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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://github.com/AugustDS/synthetic-medical-benchmark

공식 코드

Overcoming Barriers to Data Sharing with Medical Image Generation: A Comprehensive Evaluation

https://github.com/Autumn945/MGNN-SPred

공식 코드

Beyond Clicks: Modeling Multi-Relational Item Graph for Session-Based Target Behavior Prediction

https://github.com/B-Deprez/BeGin

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Advances in Continual Graph Learning for Anti-Money Laundering Systems: A Comprehensive Review

https://github.com/BNN-UPC/ENERO

공식 코드

ENERO: Efficient Real-Time WAN Routing Optimization with Deep Reinforcement Learning

https://github.com/BNN-UPC/GNN-NIDS

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Unveiling the potential of Graph Neural Networks for robust Intrusion Detection

https://github.com/BUAABIGSCity/PDFormer

공식 코드

PDFormer: Propagation Delay-Aware Dynamic Long-Range Transformer for Traffic Flow Prediction

https://github.com/BUPT-GAMMA/OpenHGNN

공식 코드

Towards Relation-centered Pooling and Convolution for Heterogeneous Graph Learning Networks

https://github.com/BUserName/PGL

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Progressive Graph Learning for Open-Set Domain Adaptation

https://github.com/BangLiu/GIANT

공식 코드

GIANT: Scalable Creation of a Web-scale Ontology

https://github.com/BaoWangMath/DP-LSSGD

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DP-LSSGD: A Stochastic Optimization Method to Lift the Utility in Privacy-Preserving ERM

https://github.com/BaratiLab/OFormer

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Transformer for Partial Differential Equations' Operator Learning

https://github.com/BiDAlab/DiveFace

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SensitiveNets: Learning Agnostic Representations with Application to Face Images

https://github.com/BingJiaChen/few-shot-indoor-localization

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Few-Shot Transfer Learning for Device-Free Fingerprinting Indoor Localization

https://github.com/BioinfoMachineLearning/DeepRefine

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EGR: Equivariant Graph Refinement and Assessment of 3D Protein Complex Structures

https://github.com/Breezelled/COMODO

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COMODO: Cross-Modal Video-to-IMU Distillation for Efficient Egocentric Human Activity Recognition

https://github.com/CAMMA-public/ORPose-Depth

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Human Pose Estimation on Privacy-Preserving Low-Resolution Depth Images

https://github.com/CANVOLCANO/DPMAC

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DPMAC: Differentially Private Communication for Cooperative Multi-Agent Reinforcement Learning

https://github.com/CIAM-Group/NCO_code/tree/main/single_objective/UDC-Large-scale-CO-master

공식 코드

UDC: A Unified Neural Divide-and-Conquer Framework for Large-Scale Combinatorial Optimization Problems

https://github.com/CODE-SUBMIT/Graph_Neighborhood_1

공식 코드

Mutual Information Maximization in Graph Neural Networks

https://github.com/CODE-SUBMIT/Neighborhood-Enlargement-in-Graph-Network

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Mutual Information Maximization in Graph Neural Networks

https://github.com/COMPiLELab/SubDEQ

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Subhomogeneous Deep Equilibrium Models

https://github.com/CRIPAC-DIG/A-PGNN

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Personalized Graph Neural Networks with Attention Mechanism for Session-Aware Recommendation

https://github.com/CRIPAC-DIG/TAGNN

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TAGNN: Target Attentive Graph Neural Networks for Session-based Recommendation

https://github.com/CaoQi92/CoupledGNN

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Popularity Prediction on Social Platforms with Coupled Graph Neural Networks

https://github.com/CederGroupHub/chgnet

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CHGNet: Pretrained universal neural network potential for charge-informed atomistic modeling

https://github.com/ChandlerBang/Pro-GNN

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Graph Structure Learning for Robust Graph Neural Networks

https://github.com/CharlieDinh/FEDL

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Federated Learning over Wireless Networks: Convergence Analysis and Resource Allocation

https://github.com/CharlieDinh/pFedMe

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Personalized Federated Learning with Moreau Envelopes

https://github.com/Chen-Cai-OSU/GNN-Over-Smoothing

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A Note on Over-Smoothing for Graph Neural Networks

https://github.com/Cra2yDavid/Powerformer

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Powerformer: A Section-adaptive Transformer for Power Flow Adjustment

https://github.com/CrawlScript/RpHGNN

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Efficient Heterogeneous Graph Learning via Random Projection

https://github.com/DPBayes/DPPVI

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Differentially private partitioned variational inference

https://github.com/DPBayes/data-sharing-examples

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Privacy-preserving data sharing via probabilistic modelling

https://github.com/DSE-MSU/DeepRobust

공식 코드

Graph Structure Learning for Robust Graph Neural Networks

https://github.com/DanieleGammelli/gnn-rl-for-amod

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Graph Neural Network Reinforcement Learning for Autonomous Mobility-on-Demand Systems

https://github.com/DashanGao/Federated-Transfer-Learning-for-EEG

공식 코드

Federated Transfer Learning for EEG Signal Classification

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