQA Source code for paper "Deep Superpixel-based Network for Blind Image Quality Assessment" Requirements Python =3.8.0 Pytorch =1.7.1 Usage wit
[SIGGRAPH 2021 Asia] DeepVecFont: Synthesizing High-quality Vector Fonts via Dual-modality Learning
DeepVecFont This is the official Pytorch implementation of the paper: Yizhi Wang and Zhouhui Lian. DeepVecFont: Synthesizing High-quality Vector Fonts
Deep Sea Treasure Environment for Multi-Objective Optimization Research
DeepSeaTreasure Environment Installation In order to get started with this environment, you can install it using the following command: python3 -m pip
Next-Best-View Estimation based on Deep Reinforcement Learning for Active Object Classification
next_best_view_rl Setup Clone the repository: git clone --recurse-submodules ... In 'third_party/zed-ros-wrapper': git checkout devel Install mujoco `
A cross-lingual COVID-19 fake news dataset
CrossFake An English-Chinese COVID-19 fake&real news dataset from the ICDMW 2021 paper below: Cross-lingual COVID-19 Fake News Detection. Jiangshu Du,
Codebase of deep learning models for inferring stability of mRNA molecules
Kaggle OpenVaccine Models Codebase of deep learning models for inferring stability of mRNA molecules, corresponding to the Kaggle Open Vaccine Challen
A fast implementation of bss_eval metrics for blind source separation
fast_bss_eval Do you have a zillion BSS audio files to process and it is taking days ? Is your simulation never ending ? Fear no more! fast_bss_eval i
This is an official pytorch implementation of Fast Fourier Convolution.
Fast Fourier Convolution (FFC) for Image Classification This is the official code of Fast Fourier Convolution for image classification on ImageNet. Ma
Direct design of biquad filter cascades with deep learning by sampling random polynomials.
IIRNet Direct design of biquad filter cascades with deep learning by sampling random polynomials. Usage git clone https://github.com/csteinmetz1/IIRNe
A high-level Python library for Quantum Natural Language Processing
lambeq About lambeq is a toolkit for quantum natural language processing (QNLP). Documentation: https://cqcl.github.io/lambeq/ Getting started Prerequ
Semi-Supervised Semantic Segmentation via Adaptive Equalization Learning, NeurIPS 2021 (Spotlight)
Semi-Supervised Semantic Segmentation via Adaptive Equalization Learning, NeurIPS 2021 (Spotlight) Abstract Due to the limited and even imbalanced dat
This is the implementation of "SELF SUPERVISED REPRESENTATION LEARNING WITH DEEP CLUSTERING FOR ACOUSTIC UNIT DISCOVERY FROM RAW SPEECH" submitted to ICASSP 2022
CPC_DeepCluster This is the implementation of "SELF SUPERVISED REPRESENTATION LEARNING WITH DEEP CLUSTERING FOR ACOUSTIC UNIT DISCOVERY FROM RAW SPEEC
Web-Scrapper using Python and Flask
Web-Scrapper "[초급]Python으로 웹 스크래퍼 만들기" 코스 -NomadCoders 기초적인 Python 문법강의부터 시작하여 웹사이트의 html파일에서 원하는 내용을 Scrapping해서 출력, csv 파일로 저장, flask를 이용한 간단한 웹페이지
Implementation of ICCV21 paper: PnP-DETR: Towards Efficient Visual Analysis with Transformers
Implementation of ICCV 2021 paper: PnP-DETR: Towards Efficient Visual Analysis with Transformers arxiv This repository is based on detr Recently, DETR
question‘s area recognition using image processing and regular expression
======================================== Paper-Question-recognition ======================================== question‘s area recognition using image p
Official implementation of "Motif-based Graph Self-Supervised Learning forMolecular Property Prediction"
Motif-based Graph Self-Supervised Learning for Molecular Property Prediction Official Pytorch implementation of NeurIPS'21 paper "Motif-based Graph Se
Source code for paper "Deep Superpixel-based Network for Blind Image Quality Assessment"
DSN-IQA Source code for paper "Deep Superpixel-based Network for Blind Image Quality Assessment" Requirements Python =3.8.0 Pytorch =1.7.1 Usage wit
Co-mining: Self-Supervised Learning for Sparsely Annotated Object Detection, AAAI 2021.
Co-mining: Self-Supervised Learning for Sparsely Annotated Object Detection This repository is an official implementation of the AAAI 2021 paper Co-mi
The easy way to combine mlflow, hydra and optuna into one machine learning pipeline.
mlflow_hydra_optuna_the_easy_way The easy way to combine mlflow, hydra and optuna into one machine learning pipeline. Objective TODO Usage 1. build do
Code & Data for the Paper "Time Masking for Temporal Language Models", WSDM 2022
Time Masking for Temporal Language Models This repository provides a reference implementation of the paper: Time Masking for Temporal Language Models
download NCERT books using scrapy
download_ncert_books download NCERT books using scrapy Downloading Books: You can either use the spider by cloning this repo and following the instruc
Using Selenium with Python to Web Scrap Popular Youtube Tech Channels.
Web Scrapping Popular Youtube Tech Channels with Selenium Data Mining, Data Wrangling, and Exploratory Data Analysis About the Data Web scrapi
A tool to easily scrape youtube data using the Google API
YouTube data scraper To easily scrape any data from the youtube homepage, a youtube channel/user, search results, playlists, and a single video itself
Universal Reddit Scraper - A comprehensive Reddit scraping command-line tool written in Python.
Universal Reddit Scraper - A comprehensive Reddit scraping command-line tool written in Python.
A PyTorch implementation of paper "Learning Shared Semantic Space for Speech-to-Text Translation", ACL (Findings) 2021
Chimera: Learning Shared Semantic Space for Speech-to-Text Translation This is a Pytorch implementation for the "Chimera" paper Learning Shared Semant
African language Speech Recognition - Speech-to-Text
Swahili-Speech-To-Text Table of Contents Swahili-Speech-To-Text Overview Scenario Approach Project Structure data: models: notebooks: scripts tests: l
DaDRA (day-druh) is a Python library for Data-Driven Reachability Analysis.
DaDRA (day-druh) is a Python library for Data-Driven Reachability Analysis. The main goal of the package is to accelerate the process of computing estimates of forward reachable sets for nonlinear dynamical systems.
Statistical Analysis 📈 focused on statistical analysis and exploration used on various data sets for personal and professional projects.
Statistical Analysis 📈 This repository focuses on statistical analysis and the exploration used on various data sets for personal and professional pr
A collection of learning outcomes data analysis using Python and SQL, from DQLab.
Data Analyst with PYTHON Data Analyst berperan dalam menghasilkan analisa data serta mempresentasikan insight untuk membantu proses pengambilan keputu
PyNHD is a part of HyRiver software stack that is designed to aid in watershed analysis through web services.
A part of HyRiver software stack that provides access to NHD+ V2 data through NLDI and WaterData web services
Python Package for DataHerb: create, search, and load datasets.
The Python Package for DataHerb A DataHerb Core Service to Create and Load Datasets.
PyPSA: Python for Power System Analysis
1 Python for Power System Analysis Contents 1 Python for Power System Analysis 1.1 About 1.2 Documentation 1.3 Functionality 1.4 Example scripts as Ju
A powerful data analysis package based on mathematical step functions. Strongly aligned with pandas.
The leading use-case for the staircase package is for the creation and analysis of step functions. Pretty exciting huh. But don't hit the close button
HyperSpy is an open source Python library for the interactive analysis of multidimensional datasets
HyperSpy is an open source Python library for the interactive analysis of multidimensional datasets that can be described as multidimensional arrays o
Additional tools for particle accelerator data analysis and machine information
PyLHC Tools This package is a collection of useful scripts and tools for the Optics Measurements and Corrections group (OMC) at CERN. Documentation Au
Toolchest provides APIs for scientific and bioinformatic data analysis.
Toolchest Python Client Toolchest provides APIs for scientific and bioinformatic data analysis. It allows you to abstract away the costliness of runni
A data analysis using python and pandas to showcase trends in school performance.
A data analysis using python and pandas to showcase trends in school performance. A data analysis to showcase trends in school performance using Panda