5195 Repositories
Python reinforcement-learning Libraries
Experiments and code to generate the GINC small-scale in-context learning dataset from "An Explanation for In-context Learning as Implicit Bayesian Inference"
GINC small-scale in-context learning dataset GINC (Generative In-Context learning Dataset) is a small-scale synthetic dataset for studying in-context
Code for NeurIPS 2021 paper "Curriculum Offline Imitation Learning"
README The code is based on the ILswiss. To run the code, use python run_experiment.py --nosrun -e your YAML file -g gpu id Generally, run_experim
Causal-BALD: Deep Bayesian Active Learning of Outcomes to Infer Treatment-Effects from Observational Data.
causal-bald | Abstract | Installation | Example | Citation | Reproducing Results DUE An implementation of the methods presented in Causal-BALD: Deep B
Official implementation of "Multi-Glimpse Network: A Robust and Efficient Classification Architecture based on Recurrent Downsampled Attention" (BMVC 2021).
Multi-Glimpse Network Multi-Glimpse Network: A Robust and Efficient Classification Architecture based on Recurrent Downsampled Attention arXiv Require
Code for ICMI2020 and ICMI2021 papers: "Studying Person-Specific Pointing and Gaze Behavior for Multimodal Referencing of Outside Objects from a Moving Vehicle" and "ML-PersRef: A Machine Learning-based Personalized Multimodal Fusion Approach for Referencing Outside Objects From a Moving Vehicle"
ML-PersRef This repository has python code (in jupyter notebooks) for both of the following papers: ML-PersRef: A Machine Learning-based Personalized
Geometric Algebra package for JAX
JAXGA - JAX Geometric Algebra GitHub | Docs JAXGA is a Geometric Algebra package on top of JAX. It can handle high dimensional algebras by storing onl
Machine Learning approach for quantifying detector distortion fields
DistortionML Machine Learning approach for quantifying detector distortion fields. This project is a feasibility study for training a surrogate model
[NeurIPS 2021] Code for Unsupervised Learning of Compositional Energy Concepts
Unsupervised Learning of Compositional Energy Concepts This is the pytorch code for the paper Unsupervised Learning of Compositional Energy Concepts.
A visualisation tool for Deep Reinforcement Learning
DRLVIS - Visualising Deep Reinforcement Learning Created by Marios Sirtmatsis with the support of Alex Bäuerle. DRLVis is an application used for visu
Automatic deep learning for image classification.
AutoDL AutoDL automates machine learning tasks enabling you to easily achieve strong predictive performance in your applications. With just a few line
NitroFE is a Python feature engineering engine which provides a variety of modules designed to internally save past dependent values for providing continuous calculation.
NitroFE is a Python feature engineering engine which provides a variety of modules designed to internally save past dependent values for providing continuous calculation.
A python library for highly configurable transformers - easing model architecture search and experimentation.
A python library for highly configurable transformers - easing model architecture search and experimentation.
PyTorch implementation of "PatchGame: Learning to Signal Mid-level Patches in Referential Games" to appear in NeurIPS 2021
PatchGame: Learning to Signal Mid-level Patches in Referential Games This repository is the official implementation of the paper - "PatchGame: Learnin
[NeurIPS 2021] PyTorch Code for Accelerating Robotic Reinforcement Learning with Parameterized Action Primitives
Robot Action Primitives (RAPS) This repository is the official implementation of Accelerating Robotic Reinforcement Learning via Parameterized Action
JORLDY an open-source Reinforcement Learning (RL) framework provided by KakaoEnterprise
Repository for Open Source Reinforcement Learning Framework JORLDY
Active Learning demo using two small datasets
ActiveLearningDemo How to run step one put the dataset folder and use command below to split the dataset to the required structure run utils.py For ea
Distilling Motion Planner Augmented Policies into Visual Control Policies for Robot Manipulation (CoRL 2021)
Distilling Motion Planner Augmented Policies into Visual Control Policies for Robot Manipulation [Project website] [Paper] This project is a PyTorch i
AutoTabular automates machine learning tasks enabling you to easily achieve strong predictive performance in your applications.
AutoTabular AutoTabular automates machine learning tasks enabling you to easily achieve strong predictive performance in your applications. With just
This library is testing the ethics of language models by using natural adversarial texts.
prompt2slip This library is testing the ethics of language models by using natural adversarial texts. This tool allows for short and simple code and v
The spiritual successor to knockknock for PyTorch Lightning, get notified when your training ends
Who's there? The spiritual successor to knockknock for PyTorch Lightning, to get a notification when your training is complete or when it crashes duri
iris - Open Source Photos Platform Powered by PyTorch
Open Source Photos Platform Powered by PyTorch. Submission for PyTorch Annual Hackathon 2021.
Official implementation of Generalized Data Weighting via Class-level Gradient Manipulation (NeurIPS 2021).
Generalized Data Weighting via Class-level Gradient Manipulation This repository is the official implementation of Generalized Data Weighting via Clas
An efficient and easy-to-use deep learning model compression framework
TinyNeuralNetwork 简体中文 TinyNeuralNetwork is an efficient and easy-to-use deep learning model compression framework, which contains features like neura
Intelligent Trading Bot: Automatically generating signals and trading based on machine learning and feature engineering
Intelligent Trading Bot: Automatically generating signals and trading based on machine learning and feature engineering
Data science, Data manipulation and Machine learning package.
duality Data science, Data manipulation and Machine learning package. Use permitted according to the terms of use and conditions set by the attached l
Source code of the paper Meta-learning with an Adaptive Task Scheduler.
ATS About Source code of the paper Meta-learning with an Adaptive Task Scheduler. If you find this repository useful in your research, please cite the
Implementing DeepMind's Fast Reinforcement Learning paper
Fast Reinforcement Learning This is a repo where I implement the algorithms in the paper, Fast reinforcement learning with generalized policy updates.
Kubeflow is a machine learning (ML) toolkit that is dedicated to making deployments of ML workflows on Kubernetes simple, portable, and scalable.
SDK: Overview of the Kubeflow pipelines service Kubeflow is a machine learning (ML) toolkit that is dedicated to making deployments of ML workflows on
Some methods for comparing network representations in deep learning and neuroscience.
Generalized Shape Metrics on Neural Representations In neuroscience and in deep learning, quantifying the (dis)similarity of neural representations ac
Distributing Deep Learning Hyperparameter Tuning for 3D Medical Image Segmentation
DistMIS Distributing Deep Learning Hyperparameter Tuning for 3D Medical Image Segmentation. DistriMIS Distributing Deep Learning Hyperparameter Tuning
PECOS - Prediction for Enormous and Correlated Spaces
PECOS - Predictions for Enormous and Correlated Output Spaces PECOS is a versatile and modular machine learning (ML) framework for fast learning and i
Generalized Data Weighting via Class-level Gradient Manipulation
Generalized Data Weighting via Class-level Gradient Manipulation This repository is the official implementation of Generalized Data Weighting via Clas
Official repository for the NeurIPS 2021 paper Get Fooled for the Right Reason: Improving Adversarial Robustness through a Teacher-guided curriculum Learning Approach
Get Fooled for the Right Reason Official repository for the NeurIPS 2021 paper Get Fooled for the Right Reason: Improving Adversarial Robustness throu
DSEE: Dually Sparsity-embedded Efficient Tuning of Pre-trained Language Models
DSEE Codes for [Preprint] DSEE: Dually Sparsity-embedded Efficient Tuning of Pre-trained Language Models Xuxi Chen, Tianlong Chen, Yu Cheng, Weizhu Ch
Generalized and Efficient Blackbox Optimization System.
OpenBox Doc | OpenBox中文文档 OpenBox: Generalized and Efficient Blackbox Optimization System OpenBox is an efficient and generalized blackbox optimizatio
Revealing and Protecting Labels in Distributed Training
Revealing and Protecting Labels in Distributed Training
FastCover: A Self-Supervised Learning Framework for Multi-Hop Influence Maximization in Social Networks by Anonymous.
FastCover: A Self-Supervised Learning Framework for Multi-Hop Influence Maximization in Social Networks by Anonymous.
SW components and demos for visual kinship recognition. An emphasis is put on the FIW dataset-- data loaders, benchmarks, results in summary.
FIW Data Development Kit Table of Contents Introduction Families In the Wild Database Publications Organization To Do License Getting Involved Introdu
[NeurIPS 2021] “Improving Contrastive Learning on Imbalanced Data via Open-World Sampling”
Improving Contrastive Learning on Imbalanced Data via Open-World Sampling Introduction Contrastive learning approaches have achieved great success in
gtfs2vec - Learning GTFS Embeddings for comparing PublicTransport Offer in Microregions
gtfs2vec This is a companion repository for a gtfs2vec - Learning GTFS Embeddings for comparing PublicTransport Offer in Microregions publication. Vis
Evaluating deep transfer learning for whole-brain cognitive decoding
Evaluating deep transfer learning for whole-brain cognitive decoding This README file contains the following sections: Project description Repository
This repository contains a PyTorch implementation of the paper Learning to Assimilate in Chaotic Dynamical Systems.
Amortized Assimilation This repository contains a PyTorch implementation of the paper Learning to Assimilate in Chaotic Dynamical Systems. Abstract: T
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
Corruption Invariant Learning for Re-identification
Corruption Invariant Learning for Re-identification The official repository for Benchmarks for Corruption Invariant Person Re-identification (NeurIPS
RMNA: A Neighbor Aggregation-Based Knowledge Graph Representation Learning Model Using Rule Mining
RMNA: A Neighbor Aggregation-Based Knowledge Graph Representation Learning Model Using Rule Mining Our code is based on Learning Attention-based Embed
Implicit Model Specialization through DAG-based Decentralized Federated Learning
Federated Learning DAG Experiments This repository contains software artifacts to reproduce the experiments presented in the Middleware '21 paper "Imp
Code and results accompanying our paper titled Mixture Proportion Estimation and PU Learning: A Modern Approach at Neurips 2021 (Spotlight)
Mixture Proportion Estimation and PU Learning: A Modern Approach This repository is the official implementation of Mixture Proportion Estimation and P
The code for the NeurIPS 2021 paper "A Unified View of cGANs with and without Classifiers".
Energy-based Conditional Generative Adversarial Network (ECGAN) This is the code for the NeurIPS 2021 paper "A Unified View of cGANs with and without
Analysis code and Latex source of the manuscript describing the conditional permutation test of confounding bias in predictive modelling.
Git repositoty of the manuscript entitled Statistical quantification of confounding bias in predictive modelling by Tamas Spisak The manuscript descri
PyTorch implementation of "PatchGame: Learning to Signal Mid-level Patches in Referential Games" to appear in NeurIPS 2021
PatchGame: Learning to Signal Mid-level Patches in Referential Games This repository is the official implementation of the paper - "PatchGame: Learnin
Bayes-Newton—A Gaussian process library in JAX, with a unifying view of approximate Bayesian inference as variants of Newton's algorithm.
Bayes-Newton Bayes-Newton is a library for approximate inference in Gaussian processes (GPs) in JAX (with objax), built and actively maintained by Wil
Companion repository to the paper accepted at the 4th ACM SIGSPATIAL International Workshop on Advances in Resilient and Intelligent Cities
Transfer learning approach to bicycle sharing systems station location planning using OpenStreetMap Companion repository to the paper accepted at the
Migration of Edge-based Distributed Federated Learning
FedFly: Towards Migration in Edge-based Distributed Federated Learning About the research Due to mobility, a device participating in Federated Learnin
Elucidating Robust Learning with Uncertainty-Aware Corruption Pattern Estimation
Elucidating Robust Learning with Uncertainty-Aware Corruption Pattern Estimation Introduction 📋 Official implementation of Explainable Robust Learnin
Companion code for the paper "Meta-Learning the Search Distribution of Black-Box Random Search Based Adversarial Attacks" by Yatsura et al.
META-RS This is the companion code for the paper "Meta-Learning the Search Distribution of Black-Box Random Search Based Adversarial Attacks" by Yatsu
A denoising diffusion probabilistic model synthesises galaxies that are qualitatively and physically indistinguishable from the real thing.
Realistic galaxy simulation via score-based generative models Official code for 'Realistic galaxy simulation via score-based generative models'. We us
Matplotlib Image labeller for classifying images
mpl-image-labeller Use Matplotlib to label images for classification. Works anywhere Matplotlib does - from the notebook to a standalone gui! For more
This is an auto-ML tool specialized in detecting of outliers
Auto-ML tool specialized in detecting of outliers Description This tool will allows you, with a Dash visualization, to compare 10 models of machine le
Use MATLAB to simulate the signal and extract features. Use PyTorch to build and train deep network to do spectrum sensing.
Deep-Learning-based-Spectrum-Sensing Use MATLAB to simulate the signal and extract features. Use PyTorch to build and train deep network to do spectru
Implementation for paper "Towards the Generalization of Contrastive Self-Supervised Learning"
Contrastive Self-Supervised Learning on CIFAR-10 Paper "Towards the Generalization of Contrastive Self-Supervised Learning", Weiran Huang, Mingyang Yi
The Simpsons and Machine Learning: What makes an Episode Great?
The Simpsons and Machine Learning: What makes an Episode Great? Check out my Medium article on this! PROBLEM: The Simpsons has had a decline in qualit
Unofficial implementation of the paper: PonderNet: Learning to Ponder in TensorFlow
PonderNet-TensorFlow This is an Unofficial Implementation of the paper: PonderNet: Learning to Ponder in TensorFlow. Official PyTorch Implementation:
PyTorch implementation of UPFlow (unsupervised optical flow learning)
UPFlow: Upsampling Pyramid for Unsupervised Optical Flow Learning By Kunming Luo, Chuan Wang, Shuaicheng Liu, Haoqiang Fan, Jue Wang, Jian Sun Megvii
DiscoNet: Learning Distilled Collaboration Graph for Multi-Agent Perception [NeurIPS 2021]
DiscoNet: Learning Distilled Collaboration Graph for Multi-Agent Perception [NeurIPS 2021] Yiming Li, Shunli Ren, Pengxiang Wu, Siheng Chen, Chen Feng
Implementation of our NeurIPS 2021 paper "A Bi-Level Framework for Learning to Solve Combinatorial Optimization on Graphs".
PPO-BiHyb This is the official implementation of our NeurIPS 2021 paper "A Bi-Level Framework for Learning to Solve Combinatorial Optimization on Grap
A large-scale database for graph representation learning
A large-scale database for graph representation learning
Machine Learning with JAX Tutorials
The purpose of this repo is to make it easy to get started with JAX. It contains my "Machine Learning with JAX" series of tutorials (YouTube videos and Jupyter Notebooks) as well as the content I found useful while learning JAX.
An original implementation of "MetaICL Learning to Learn In Context" by Sewon Min, Mike Lewis, Luke Zettlemoyer and Hannaneh Hajishirzi
MetaICL: Learning to Learn In Context This includes an original implementation of "MetaICL: Learning to Learn In Context" by Sewon Min, Mike Lewis, Lu
LILLIE: Information Extraction and Database Integration Using Linguistics and Learning-Based Algorithms
LILLIE: Information Extraction and Database Integration Using Linguistics and Learning-Based Algorithms Based on the work by Smith et al. (2021) Query
fMRIprep Pipeline To Machine Learning
fMRIprep Pipeline To Machine Learning(Demo) 所有配置均在config.py文件下定义 前置环境(lilab) 各个节点均安装docker,并有fmripre的镜像 可以使用conda中的base环境(相应的第三份包之后更新) 1. fmriprep scr
This repo represents all we learned and are learning in Data Structure course.
DataStructure Journey This repo represents all we learned and are learning in Data Structure course which is based on CLRS book and is being taught by
Decision Border Visualizer for Classification Algorithms
dbv Decision Border Visualizer for Classification Algorithms Project description A python package for Machine Learning Engineers who want to visualize
Code for "Discovering Non-monotonic Autoregressive Orderings with Variational Inference" (paper and code updated from ICLR 2021)
Discovering Non-monotonic Autoregressive Orderings with Variational Inference Description This package contains the source code implementation of the
An original implementation of "MetaICL Learning to Learn In Context" by Sewon Min, Mike Lewis, Luke Zettlemoyer and Hannaneh Hajishirzi
MetaICL: Learning to Learn In Context This includes an original implementation of "MetaICL: Learning to Learn In Context" by Sewon Min, Mike Lewis, Lu
A Comprehensive Study on Learning-Based PE Malware Family Classification Methods
A Comprehensive Study on Learning-Based PE Malware Family Classification Methods Datasets Because of copyright issues, both the MalwareBazaar dataset
The command line interface for Gradient - Gradient is an an end-to-end MLOps platform
Gradient CLI Get started: Create Account • Install CLI • Tutorials • Docs Resources: Website • Blog • Support • Contact Sales Gradient is an an end-to
Learning hidden low dimensional dyanmics using a Generalized Onsager Principle and neural networks
OnsagerNet Learning hidden low dimensional dyanmics using a Generalized Onsager Principle and neural networks This is the original pyTorch implemenati
Multi-Agent Reinforcement Learning for Active Voltage Control on Power Distribution Networks (MAPDN)
Multi-Agent Reinforcement Learning for Active Voltage Control on Power Distribution Networks (MAPDN) This is the implementation of the paper Multi-Age
Digital Twin Mobility Profiling: A Spatio-Temporal Graph Learning Approach
Digital Twin Mobility Profiling: A Spatio-Temporal Graph Learning Approach This is the implementation of traffic prediction code in DTMP based on PyTo
Pytorch implementation of Straight Sampling Network For Point Cloud Learning (ICIP2021).
Pytorch code for SS-Net This is a pytorch implementation of Straight Sampling Network For Point Cloud Learning (ICIP2021). Environment Code is tested
A project that uses optical flow and machine learning to detect aimhacking in video clips.
waldo-anticheat A project that aims to use optical flow and machine learning to visually detect cheating or hacking in video clips from fps games. Che
Implementation for HFGI: High-Fidelity GAN Inversion for Image Attribute Editing
HFGI: High-Fidelity GAN Inversion for Image Attribute Editing High-Fidelity GAN Inversion for Image Attribute Editing Update: We released the inferenc
Generic image compressor for machine learning. Pytorch code for our paper "Lossy compression for lossless prediction".
Lossy Compression for Lossless Prediction Using: Training: This repostiory contains our implementation of the paper: Lossy Compression for Lossless Pr
[ICCV'21] Learning Conditional Knowledge Distillation for Degraded-Reference Image Quality Assessment
CKDN The official implementation of the ICCV2021 paper "Learning Conditional Knowledge Distillation for Degraded-Reference Image Quality Assessment" O
Dynamic Visual Reasoning by Learning Differentiable Physics Models from Video and Language (NeurIPS 2021)
VRDP (NeurIPS 2021) Dynamic Visual Reasoning by Learning Differentiable Physics Models from Video and Language Mingyu Ding, Zhenfang Chen, Tao Du, Pin
Fast and Easy-to-use Distributed Graph Learning for PyTorch Geometric
Fast and Easy-to-use Distributed Graph Learning for PyTorch Geometric
A complete end-to-end machine learning portal that covers processes starting from model training to the model predicting results using FastAPI.
Machine Learning Portal Goal Application Workflow Process Design Live Project Goal A complete end-to-end machine learning portal that covers processes
Ralph is a command-line tool to fetch, extract, convert and push your tracking logs from various storage backends to your LRS or any other compatible storage or database backend.
Ralph is a command-line tool to fetch, extract, convert and push your tracking logs (aka learning events) from various storage backends to your
Short PhD seminar on Machine Learning Security (Adversarial Machine Learning)
Short PhD seminar on Machine Learning Security (Adversarial Machine Learning)
[NeurIPS 2021] Official implementation of paper "Learning to Simulate Self-driven Particles System with Coordinated Policy Optimization".
Code for Coordinated Policy Optimization Webpage | Code | Paper | Talk (English) | Talk (Chinese) Hi there! This is the source code of the paper “Lear
Anderson Acceleration for Deep Learning
Anderson Accelerated Deep Learning (AADL) AADL is a Python package that implements the Anderson acceleration to speed-up the training of deep learning
Deep Illuminator is a data augmentation tool designed for image relighting. It can be used to easily and efficiently generate a wide range of illumination variants of a single image.
Deep Illuminator Deep Illuminator is a data augmentation tool designed for image relighting. It can be used to easily and efficiently generate a wide
SurvITE: Learning Heterogeneous Treatment Effects from Time-to-Event Data
SurvITE: Learning Heterogeneous Treatment Effects from Time-to-Event Data SurvITE: Learning Heterogeneous Treatment Effects from Time-to-Event Data Au
Code for "Learning Graph Cellular Automata"
Learning Graph Cellular Automata This code implements the experiments from the NeurIPS 2021 paper: "Learning Graph Cellular Automata" Daniele Grattaro
Open source single image super-resolution toolbox containing various functionality for training a diverse number of state-of-the-art super-resolution models. Also acts as the companion code for the IEEE signal processing letters paper titled 'Improving Super-Resolution Performance using Meta-Attention Layers’.
Deep-FIR Codebase - Super Resolution Meta Attention Networks About This repository contains the main coding framework accompanying our work on meta-at
The official repository for "Intermediate Layers Matter in Momentum Contrastive Self Supervised Learning" paper.
Intermdiate layer matters - SSL The official repository for "Intermediate Layers Matter in Momentum Contrastive Self Supervised Learning" paper. Downl
Meta-Learning Sparse Implicit Neural Representations (NeurIPS 2021)
Meta-SparseINR Official PyTorch implementation of "Meta-learning Sparse Implicit Neural Representations" (NeurIPS 2021) by Jaeho Lee*, Jihoon Tack*, N
Node Dependent Local Smoothing for Scalable Graph Learning
Node Dependent Local Smoothing for Scalable Graph Learning Requirements Environments: Xeon Gold 5120 (CPU), 384GB(RAM), TITAN RTX (GPU), Ubuntu 16.04
A collection of Google research projects related to Federated Learning and Federated Analytics.
Federated Research Federated Research is a collection of research projects related to Federated Learning and Federated Analytics. Federated learning i
This repo is the official implementation of "L2ight: Enabling On-Chip Learning for Optical Neural Networks via Efficient in-situ Subspace Optimization".
L2ight is a closed-loop ONN on-chip learning framework to enable scalable ONN mapping and efficient in-situ learning. L2ight adopts a three-stage learning flow that first calibrates the complicated photonic circuit states under challenging physical constraints, then performs photonic core mapping via combined analytical solving and zeroth-order optimization.