5218 Repositories
Python structural-learning Libraries
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
Python package for causal inference using Bayesian structural time-series models.
Python Causal Impact Causal inference using Bayesian structural time-series models. This package aims at defining a python equivalent of the R CausalI
pymc-learn: Practical Probabilistic Machine Learning in Python
pymc-learn: Practical Probabilistic Machine Learning in Python Contents: Github repo What is pymc-learn? Quick Install Quick Start Index What is pymc-
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.
Python package to visualize and cluster partial dependence.
partial_dependence A python library for plotting partial dependence patterns of machine learning classifiers. The technique is a black box approach to
XAI - An eXplainability toolbox for machine learning
XAI - An eXplainability toolbox for machine learning XAI is a Machine Learning library that is designed with AI explainability in its core. XAI contai
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.
Fit interpretable models. Explain blackbox machine learning.
InterpretML - Alpha Release In the beginning machines learned in darkness, and data scientists struggled in the void to explain them. Let there be lig
AdaNet is a lightweight TensorFlow-based framework for automatically learning high-quality models with minimal expert intervention
AdaNet is a lightweight TensorFlow-based framework for automatically learning high-quality models with minimal expert intervention. AdaNet buil
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
Provide an input CSV and a target field to predict, generate a model + code to run it.
automl-gs Give an input CSV file and a target field you want to predict to automl-gs, and get a trained high-performing machine learning or deep learn
Neural Architecture Search Powered by Swarm Intelligence 🐜
Neural Architecture Search Powered by Swarm Intelligence 🐜 DeepSwarm DeepSwarm is an open-source library which uses Ant Colony Optimization to tackle
A hyperparameter optimization framework
Optuna: A hyperparameter optimization framework Website | Docs | Install Guide | Tutorial Optuna is an automatic hyperparameter optimization software
🌊 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.
onelearn: Online learning in Python
onelearn: Online learning in Python Documentation | Reproduce experiments | onelearn stands for ONE-shot LEARNning. It is a small python package for o
A clean implementation based on AlphaZero for any game in any framework + tutorial + Othello/Gobang/TicTacToe/Connect4 and more
Alpha Zero General (any game, any framework!) A simplified, highly flexible, commented and (hopefully) easy to understand implementation of self-play
Implementation of Uniformer, a simple attention and 3d convolutional net that achieved SOTA in a number of video classification tasks
Uniformer - Pytorch Implementation of Uniformer, a simple attention and 3d convolutional net that achieved SOTA in a number of video classification ta
Transform ML models into a native code with zero dependencies
m2cgen (Model 2 Code Generator) - is a lightweight library which provides an easy way to transpile trained statistical models into a native code
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
🚪✊Knock Knock: Get notified when your training ends with only two additional lines of code
Knock Knock A small library to get a notification when your training is complete or when it crashes during the process with two additional lines of co
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
A collection of video resources for machine learning
Machine Learning Videos This is a collection of recorded talks at machine learning conferences, workshops, seminars, summer schools, and miscellaneous
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.
Gated-Shape CNN for Semantic Segmentation (ICCV 2019)
GSCNN This is the official code for: Gated-SCNN: Gated Shape CNNs for Semantic Segmentation Towaki Takikawa, David Acuna, Varun Jampani, Sanja Fidler
A CV toolkit for my papers.
PyTorch-Encoding created by Hang Zhang Documentation Please visit the Docs for detail instructions of installation and usage. Please visit the link to
Code for our paper "Multi-scale Guided Attention for Medical Image Segmentation"
Medical Image Segmentation with Guided Attention This repository contains the code of our paper: "'Multi-scale self-guided attention for medical image
UPSNet: A Unified Panoptic Segmentation Network
UPSNet: A Unified Panoptic Segmentation Network Introduction UPSNet is initially described in a CVPR 2019 oral paper. Disclaimer This repository is te
Understanding Convolution for Semantic Segmentation
TuSimple-DUC by Panqu Wang, Pengfei Chen, Ye Yuan, Ding Liu, Zehua Huang, Xiaodi Hou, and Garrison Cottrell. Introduction This repository is for Under
Learning to Adapt Structured Output Space for Semantic Segmentation, CVPR 2018 (spotlight)
Learning to Adapt Structured Output Space for Semantic Segmentation Pytorch implementation of our method for adapting semantic segmentation from the s
Tensorflow Implementation of Pixel Transposed Convolutional Networks (PixelTCN and PixelTCL)
Pixel Transposed Convolutional Networks Created by Hongyang Gao, Hao Yuan, Zhengyang Wang and Shuiwang Ji at Texas A&M University. Introduction Pixel
Real-time Joint Semantic Reasoning for Autonomous Driving
MultiNet MultiNet is able to jointly perform road segmentation, car detection and street classification. The model achieves real-time speed and state-
Tensorflow implementation of "Learning Deconvolution Network for Semantic Segmentation"
Tensorflow implementation of Learning Deconvolution Network for Semantic Segmentation. Install Instructions Works with tensorflow 1.11.0 and uses the
DeconvNet : Learning Deconvolution Network for Semantic Segmentation
DeconvNet: Learning Deconvolution Network for Semantic Segmentation Created by Hyeonwoo Noh, Seunghoon Hong and Bohyung Han at POSTECH Acknowledgement
PSPNet in Chainer
PSPNet This is an unofficial implementation of Pyramid Scene Parsing Network (PSPNet) in Chainer. Training Requirement Python 3.4.4+ Chainer 3.0.0b1+
Keras implementation of Real-Time Semantic Segmentation on High-Resolution Images
Keras-ICNet [paper] Keras implementation of Real-Time Semantic Segmentation on High-Resolution Images. Training in progress! Requisites Python 3.6.3 K
The repository contains source code and models to use PixelNet architecture used for various pixel-level tasks. More details can be accessed at http://www.cs.cmu.edu/~aayushb/pixelNet/.
PixelNet: Representation of the pixels, by the pixels, and for the pixels. We explore design principles for general pixel-level prediction problems, f
TensorFlow implementation of ENet
TensorFlow-ENet TensorFlow implementation of ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation. This model was tested on th
TensorFlow implementation of ENet, trained on the Cityscapes dataset.
segmentation TensorFlow implementation of ENet (https://arxiv.org/pdf/1606.02147.pdf) based on the official Torch implementation (https://github.com/e
Fully convolutional networks for semantic segmentation
FCN-semantic-segmentation Simple end-to-end semantic segmentation using fully convolutional networks [1]. Takes a pretrained 34-layer ResNet [2], remo
Pytorch for Segmentation
Pytorch for Semantic Segmentation This repo has been deprecated currently and I will not maintain it. Meanwhile, I strongly recommend you can refer to
Chainer Implementation of Fully Convolutional Networks. (Training code to reproduce the original result is available.)
fcn - Fully Convolutional Networks Chainer implementation of Fully Convolutional Networks. Installation pip install fcn Inference Inference is done as
TorchCV: A PyTorch-Based Framework for Deep Learning in Computer Vision
TorchCV: A PyTorch-Based Framework for Deep Learning in Computer Vision @misc{you2019torchcv, author = {Ansheng You and Xiangtai Li and Zhen Zhu a
DeepLab is a state-of-art deep learning system for semantic image segmentation built on top of Caffe.
DeepLab Introduction DeepLab is a state-of-art deep learning system for semantic image segmentation built on top of Caffe. It combines densely-compute
SegNet-like Autoencoders in TensorFlow
SegNet SegNet is a TensorFlow implementation of the segmentation network proposed by Kendall et al., with cool features like strided deconvolution, a
Semantic segmentation models, datasets and losses implemented in PyTorch.
Semantic Segmentation in PyTorch Semantic Segmentation in PyTorch Requirements Main Features Models Datasets Losses Learning rate schedulers Data augm
Real-Time Semantic Segmentation in Mobile device
Real-Time Semantic Segmentation in Mobile device This project is an example project of semantic segmentation for mobile real-time app. The architectur
Generic U-Net Tensorflow implementation for image segmentation
Tensorflow Unet Warning This project is discontinued in favour of a Tensorflow 2 compatible reimplementation of this project found under https://githu
Deep Learning Tutorial for Kaggle Ultrasound Nerve Segmentation competition, using Keras
Deep Learning Tutorial for Kaggle Ultrasound Nerve Segmentation competition, using Keras This tutorial shows how to use Keras library to build deep ne
A combination of autoregressors and autoencoders using XLNet for sentiment analysis
A combination of autoregressors and autoencoders using XLNet for sentiment analysis Abstract In this paper sentiment analysis has been performed in or
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.
CNN Based Meta-Learning for Noisy Image Classification and Template Matching
CNN Based Meta-Learning for Noisy Image Classification and Template Matching Introduction This master thesis used a few-shot meta learning approach to
A graph adversarial learning toolbox based on PyTorch and DGL.
GraphWar: Arms Race in Graph Adversarial Learning NOTE: GraphWar is still in the early stages and the API will likely continue to change. 🚀 Installat
Models Supported: AlbUNet [18, 34, 50, 101, 152] (1D and 2D versions for Single and Multiclass Segmentation, Feature Extraction with supports for Deep Supervision and Guided Attention)
AlbUNet-1D-2D-Tensorflow-Keras This repository contains 1D and 2D Signal Segmentation Model Builder for AlbUNet and several of its variants developed
FS-Mol: A Few-Shot Learning Dataset of Molecules
FS-Mol is A Few-Shot Learning Dataset of Molecules, containing molecular compounds with measurements of activity against a variety of protein targets. The dataset is presented with a model evaluation benchmark which aims to drive few-shot learning research in the domain of molecules and graph-structured data.
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.
This is the official released code for our paper, The Emergence of Objectness: Learning Zero-Shot Segmentation from Videos
The-Emergence-of-Objectness This is the official released code for our paper, The Emergence of Objectness: Learning Zero-Shot Segmentation from Videos
A criticism of a recent paper on buggy image downsampling methods in popular image processing and deep learning libraries.
A criticism of a recent paper on buggy image downsampling methods in popular image processing and deep learning libraries.
[CVPR'20] TTSR: Learning Texture Transformer Network for Image Super-Resolution
TTSR Official PyTorch implementation of the paper Learning Texture Transformer Network for Image Super-Resolution accepted in CVPR 2020. Contents Intr
This is the repository for Learning to Generate Piano Music With Sustain Pedals
SusPedal-Gen This is the official repository of Learning to Generate Piano Music With Sustain Pedals Demo Page Dataset The dataset used in this projec
Interactive convnet features visualization for Keras
Quiver Interactive convnet features visualization for Keras The quiver workflow Video Demo Build your model in keras model = Model(...) Launch the vis
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
Blazing fast language detection using fastText model
Luga A blazing fast language detection using fastText's language models Luga is a Swahili word for language. fastText provides a blazing fast language
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
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
ResNEsts and DenseNEsts: Block-based DNN Models with Improved Representation Guarantees
ResNEsts and DenseNEsts: Block-based DNN Models with Improved Representation Guarantees This repository is the official implementation of the empirica
Official implementation of NeurIPS'21: Implicit SVD for Graph Representation Learning
isvd Official implementation of NeurIPS'21: Implicit SVD for Graph Representation Learning If you find this code useful, you may cite us as: @inprocee
A Python script to convert your favorite TV series into an Anki deck.
Ankiniser A Python3.8 script to convert your favorite TV series into an Anki deck. How to install? Download the script with git or download it manualy
ML for NLP and Computer Vision.
Sparrow is our open-source ML product. It runs on Skipper MLOps infrastructure.
Dense Unsupervised Learning for Video Segmentation (NeurIPS*2021)
Dense Unsupervised Learning for Video Segmentation This repository contains the official implementation of our paper: Dense Unsupervised Learning for
[NeurIPS 2021] Code for Learning Signal-Agnostic Manifolds of Neural Fields
Learning Signal-Agnostic Manifolds of Neural Fields This is the uncleaned code for the paper Learning Signal-Agnostic Manifolds of Neural Fields. The
Predict the latency time of the deep learning models
Deep Neural Network Prediction Step 1. Genernate random parameters and Run them sequentially : $ python3 collect_data.py -gp -ep -pp -pl pooling -num
We have built a Voice based Personal Assistant for people to access files hands free in their device using natural language processing.
Voice Based Personal Assistant We have built a Voice based Personal Assistant for people to access files hands free in their device using natural lang
Implementation of H-Transformer-1D, Hierarchical Attention for Sequence Learning using 🤗 transformers
hierarchical-transformer-1d Implementation of H-Transformer-1D, Hierarchical Attention for Sequence Learning using 🤗 transformers In Progress!! 2021.
IMDB film review sentiment classification based on BERT's supervised learning model.
IMDB film review sentiment classification based on BERT's supervised learning model. On the other hand, the model can be extended to other natural language multi-classification tasks.
Bodywork deploys machine learning projects developed in Python, to Kubernetes.
Bodywork deploys machine learning projects developed in Python, to Kubernetes. It helps you to: serve models as microservices execute batch jobs run r
ProsePainter combines direct digital painting with real-time guided machine-learning based image optimization.
ProsePainter Create images by painting with words. ProsePainter combines direct digital painting with real-time guided machine-learning based image op
A free, multiplatform SDK for real-time facial motion capture using blendshapes, and rigid head pose in 3D space from any RGB camera, photo, or video.
mocap4face by Facemoji mocap4face by Facemoji is a free, multiplatform SDK for real-time facial motion capture based on Facial Action Coding System or
[NeurIPS 2021] Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methods
Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methods Large Scale Learning on Non-Homophilous Graphs: New Benchmark
Music Classification: Beyond Supervised Learning, Towards Real-world Applications
Music Classification: Beyond Supervised Learning, Towards Real-world Applications
Isaac Gym Reinforcement Learning Environments
Isaac Gym Reinforcement Learning Environments
ACAV100M: Automatic Curation of Large-Scale Datasets for Audio-Visual Video Representation Learning. In ICCV, 2021.
ACAV100M: Automatic Curation of Large-Scale Datasets for Audio-Visual Video Representation Learning This repository contains the code for our ICCV 202
Codes accompanying the paper "Believe What You See: Implicit Constraint Approach for Offline Multi-Agent Reinforcement Learning" (NeurIPS 2021 Spotlight
Implicit Constraint Q-Learning This is a pytorch implementation of ICQ on Datasets for Deep Data-Driven Reinforcement Learning (D4RL) and ICQ-MA on SM
Self-Supervised Learning with Data Augmentations Provably Isolates Content from Style
Self-Supervised Learning with Data Augmentations Provably Isolates Content from Style [NeurIPS 2021] Official code to reproduce the results and data p
FCA: Learning a 3D Full-coverage Vehicle Camouflage for Multi-view Physical Adversarial Attack
FCA: Learning a 3D Full-coverage Vehicle Camouflage for Multi-view Physical Adversarial Attack Case study of the FCA. The code can be find in FCA. Cas
Python code for solving 3D structural problems using the finite element method
3DFEM Python 3D finite element code This python code allows for solving 3D structural problems using the finite element method. New features will be a
Implementation of the bachelor's thesis "Real-time stock predictions with deep learning and news scraping".
Real-time stock predictions with deep learning and news scraping This repository contains a partial implementation of my bachelor's thesis "Real-time
Pytools is an open source library containing general machine learning and visualisation utilities for reuse
pytools is an open source library containing general machine learning and visualisation utilities for reuse, including: Basic tools for API developmen
Conservative Q Learning for Offline Reinforcement Reinforcement Learning in JAX
CQL-JAX This repository implements Conservative Q Learning for Offline Reinforcement Reinforcement Learning in JAX (FLAX). Implementation is built on
MIRACLE (Missing data Imputation Refinement And Causal LEarning)
MIRACLE (Missing data Imputation Refinement And Causal LEarning) Code Author: Trent Kyono This repository contains the code used for the "MIRACLE: Cau
Code & Experiments for "LILA: Language-Informed Latent Actions" to be presented at the Conference on Robot Learning (CoRL) 2021.
LILA LILA: Language-Informed Latent Actions Code and Experiments for Language-Informed Latent Actions (LILA), for using natural language to guide assi
Investigating automatic navigation towards standard US views integrating MARL with the virtual US environment developed in CT2US simulation
AutomaticUSnavigation Investigating automatic navigation towards standard US views integrating MARL with the virtual US environment developed in CT2US
Physics-informed Neural Operator for Learning Partial Differential Equation
PINO Physics-informed Neural Operator for Learning Partial Differential Equation Abstract: Machine learning methods have recently shown promise in sol
Gym environments used in the paper: "Developmental Reinforcement Learning of Control Policy of a Quadcopter UAV with Thrust Vectoring Rotors"
gym_multirotor Gym to train reinforcement learning agents on UAV platforms Quadrotor Tiltrotor Requirements This package has been tested on Ubuntu 18.
An offline deep reinforcement learning library
d3rlpy: An offline deep reinforcement learning library d3rlpy is an offline deep reinforcement learning library for practitioners and researchers. imp