1974 Repositories
Python resilient-swarm-communications-with-meta-graph-convolutional-networks Libraries
Implementation of Auto-Conditioned Recurrent Networks for Extended Complex Human Motion Synthesis
acLSTM_motion This folder contains an implementation of acRNN for the CMU motion database written in Pytorch. See the following links for more backgro
Something I built to test for Log4J vulnerabilities on customer networks.
Log4J-Scanner Something I built to test for Log4J vulnerabilities on customer networks. I'm not responsible if your computer blows up, catches fire or
HTTP graph database built in Python 3
KiwiDB HTTP graph database built in Python 3. Reference Format References are strings in the format: {refIDENTIFIER@GROUP} Authentication Currently, t
Almost State-of-the-art Text Generation library
Ps: we are adding transformer model soon Text Gen 🐐 Almost State-of-the-art Text Generation library Text gen is a python library that allow you build
Codes for building and training the neural network model described in Domain-informed neural networks for interaction localization within astroparticle experiments.
Domain-informed Neural Networks Codes for building and training the neural network model described in Domain-informed neural networks for interaction
This repository contain code on Novelty-Driven Binary Particle Swarm Optimisation for Truss Optimisation Problems.
This repository contain code on Novelty-Driven Binary Particle Swarm Optimisation for Truss Optimisation Problems. The main directory include the code
Pytorch implementation of our paper under review -- 1xN Pattern for Pruning Convolutional Neural Networks
1xN Pattern for Pruning Convolutional Neural Networks (paper) . This is Pytorch re-implementation of "1xN Pattern for Pruning Convolutional Neural Net
Additional environments compatible with OpenAI gym
Decentralized Control of Quadrotor Swarms with End-to-end Deep Reinforcement Learning A codebase for training reinforcement learning policies for quad
[ICCV 2021] Target Adaptive Context Aggregation for Video Scene Graph Generation
Target Adaptive Context Aggregation for Video Scene Graph Generation This is a PyTorch implementation for Target Adaptive Context Aggregation for Vide
Code for the ICCV'21 paper "Context-aware Scene Graph Generation with Seq2Seq Transformers"
ICCV'21 Context-aware Scene Graph Generation with Seq2Seq Transformers Authors: Yichao Lu*, Himanshu Rai*, Cheng Chang*, Boris Knyazev†, Guangwei Yu,
Reference code for the paper "Cross-Camera Convolutional Color Constancy" (ICCV 2021)
Cross-Camera Convolutional Color Constancy, ICCV 2021 (Oral) Mahmoud Afifi1,2, Jonathan T. Barron2, Chloe LeGendre2, Yun-Ta Tsai2, and Francois Bleibe
Implementation of Nalbach et al. 2017 paper.
Deep Shading Convolutional Neural Networks for Screen-Space Shading Our project is based on Nalbach et al. 2017 paper. In this project, a set of buffe
Library extending Jupyter notebooks to integrate with Apache TinkerPop and RDF SPARQL.
Graph Notebook: easily query and visualize graphs The graph notebook provides an easy way to interact with graph databases using Jupyter notebooks. Us
A Persistent Embedded Graph Database for Python
Cog - Embedded Graph Database for Python cogdb.io New release: 2.0.5! Installing Cog pip install cogdb Cog is a persistent embedded graph database im
This is a Boids Simulation, written in Python with Pygame.
PyNBoids A Python Boids Simulation This is a Boids simulation, written in Python3, with Pygame2 and NumPy. To use: Save the pynboids_sp.py file (and n
GANformer: Generative Adversarial Transformers
GANformer: Generative Adversarial Transformers Drew A. Hudson* & C. Lawrence Zitnick Update: We released the new GANformer2 paper! *I wish to thank Ch
Edge-Augmented Graph Transformer
Edge-augmented Graph Transformer Introduction This is the official implementation of the Edge-augmented Graph Transformer (EGT) as described in https:
This is the repository for the NeurIPS-21 paper [Contrastive Graph Poisson Networks: Semi-Supervised Learning with Extremely Limited Labels].
CGPN This is the repository for the NeurIPS-21 paper [Contrastive Graph Poisson Networks: Semi-Supervised Learning with Extremely Limited Labels]. Req
Reimplementation of NeurIPS'19: "Meta-Weight-Net: Learning an Explicit Mapping For Sample Weighting" by Shu et al.
[Re] Meta-Weight-Net: Learning an Explicit Mapping For Sample Weighting Reimplementation of NeurIPS'19: "Meta-Weight-Net: Learning an Explicit Mapping
Efficient and Scalable Physics-Informed Deep Learning and Scientific Machine Learning on top of Tensorflow for multi-worker distributed computing
Notice: Support for Python 3.6 will be dropped in v.0.2.1, please plan accordingly! Efficient and Scalable Physics-Informed Deep Learning Collocation-
PyDEns is a framework for solving Ordinary and Partial Differential Equations (ODEs & PDEs) using neural networks
PyDEns PyDEns is a framework for solving Ordinary and Partial Differential Equations (ODEs & PDEs) using neural networks. With PyDEns one can solve PD
Physics-Informed Neural Networks (PINN) and Deep BSDE Solvers of Differential Equations for Scientific Machine Learning (SciML) accelerated simulation
NeuralPDE NeuralPDE.jl is a solver package which consists of neural network solvers for partial differential equations using scientific machine learni
Sparse Physics-based and Interpretable Neural Networks
Sparse Physics-based and Interpretable Neural Networks for PDEs This repository contains the code and manuscript for research done on Sparse Physics-b
Implementation of Research Paper "Learning to Enhance Low-Light Image via Zero-Reference Deep Curve Estimation"
Zero-DCE and Zero-DCE++(Lite architechture for Mobile and edge Devices) Papers Abstract The paper presents a novel method, Zero-Reference Deep Curve E
People Interaction Graph
Gihan Jayatilaka*, Jameel Hassan*, Suren Sritharan*, Janith Senananayaka, Harshana Weligampola, et. al., 2021. Holistic Interpretation of Public Scenes Using Computer Vision and Temporal Graphs to Identify Social Distancing Violations. arXiv preprint.
An implementation of quantum convolutional neural network with MindQuantum. Huawei, classifying MNIST dataset
关于实现的一点说明 山东大学 2020级 苏博南 www.subonan.com 文件说明 tools.py 这里面主要有两个函数: resize(a, lenb) 这其实是我找同学写的一个小算法hhh。给出一个$28\times 28$的方阵a,返回一个$lenb\times lenb$的方阵。因
Decentralised graph database management system
Decentralised graph database management system To get started clone the repo, and run the command below. python3 database.py Now, create a new termina
Quickly visualize docker networks with graphviz.
Docker Network Graph Visualize the relationship between Docker networks and containers as a neat graphviz graph. Example Usage usage: docker-net-graph
This repository is the official implementation of the Hybrid Self-Attention NEAT algorithm.
This repository is the official implementation of the Hybrid Self-Attention NEAT algorithm. It contains the code to reproduce the results presented in the original paper: https://arxiv.org/abs/2112.03670
Hierarchical Clustering: O(1)-Approximation for Well-Clustered Graphs
Hierarchical Clustering: O(1)-Approximation for Well-Clustered Graphs This repository contains code to accompany the paper "Hierarchical Clustering: O
An official source code for paper Deep Graph Clustering via Dual Correlation Reduction, accepted by AAAI 2022
Dual Correlation Reduction Network An official source code for paper Deep Graph Clustering via Dual Correlation Reduction, accepted by AAAI 2022. Any
Hunt down social media accounts by username across social networks
Hunt down social media accounts by username across social networks Installation | Usage | Docker Notes | Contributing Installation # clone the repo $
Recursive Bayesian Networks
Recursive Bayesian Networks This repository contains the code to reproduce the results from the NeurIPS 2021 paper Lieck R, Rohrmeier M (2021) Recursi
[AAAI 2022] Sparse Structure Learning via Graph Neural Networks for Inductive Document Classification
Sparse Structure Learning via Graph Neural Networks for inductive document classification Make graph dataset create co-occurrence graph for datasets.
Code for Contrastive-Geometry Networks for Generalized 3D Pose Transfer
CGTransformer Code for our AAAI 2022 paper "Contrastive-Geometry Transformer network for Generalized 3D Pose Transfer" Contrastive-Geometry Transforme
A high-performance distributed deep learning system targeting large-scale and automated distributed training.
HETU Documentation | Examples Hetu is a high-performance distributed deep learning system targeting trillions of parameters DL model training, develop
A computational block to solve entity alignment over textual attributes in a knowledge graph creation pipeline.
How to apply? Create your config.ini file following the example provided in config.ini Choose one of the options below to run: Run with Python3 pip in
Artifacts for paper "MMO: Meta Multi-Objectivization for Software Configuration Tuning"
MMO: Meta Multi-Objectivization for Software Configuration Tuning This repository contains the data and code for the following paper that is currently
AI4Good project for detecting waste in the environment
Detect waste AI4Good project for detecting waste in environment. www.detectwaste.ml. Our latest results were published in Waste Management journal in
Generate high quality pictures. GAN. Generative Adversarial Networks
ESRGAN generate high quality pictures. GAN. Generative Adversarial Networks """ Super-resolution of CelebA using Generative Adversarial Networks. The
The source code of the paper "SHGNN: Structure-Aware Heterogeneous Graph Neural Network"
SHGNN: Structure-Aware Heterogeneous Graph Neural Network The source code and dataset of the paper: SHGNN: Structure-Aware Heterogeneous Graph Neural
Some code of the implements of Geological Modeling Using 3D Pixel-Adaptive and Deformable Convolutional Neural Network
3D-GMPDCNN Geological Modeling Using 3D Pixel-Adaptive and Deformable Convolutional Neural Network PyTorch implementation of "Geological Modeling Usin
PyGAD, a Python 3 library for building the genetic algorithm and training machine learning algorithms (Keras & PyTorch).
PyGAD: Genetic Algorithm in Python PyGAD is an open-source easy-to-use Python 3 library for building the genetic algorithm and optimizing machine lear
FinRL-Meta: A Universe for Data-Driven Financial Reinforcement Learning. 🔥
FinRL-Meta: A Universe of Market Environments. FinRL-Meta is a universe of market environments for data-driven financial reinforcement learning. Users
Semantic graph parser based on Categorial grammars
Lambekseq "Everyone who failed Greek or Latin hates it." This package is for proving theorems in Categorial grammars (CG) and constructing semantic gr
RDFLib is a Python library for working with RDF, a simple yet powerful language for representing information.
RDFLib RDFLib is a pure Python package for working with RDF. RDFLib contains most things you need to work with RDF, including: parsers and serializers
PyG (PyTorch Geometric) - A library built upon PyTorch to easily write and train Graph Neural Networks (GNNs)
PyG (PyTorch Geometric) is a library built upon PyTorch to easily write and train Graph Neural Networks (GNNs) for a wide range of applications related to structured data.
Official code for Next Check-ins Prediction via History and Friendship on Location-Based Social Networks (MDM 2018)
MUC Next Check-ins Prediction via History and Friendship on Location-Based Social Networks (MDM 2018) Performance Details for Accuracy: | Dataset
Official implementation of the article "Unsupervised JPEG Domain Adaptation For Practical Digital Forensics"
Unsupervised JPEG Domain Adaptation for Practical Digital Image Forensics @WIFS2021 (Montpellier, France) Rony Abecidan, Vincent Itier, Jeremie Boulan
Diverse graph algorithms implemented using JGraphT library.
# 1. Installing Maven & Pandas First, please install Java (JDK11) and Python 3 if they are not already. Next, make sure that Maven (for importing J
Official Implementation of "LUNAR: Unifying Local Outlier Detection Methods via Graph Neural Networks"
LUNAR Official Implementation of "LUNAR: Unifying Local Outlier Detection Methods via Graph Neural Networks" Adam Goodge, Bryan Hooi, Ng See Kiong and
Official PyTorch implementation of the paper "Graph-based Generative Face Anonymisation with Pose Preservation" in ICIAP 2021
Contents AnonyGAN Installation Dataset Preparation Generating Images Using Pretrained Model Train and Test New Models Evaluation Acknowledgments Citat
Code for Contrastive-Geometry Networks for Generalized 3D Pose Transfer
Code for Contrastive-Geometry Networks for Generalized 3D Pose Transfer
A PyTorch library and evaluation platform for end-to-end compression research
CompressAI CompressAI (compress-ay) is a PyTorch library and evaluation platform for end-to-end compression research. CompressAI currently provides: c
A simple Monte Carlo simulation using Python and matplotlib library
Monte Carlo python simulation Install linux dependencies sudo apt update sudo apt install build-essential \ software-properties-commo
A library for researching neural networks compression and acceleration methods.
A library for researching neural networks compression and acceleration methods.
1st Solution For NeurIPS 2021 Competition on ML4CO Dual Task
KIDA: Knowledge Inheritance in Data Aggregation This project releases our 1st place solution on NeurIPS2021 ML4CO Dual Task. Slide and model weights a
A small library for doing fluid simulation with neural networks.
Neural Fluid Fields This is a small library for doing fluid simulation with neural fields. Check out our review paper, Neural Fields in Visual Computi
In-memory Graph Database and Knowledge Graph with Natural Language Interface, compatible with Pandas
CogniPy for Pandas - In-memory Graph Database and Knowledge Graph with Natural Language Interface Whats in the box Reasoning, exploration of RDF/OWL,
Official implementation of the paper "Light Field Networks: Neural Scene Representations with Single-Evaluation Rendering"
Light Field Networks Project Page | Paper | Data | Pretrained Models Vincent Sitzmann*, Semon Rezchikov*, William Freeman, Joshua Tenenbaum, Frédo Dur
WSDM2022 Challenge - Large scale temporal graph link prediction
WSDM 2022 Large-scale Temporal Graph Link Prediction - Baseline and Initial Test Set WSDM Cup Website link Link to this challenge This branch offers A
Plenoxels: Radiance Fields without Neural Networks
Plenoxels: Radiance Fields without Neural Networks Alex Yu*, Sara Fridovich-Keil*, Matthew Tancik, Qinhong Chen, Benjamin Recht, Angjoo Kanazawa UC Be
Implementation of Heterogeneous Graph Attention Network
HetGAN Implementation of Heterogeneous Graph Attention Network This is the code repository of paper "Prediction of Metro Ridership During the COVID-19
A modular PyTorch library for optical flow estimation using neural networks
A modular PyTorch library for optical flow estimation using neural networks
Scripts and outputs related to the paper Prediction of Adverse Biological Effects of Chemicals Using Knowledge Graph Embeddings.
Knowledge Graph Embeddings and Chemical Effect Prediction, 2020. Scripts and outputs related to the paper Prediction of Adverse Biological Effects of
Enhancing Column Generation by a Machine-Learning-BasedPricing Heuristic for Graph Coloring
Enhancing Column Generation by a Machine-Learning-BasedPricing Heuristic for Graph Coloring (to appear at AAAI 2022) We propose a machine-learning-bas
The source code for Adaptive Kernel Graph Neural Network at AAAI2022
AKGNN The source code for Adaptive Kernel Graph Neural Network at AAAI2022. Please cite our paper if you think our work is helpful to you: @inproceedi
Wikidated : An Evolving Knowledge Graph Dataset of Wikidata’s Revision History
Wikidated Wikidated 1.0 is a dataset of Wikidata’s full revision history, which encodes changes between Wikidata revisions as sets of deletions and ad
Codes for TIM2021 paper "Anchor-Based Spatio-Temporal Attention 3-D Convolutional Networks for Dynamic 3-D Point Cloud Sequences"
Codes for TIM2021 paper "Anchor-Based Spatio-Temporal Attention 3-D Convolutional Networks for Dynamic 3-D Point Cloud Sequences"
Supplemental learning materials for "Fourier Feature Networks and Neural Volume Rendering"
Fourier Feature Networks and Neural Volume Rendering This repository is a companion to a lecture given at the University of Cambridge Engineering Depa
Plenoxels: Radiance Fields without Neural Networks, Code release WIP
Plenoxels: Radiance Fields without Neural Networks Alex Yu*, Sara Fridovich-Keil*, Matthew Tancik, Qinhong Chen, Benjamin Recht, Angjoo Kanazawa UC Be
[KDD 2021, Research Track] DiffMG: Differentiable Meta Graph Search for Heterogeneous Graph Neural Networks
DiffMG This repository contains the code for our KDD 2021 Research Track paper: DiffMG: Differentiable Meta Graph Search for Heterogeneous Graph Neura
NeurIPS'19: Meta-Weight-Net: Learning an Explicit Mapping For Sample Weighting (Pytorch implementation for noisy labels).
Meta-Weight-Net NeurIPS'19: Meta-Weight-Net: Learning an Explicit Mapping For Sample Weighting (Official Pytorch implementation for noisy labels). The
[NeurIPS 2020] Code for the paper "Balanced Meta-Softmax for Long-Tailed Visual Recognition"
Balanced Meta-Softmax Code for the paper Balanced Meta-Softmax for Long-Tailed Visual Recognition Jiawei Ren, Cunjun Yu, Shunan Sheng, Xiao Ma, Haiyu
[CVPR 2021] MetaSAug: Meta Semantic Augmentation for Long-Tailed Visual Recognition
MetaSAug: Meta Semantic Augmentation for Long-Tailed Visual Recognition (CVPR 2021) arXiv Prerequisite PyTorch = 1.2.0 Python3 torchvision PIL argpar
Conflict-aware Inference of Python Compatible Runtime Environments with Domain Knowledge Graph, ICSE 2022
PyCRE Conflict-aware Inference of Python Compatible Runtime Environments with Domain Knowledge Graph, ICSE 2022 Dependencies This project is developed
A modular application for performing anomaly detection in networks
Deep-Learning-Models-for-Network-Annomaly-Detection The modular app consists for mainly three annomaly detection algorithms. The system supports model
Official Implementation of "LUNAR: Unifying Local Outlier Detection Methods via Graph Neural Networks"
LUNAR Official Implementation of "LUNAR: Unifying Local Outlier Detection Methods via Graph Neural Networks" Adam Goodge, Bryan Hooi, Ng See Kiong and
This repository is an implementation of paper : Improving the Training of Graph Neural Networks with Consistency Regularization
CRGNN Paper : Improving the Training of Graph Neural Networks with Consistency Regularization Environments Implementing environment: GeForce RTX™ 3090
Junction Tree Variational Autoencoder for Molecular Graph Generation (ICML 2018)
Junction Tree Variational Autoencoder for Molecular Graph Generation Official implementation of our Junction Tree Variational Autoencoder https://arxi
Meandering In Networks of Entities to Reach Verisimilar Answers
MINERVA Meandering In Networks of Entities to Reach Verisimilar Answers Code and models for the paper Go for a Walk and Arrive at the Answer - Reasoni
Federated Deep Reinforcement Learning for the Distributed Control of NextG Wireless Networks.
FDRL-PC-Dyspan Federated Deep Reinforcement Learning for the Distributed Control of NextG Wireless Networks. This repository contains the entire code
The best solution of the Weather Prediction track in the Yandex Shifts challenge
yandex-shifts-weather The repository contains information about my solution for the Weather Prediction track in the Yandex Shifts challenge https://re
RID-Noise: Towards Robust Inverse Design under Noisy Environments
This is code of RID-Noise. Reproduce RID-Noise Results Toy tasks Please refer to the notebook ridnoise.ipynb to view experiments on three toy tasks. B
A new framework, collaborative cascade prediction based on graph neural networks (CCasGNN) to jointly utilize the structural characteristics, sequence features, and user profiles.
CCasGNN A new framework, collaborative cascade prediction based on graph neural networks (CCasGNN) to jointly utilize the structural characteristics,
"Graph Neural Controlled Differential Equations for Traffic Forecasting", AAAI 2022
Graph Neural Controlled Differential Equations for Traffic Forecasting Setup Python environment for STG-NCDE Install python environment $ conda env cr
Flexible Networks for Learning Physical Dynamics of Deformable Objects (2021)
Flexible Networks for Learning Physical Dynamics of Deformable Objects (2021) By Jinhyung Park, Dohae Lee, In-Kwon Lee from Yonsei University (Seoul,
SalFBNet: Learning Pseudo-Saliency Distribution via Feedback Convolutional Networks
SalFBNet This repository includes Pytorch implementation for the following paper: SalFBNet: Learning Pseudo-Saliency Distribution via Feedback Convolu
Graph WaveNet apdapted for brain connectivity analysis.
Graph WaveNet for brain network analysis This is the implementation of the Graph WaveNet model used in our manuscript: S. Wein , A. Schüller, A. M. To
This repository is an implementation of paper : Improving the Training of Graph Neural Networks with Consistency Regularization
CRGNN Paper : Improving the Training of Graph Neural Networks with Consistency Regularization Environments Implementing environment: GeForce RTX™ 3090
Bayesian Deep Learning and Deep Reinforcement Learning for Object Shape Error Response and Correction of Manufacturing Systems
Bayesian Deep Learning for Manufacturing 2.0 (dlmfg) Object Shape Error Response (OSER) Digital Lifecycle Management - In Process Quality Improvement
Self-Supervised Learning of Event-based Optical Flow with Spiking Neural Networks
Self-Supervised Learning of Event-based Optical Flow with Spiking Neural Networks Work accepted at NeurIPS'21 [paper, video]. If you use this code in
An Implementation of Transformer in Transformer in TensorFlow for image classification, attention inside local patches
Transformer-in-Transformer An Implementation of the Transformer in Transformer paper by Han et al. for image classification, attention inside local pa
A python script to visualise explain plans as a graph using graphviz
README Needs to be improved Prerequisites Need to have graphiz installed on the machine. Refer to https://graphviz.readthedocs.io/en/stable/manual.htm
Federated learning on graph, especially on graph neural networks (GNNs), knowledge graph, and private GNN.
Federated learning on graph, especially on graph neural networks (GNNs), knowledge graph, and private GNN.
Papers about explainability of GNNs
Papers about explainability of GNNs
Pytorch code for "Text-Independent Speaker Verification Using 3D Convolutional Neural Networks".
:speaker: Deep Learning & 3D Convolutional Neural Networks for Speaker Verification
Extracts data from the database for a graph-node and stores it in parquet files
subgraph-extractor Extracts data from the database for a graph-node and stores it in parquet files Installation For developing, it's recommended to us
Unsupervised Image to Image Translation with Generative Adversarial Networks
Unsupervised Image to Image Translation with Generative Adversarial Networks Paper: Unsupervised Image to Image Translation with Generative Adversaria
The implementation code for "DAGAN: Deep De-Aliasing Generative Adversarial Networks for Fast Compressed Sensing MRI Reconstruction"
DAGAN This is the official implementation code for DAGAN: Deep De-Aliasing Generative Adversarial Networks for Fast Compressed Sensing MRI Reconstruct