2881 Repositories
Python covid19-data-prediction Libraries
LinkML based SPARQL template library and execution engine
sparqlfun LinkML based SPARQL template library and execution engine modularized core library of SPARQL templates generic templates using common vocabs
Convert your JSON data to a valid Python object to allow accessing keys with the member access operator(.)
JSONObjectMapper Allows you to transform JSON data into an object whose members can be queried using the member access operator. Unlike json.dumps in
A benchmark of data-centric tasks from across the machine learning lifecycle.
A benchmark of data-centric tasks from across the machine learning lifecycle.
Code and real data for the paper "Counterfactual Temporal Point Processes", available at arXiv.
counterfactual-tpp This is a repository containing code and real data for the paper Counterfactual Temporal Point Processes. Pre-requisites This code
Source code for our paper "Do Not Trust Prediction Scores for Membership Inference Attacks"
Do Not Trust Prediction Scores for Membership Inference Attacks Abstract: Membership inference attacks (MIAs) aim to determine whether a specific samp
Official PyTorch implementation for "Low Precision Decentralized Distributed Training with Heterogenous Data"
Low Precision Decentralized Training with Heterogenous Data Official PyTorch implementation for "Low Precision Decentralized Distributed Training with
Bio-Computing Platform Featuring Large-Scale Representation Learning and Multi-Task Deep Learning “螺旋桨”生物计算工具集
English | 简体中文 Latest News 2021.10.25 Paper "Docking-based Virtual Screening with Multi-Task Learning" is accepted by BIBM 2021. 2021.07.29 PaddleHeli
Machine Learning Privacy Meter: A tool to quantify the privacy risks of machine learning models with respect to inference attacks, notably membership inference attacks
ML Privacy Meter Machine learning is playing a central role in automated decision making in a wide range of organization and service providers. The da
Official repository for "Restormer: Efficient Transformer for High-Resolution Image Restoration". SOTA results for single-image motion deblurring, image deraining, image denoising (synthetic and real data), and dual-pixel defocus deblurring.
Restormer: Efficient Transformer for High-Resolution Image Restoration Syed Waqas Zamir, Aditya Arora, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan,
Self-attentive task GAN for space domain awareness data augmentation.
SATGAN TODO: update the article URL once published. Article about this implemention The self-attentive task generative adversarial network (SATGAN) le
This library is an ongoing effort towards bringing the data exchanging ability between Java/Scala and Python
PyJava This library is an ongoing effort towards bringing the data exchanging ability between Java/Scala and Python
A number of methods in order to perform Natural Language Processing on live data derived from Twitter
A number of methods in order to perform Natural Language Processing on live data derived from Twitter
Self-supervised Point Cloud Prediction Using 3D Spatio-temporal Convolutional Networks
Self-supervised Point Cloud Prediction Using 3D Spatio-temporal Convolutional Networks This is a Pytorch-Lightning implementation of the paper "Self-s
A PyTorch implementation of "CoAtNet: Marrying Convolution and Attention for All Data Sizes".
CoAtNet Overview This is a PyTorch implementation of CoAtNet specified in "CoAtNet: Marrying Convolution and Attention for All Data Sizes", arXiv 2021
Contrastive Feature Loss for Image Prediction
Contrastive Feature Loss for Image Prediction We provide a PyTorch implementation of our contrastive feature loss presented in: Contrastive Feature Lo
Project issue to website data transformation toolkit
braintransform Project issue to website data transformation toolkit. Introduction The purpose of these scripts is to be able to dynamically generate t
Simulation code and tutorial for BBHnet training data
Simulation Dataset for BBHnet NOTE: OLD README, UPDATE IN PROGRESS We generate simulation dataset to train BBHnet, our deep learning framework for det
TPH-YOLOv5: Improved YOLOv5 Based on Transformer Prediction Head for Object Detection on Drone-Captured Scenarios
TPH-YOLOv5 This repo is the implementation of "TPH-YOLOv5: Improved YOLOv5 Based on Transformer Prediction Head for Object Detection on Drone-Captured
Clip Bing Maps backgound as RGB geotif image using center-point from vector data of a shapefile and Bing Maps zoom
Clip Bing Maps backgound as RGB geotif image using center-point from vector data of a shapefile and Bing Maps zoom. Also, rasterize shapefile vectors as corresponding label image.
PyTorch Kafka Dataset: A definition of a dataset to get training data from Kafka.
PyTorch Kafka Dataset: A definition of a dataset to get training data from Kafka.
GeneGAN: Learning Object Transfiguration and Attribute Subspace from Unpaired Data
GeneGAN: Learning Object Transfiguration and Attribute Subspace from Unpaired Data By Shuchang Zhou, Taihong Xiao, Yi Yang, Dieqiao Feng, Qinyao He, W
A TensorFlow Implementation of "Deep Multi-Scale Video Prediction Beyond Mean Square Error" by Mathieu, Couprie & LeCun.
Adversarial Video Generation This project implements a generative adversarial network to predict future frames of video, as detailed in "Deep Multi-Sc
SalGAN: Visual Saliency Prediction with Generative Adversarial Networks
SalGAN: Visual Saliency Prediction with Adversarial Networks Junting Pan Cristian Canton Ferrer Kevin McGuinness Noel O'Connor Jordi Torres Elisa Sayr
Code and data for paper "Deep Photo Style Transfer"
deep-photo-styletransfer Code and data for paper "Deep Photo Style Transfer" Disclaimer This software is published for academic and non-commercial use
A data preprocessing and feature engineering script for a machine learning pipeline is prepared.
FEATURE ENGINEERING Business Problem: A data preprocessing and feature engineering script for a machine learning pipeline needs to be prepared. It is
Required for a machine learning pipeline data preprocessing and variable engineering script needs to be prepared
Feature-Engineering Required for a machine learning pipeline data preprocessing and variable engineering script needs to be prepared. When the dataset
The repository forked from NVlabs uses our data. (Differentiable rasterization applied to 3D model simplification tasks)
nvdiffmodeling [origin_code] Differentiable rasterization applied to 3D model simplification tasks, as described in the paper: Appearance-Driven Autom
Extract rooms type, door, neibour rooms, rooms corners nad bounding boxes, and generate graph from rplan dataset
Housegan-data-reader House-GAN++ (data-reader) Code and instructions for converting rplan dataset (raster images) to housegan++ data format. House-GAN
A Semi-Intelligent ChatBot filled with statistical and economical data for the Premier League.
MONEYBALL - ChatBot Module: 4006CEM, Class: B, Group: 5 Contributors: Jonas Djondo Roshan Kc Cole Samson Daniel Rodrigues Ihteshaam Naseer Kind remind
Data Utilities e.g. for importing files to onetask
Use this repository to easily convert your source files (csv, txt, excel, json, html) into record-oriented JSON files that can be uploaded into onetask.
Finding Label and Model Errors in Perception Data With Learned Observation Assertions
Finding Label and Model Errors in Perception Data With Learned Observation Assertions This is the project page for Finding Label and Model Errors in P
🐦 Quickly annotate data from the comfort of your Jupyter notebook
🐦 pigeon - Quickly annotate data on Jupyter Pigeon is a simple widget that lets you quickly annotate a dataset of unlabeled examples from the comfort
This is the code used in the paper "Entity Embeddings of Categorical Variables".
This is the code used in the paper "Entity Embeddings of Categorical Variables". If you want to get the original version of the code used for the Kagg
A scikit-learn-compatible module for estimating prediction intervals.
MAPIE - Model Agnostic Prediction Interval Estimator MAPIE allows you to easily estimate prediction intervals (or prediction sets) using your favourit
PyClustering is a Python, C++ data mining library.
pyclustering is a Python, C++ data mining library (clustering algorithm, oscillatory networks, neural networks). The library provides Python and C++ implementations (C++ pyclustering library) of each algorithm or model. C++ pyclustering library is a part of pyclustering and supported for Linux, Windows and MacOS operating systems.
The Fundamental Clustering Problems Suite (FCPS) summaries 54 state-of-the-art clustering algorithms, common cluster challenges and estimations of the number of clusters as well as the testing for cluster tendency.
FCPS Fundamental Clustering Problems Suite The package provides over sixty state-of-the-art clustering algorithms for unsupervised machine learning pu
t-SNE and hierarchical clustering are popular methods of exploratory data analysis, particularly in biology.
tree-SNE t-SNE and hierarchical clustering are popular methods of exploratory data analysis, particularly in biology. Building on recent advances in s
Subpopulation detection in high-dimensional single-cell data
PhenoGraph for Python3 PhenoGraph is a clustering method designed for high-dimensional single-cell data. It works by creating a graph ("network") repr
Kats, a kit to analyze time series data, a lightweight, easy-to-use, generalizable, and extendable framework to perform time series analysis, from understanding the key statistics and characteristics, detecting change points and anomalies, to forecasting future trends.
Description Kats is a toolkit to analyze time series data, a lightweight, easy-to-use, and generalizable framework to perform time series analysis. Ti
Time Series Prediction with tf.contrib.timeseries
TensorFlow-Time-Series-Examples Additional examples for TensorFlow Time Series(TFTS). Read a Time Series with TFTS From a Numpy Array: See "test_input
An implementation of a sequence to sequence neural network using an encoder-decoder
Keras implementation of a sequence to sequence model for time series prediction using an encoder-decoder architecture. I created this post to share a
Contains an implementation (sklearn API) of the algorithm proposed in "GENDIS: GEnetic DIscovery of Shapelets" and code to reproduce all experiments.
GENDIS GENetic DIscovery of Shapelets In the time series classification domain, shapelets are small subseries that are discriminative for a certain cl
Calling Julia from Python - an experiment on data loading
Calling Julia from Python - an experiment on data loading See the slides. TLDR After reading Patrick's blog post, we decided to try to replace C++ wit
The audio-video synchronization of MKV Container Format is exploited to achieve data hiding
The audio-video synchronization of MKV Container Format is exploited to achieve data hiding, where the hidden data can be utilized for various management purposes, including hyper-linking, annotation, and authentication
Urban Big Data Centre Housing Sensor Project
Housing Sensor Project The Urban Big Data Centre is conducting a study of indoor environmental data in Scottish houses. We are using Raspberry Pi devi
Fast, general, and tested differentiable structured prediction in PyTorch
Fast, general, and tested differentiable structured prediction in PyTorch
[NeurIPS 2021] Deceive D: Adaptive Pseudo Augmentation for GAN Training with Limited Data
Deceive D: Adaptive Pseudo Augmentation for GAN Training with Limited Data (NeurIPS 2021) This repository will provide the official PyTorch implementa
RATE: Overcoming Noise and Sparsity of Textual Features in Real-Time Location Estimation (CIKM'17)
RATE: Overcoming Noise and Sparsity of Textual Features in Real-Time Location Estimation This is the implementation of RATE: Overcoming Noise and Spar
Scrutinizing XAI with linear ground-truth data
This repository contains all the experiments presented in the corresponding paper: "Scrutinizing XAI using linear ground-truth data with suppressor va
Code and real data for the paper "Counterfactual Temporal Point Processes", available at arXiv.
counterfactual-tpp This is a repository containing code and real data for the paper Counterfactual Temporal Point Processes. Pre-requisites This code
Code and data accompanying our SVRHM'21 paper.
Code and data accompanying our SVRHM'21 paper. Requires tensorflow 1.13, python 3.7, scikit-learn, and pytorch 1.6.0 to be installed. Python scripts i
Automated detection of anomalous exoplanet transits in light curve data.
Automatically detecting anomalous exoplanet transits This repository contains the source code for the paper "Automatically detecting anomalous exoplan
DataCLUE: 国内首个以数据为中心的AI测评(含模型分析报告)
DataCLUE: A Benchmark Suite for Data-centric NLP You can get the english version of README. 以数据为中心的AI测评(DataCLUE) 内容导引 章节 描述 简介 介绍以数据为中心的AI测评(DataCLUE
AIR^2 for Interaction Prediction
This is the repository for AIR^2 for Interaction Prediction. Explanation of the solution: Video: link License AIR is released under the Apache 2.0 lic
Text-to-Music Retrieval using Pre-defined/Data-driven Emotion Embeddings
Text2Music Emotion Embedding Text-to-Music Retrieval using Pre-defined/Data-driven Emotion Embeddings Reference Emotion Embedding Spaces for Matching
A collection of online resources to help you on your Tech journey.
Everything Tech Resources & Projects About The Project Coming from an engineering background and looking to up skill yourself on a new field can be di
SASE : Self-Adaptive noise distribution network for Speech Enhancement with heterogeneous data of Cross-Silo Federated learning
SASE : Self-Adaptive noise distribution network for Speech Enhancement with heterogeneous data of Cross-Silo Federated learning We propose a SASE mode
FLIR/DJI IR Camera Data Parser, Python Version
FLIR/DJI IR Camera Data Parser, Python Version Parser infrared camera data as NumPy data. Usage Clone this respository and cd thermal_parser. Run pip
Scikit learn library models to account for data and concept drift.
liquid_scikit_learn Scikit learn library models to account for data and concept drift. This python library focuses on solving data drift and concept d
DeepSTD: Mining Spatio-temporal Disturbances of Multiple Context Factors for Citywide Traffic Flow Prediction
DeepSTD: Mining Spatio-temporal Disturbances of Multiple Context Factors for Citywide Traffic Flow Prediction This is the implementation of DeepSTD in
Python plugin/extra to load data files from an external source (such as AWS S3) to a local directory
Data Loader Plugin - Python Table of Content (ToC) Data Loader Plugin - Python Table of Content (ToC) Overview References Python module Python virtual
Service for working with open data of the State Duma of the Russian Federation
Сервис для работы с открытыми данными Госдумы РФ Исходные данные из API Госдумы РФ извлекаются с помощью Apache Nifi и приземляются в хранилище Clickh
MASS (Mueen's Algorithm for Similarity Search) - a python 2 and 3 compatible library used for searching time series sub-sequences under z-normalized Euclidean distance for similarity.
Introduction MASS allows you to search a time series for a subquery resulting in an array of distances. These array of distances enable you to identif
ObsPy: A Python Toolbox for seismology/seismological observatories.
ObsPy is an open-source project dedicated to provide a Python framework for processing seismological data. It provides parsers for common file formats
sktime companion package for deep learning based on TensorFlow
NOTE: sktime-dl is currently being updated to work correctly with sktime 0.6, and wwill be fully relaunched over the summer. The plan is Refactor and
Luminaire is a python package that provides ML driven solutions for monitoring time series data.
A hands-off Anomaly Detection Library Table of contents What is Luminaire Quick Start Time Series Outlier Detection Workflow Anomaly Detection for Hig
Time Series Cross-Validation -- an extension for scikit-learn
TSCV: Time Series Cross-Validation This repository is a scikit-learn extension for time series cross-validation. It introduces gaps between the traini
Python library to download market data via Bloomberg, Eikon, Quandl, Yahoo etc.
findatapy findatapy creates an easy to use Python API to download market data from many sources including Quandl, Bloomberg, Yahoo, Google etc. using
Deep Survival Machines - Fully Parametric Survival Regression
Package: dsm Python package dsm provides an API to train the Deep Survival Machines and associated models for problems in survival analysis. The under
A framework for using LSTMs to detect anomalies in multivariate time series data. Includes spacecraft anomaly data and experiments from the Mars Science Laboratory and SMAP missions.
Telemanom (v2.0) v2.0 updates: Vectorized operations via numpy Object-oriented restructure, improved organization Merge branches into single branch fo
DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphical models and potential outcomes frameworks.
DoWhy | An end-to-end library for causal inference Amit Sharma, Emre Kiciman Introducing DoWhy and the 4 steps of causal inference | Microsoft Researc
Responsible Machine Learning with Python
Examples of techniques for training interpretable ML models, explaining ML models, and debugging ML models for accuracy, discrimination, and security.
LOFO (Leave One Feature Out) Importance calculates the importances of a set of features based on a metric of choice,
LOFO (Leave One Feature Out) Importance calculates the importances of a set of features based on a metric of choice, for a model of choice, by iteratively removing each feature from the set, and evaluating the performance of the model, with a validation scheme of choice, based on the chosen metric.
moDel Agnostic Language for Exploration and eXplanation
moDel Agnostic Language for Exploration and eXplanation Overview Unverified black box model is the path to the failure. Opaqueness leads to distrust.
An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.
NNI Doc | 简体中文 NNI (Neural Network Intelligence) is a lightweight but powerful toolkit to help users automate Feature Engineering, Neural Architecture
🌊 River is a Python library for online machine learning.
River is a Python library for online machine learning. It is the result of a merger between creme and scikit-multiflow. River's ambition is to be the go-to library for doing machine learning on streaming data.
Hangar is version control for tensor data. Commit, branch, merge, revert, and collaborate in the data-defined software era.
Overview docs tests package Hangar is version control for tensor data. Commit, branch, merge, revert, and collaborate in the data-defined software era
Transpile trained scikit-learn estimators to C, Java, JavaScript and others.
sklearn-porter Transpile trained scikit-learn estimators to C, Java, JavaScript and others. It's recommended for limited embedded systems and critical
ModelChimp is an experiment tracker for Deep Learning and Machine Learning experiments.
ModelChimp What is ModelChimp? ModelChimp is an experiment tracker for Deep Learning and Machine Learning experiments. ModelChimp provides the followi
BentoML is a flexible, high-performance framework for serving, managing, and deploying machine learning models.
Model Serving Made Easy BentoML is a flexible, high-performance framework for serving, managing, and deploying machine learning models. Supports multi
An orchestration platform for the development, production, and observation of data assets.
Dagster An orchestration platform for the development, production, and observation of data assets. Dagster lets you define jobs in terms of the data f
Metaflow is a human-friendly Python/R library that helps scientists and engineers build and manage real-life data science projects
Metaflow Metaflow is a human-friendly Python/R library that helps scientists and engineers build and manage real-life data science projects. Metaflow
Handle, manipulate, and convert data with units in Python
unyt A package for handling numpy arrays with units. Often writing code that deals with data that has units can be confusing. A function might return
Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.
Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.
List of Data Science Cheatsheets to rule the world
Data Science Cheatsheets List of Data Science Cheatsheets to rule the world. Table of Contents Business Science Business Science Problem Framework Dat
A Kitti Road Segmentation model implemented in tensorflow.
KittiSeg KittiSeg performs segmentation of roads by utilizing an FCN based model. The model achieved first place on the Kitti Road Detection Benchmark
RefineNet: Multi-Path Refinement Networks for High-Resolution Semantic Segmentation
Multipath RefineNet A MATLAB based framework for semantic image segmentation and general dense prediction tasks on images. This is the source code for
PyTorch implementation of Federated Learning with Non-IID Data, and federated learning algorithms, including FedAvg, FedProx.
Federated Learning with Non-IID Data This is an implementation of the following paper: Yue Zhao, Meng Li, Liangzhen Lai, Naveen Suda, Damon Civin, Vik
Identifies the faulty wafer before it can be used for the fabrication of integrated circuits and, in photovoltaics, to manufacture solar cells.
Identifies the faulty wafer before it can be used for the fabrication of integrated circuits and, in photovoltaics, to manufacture solar cells. The project retrains itself after every prediction, making it more robust and generalized over time.
A package to fetch sentinel 2 Satellite data from Google.
Sentinel 2 Data Fetcher Installation Create a Virtual Environment and activate it. python3 -m venv venv . venv/bin/activate Install the Package via pi
Very simple encoding scheme that will encode data as a series of OwOs or UwUs.
OwO Encoder Very simple encoding scheme that will encode data as a series of OwOs or UwUs. The encoder is a simple state machine. Still needs a decode
Python based framework for Automatic AI for Regression and Classification over numerical data.
Python based framework for Automatic AI for Regression and Classification over numerical data. Performs model search, hyper-parameter tuning, and high-quality Jupyter Notebook code generation.
Sentiment analysis on streaming twitter data using Spark Structured Streaming & Python
Sentiment analysis on streaming twitter data using Spark Structured Streaming & Python This project is a good starting point for those who have little
Doom o’clock is a website/project that features a countdown of “when will the earth end” and a greenhouse gas effect emission prediction that’s predicted
Doom o’clock is a website/project that features a countdown of “when will the earth end” and a greenhouse gas effect emission prediction that’s predicted
Stock Price Prediction Bank Jago Using Facebook Prophet Machine Learning & Python
Stock Price Prediction Bank Jago Using Facebook Prophet Machine Learning & Python Overview Bank Jago has attracted investors' attention since the end
A meta plugin for processing timelapse data timepoint by timepoint in napari
napari-time-slicer A meta plugin for processing timelapse data timepoint by timepoint. It enables a list of napari plugins to process 2D+t or 3D+t dat
Data and code for the paper "Importance of Kernel Bandwidth in Quantum Machine Learning"
Reproducibility materials for "Importance of Kernel Bandwidth in Quantum Machine Learning" Repo structure: code contains Python scripts used to genera
Understanding the Generalization Benefit of Model Invariance from a Data Perspective
Understanding the Generalization Benefit of Model Invariance from a Data Perspective This is the code for our NeurIPS2021 paper "Understanding the Gen
RMTD: Robust Moving Target Defence Against False Data Injection Attacks in Power Grids
RMTD: Robust Moving Target Defence Against False Data Injection Attacks in Power Grids Real-time detection performance. This repo contains the code an