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Privacy Preserving

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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://bitbucket.org/aauvap/ctgnn

공식 코드

Multi-Task Classification of Sewer Pipe Defects and Properties using a Cross-Task Graph Neural Network Decoder

https://bitbucket.org/uwtppml/idash2019

공식 코드

High Performance Logistic Regression for Privacy-Preserving Genome Analysis

https://bitbucket.org/uwtppml/privfair

공식 코드

PrivFair: a Library for Privacy-Preserving Fairness Auditing

https://git.rwth-aachen.de/netsci/publication-scale-dependent-node-roles

공식 코드

Local, global and scale-dependent node roles

https://github.com/000Justin000/GBPN

공식 코드

Graph Belief Propagation Networks

https://github.com/000Justin000/gnn-residual-correlation

공식 코드

Residual Correlation in Graph Neural Network Regression

https://github.com/0215arthur/hg-gnn

공식 코드

Heterogeneous Global Graph Neural Networks for Personalized Session-based Recommendation

https://github.com/1041877801/COMBHelper

공식 코드

COMBHelper: A Neural Approach to Reduce Search Space for Graph Combinatorial Problems

https://github.com/1wrx1/GIA

공식 코드

Exploring the Vulnerabilities of Federated Learning: A Deep Dive into Gradient Inversion Attacks

https://github.com/2024sig/sig

공식 코드

SIG: Efficient Self-Interpretable Graph Neural Network for Continuous-time Dynamic Graphs

https://github.com/3dem/model-angelo

공식 코드

A Graph Neural Network Approach to Automated Model Building in Cryo-EM Maps

https://github.com/3dlg-hcvc/plan2scene

공식 코드

Plan2Scene: Converting Floorplans to 3D Scenes

https://github.com/3dperceptionlab/biotacsp-stability-set-v2

공식 코드

TactileGCN: A Graph Convolutional Network for Predicting Grasp Stability with Tactile Sensors

https://github.com/401208163/FedPN

공식 코드

FedPH: Privacy-enhanced Heterogeneous Federated Learning

https://github.com/564612540/GLASU

공식 코드

GLASU: A Communication-Efficient Algorithm for Federated Learning with Vertically Distributed Graph Data

https://github.com/77yq77/scbit

공식 코드

scBIT: Integrating Single-cell Transcriptomic Data into fMRI-based Prediction for Alzheimer's Disease Diagnosis

https://github.com/82magnolia/event_localization

공식 코드

Privacy-Preserving Visual Localization with Event Cameras

https://github.com/ACM-MM-2020-Submission/Submission123

공식 코드

Adversarial Privacy-preserving Filter

https://github.com/AI-secure/DataLens

공식 코드

DataLens: Scalable Privacy Preserving Training via Gradient Compression and Aggregation

https://github.com/AIforGreatGood/charge3net

공식 코드

Higher-Order Equivariant Neural Networks for Charge Density Prediction in Materials

https://github.com/AIoT-Lab-BKAI/PIMA

공식 코드

A Novel Approach for Pill-Prescription Matching with GNN Assistance and Contrastive Learning

https://github.com/AaltoPML/Rethinking-pooling-in-GNNs

공식 코드

Rethinking pooling in graph neural networks

https://github.com/Abrar2652/Network-Intrusion-Detection

공식 코드

CAGN-GAT Fusion: A Hybrid Contrastive Attentive Graph Neural Network for Network Intrusion Detection

https://github.com/Academic-Hammer/SciTSR

공식 코드

Complicated Table Structure Recognition

https://github.com/Acciorocketships/pymarl2

공식 코드

QGNN: Value Function Factorisation with Graph Neural Networks

https://github.com/Alex-Mathai-98/Monolith-to-Microservices

공식 코드

Monolith to Microservices: Representing Application Software through Heterogeneous Graph Neural Network

https://github.com/AlexMV12/PyCrCNN

공식 코드

A Privacy-Preserving Distributed Architecture for Deep-Learning-as-a-Service

https://github.com/AliSaadatV/Causal-Gene-Pathway-Finder

공식 코드

DNA Language Model and Interpretable Graph Neural Network Identify Genes and Pathways Involved in Rare Diseases

https://github.com/Amit507017/FedCollabNN

공식 코드

Private Dataset Generation Using Privacy Preserving Collaborative Learning

https://github.com/AmitRoy7781/SST-GNN

공식 코드

SST-GNN: Simplified Spatio-temporal Traffic forecasting model using Graph Neural Network

https://github.com/AmitRoy7781/USTGCN

공식 코드

Unified Spatio-Temporal Modeling for Traffic Forecasting using Graph Neural Network

https://github.com/AndreasOpedal/ba-graph-disentanglement

공식 코드

Recovering Barabási-Albert Parameters of Graphs through Disentanglement

https://github.com/AnimeshSinha1309/qroute-router

공식 코드

Qubit Routing using Graph Neural Network aided Monte Carlo Tree Search

https://github.com/AnsongLi/Disen-GNN

공식 코드

Disentangled Graph Neural Networks for Session-based Recommendation

https://github.com/AnsongLi/TIE-DGNN

공식 코드

Transition Information Enhanced Disentangled Graph Neural Networks for Session-based Recommendation

https://github.com/AsalMehradfar/FALCON

공식 코드

FALCON: An ML Framework for Fully Automated Layout-Constrained Analog Circuit Design

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.