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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/DavideScassola/graph-conditional-flow-matching
공식 코드Graph Conditional Flow Matching for Relational Data Generation
https://github.com/Deceptrax123/LGIN
공식 코드https://github.com/DeepGraphLearning/GNN-QE
공식 코드Neural-Symbolic Models for Logical Queries on Knowledge Graphs
https://github.com/DeepGraphLearning/NBFNet
공식 코드Neural Bellman-Ford Networks: A General Graph Neural Network Framework for Link Prediction
https://github.com/DennisCraandijk/DL-Abstract-Argumentation
공식 코드https://github.com/DeutscheKI/tevr-asr-tool
공식 코드TEVR: Improving Speech Recognition by Token Entropy Variance Reduction
https://github.com/DevSinghSachan/syntax-augmented-bert
공식 코드Do Syntax Trees Help Pre-trained Transformers Extract Information?
https://github.com/Di-Chai/FedEval
공식 코드A Survey for Federated Learning Evaluations: Goals and Measures
https://github.com/DistributedML/Biscotti
공식 코드Biscotti: A Ledger for Private and Secure Peer-to-Peer Machine Learning
https://github.com/DominiqueMercier/PPML-TSA
공식 코드https://github.com/DreamingRaven/python-reseal
공식 코드https://github.com/Drone-Banks/DICER-WWW-2021
공식 코드Dual Side Deep Context-aware Modulation for Social Recommendation
https://github.com/DynamicSanitizer/DySan
공식 코드https://github.com/EasyFL-AI/EasyFL
공식 코드https://github.com/EasyFL-AI/EasyFL/tree/master/applications/fedreid
공식 코드Performance Optimization for Federated Person Re-identification via Benchmark Analysis
https://github.com/EnergyWeatherAI/FL-Wind-NBM
공식 코드Wind turbine condition monitoring based on intra- and inter-farm federated learning
https://github.com/EnyanDai/NRGNN
공식 코드NRGNN: Learning a Label Noise-Resistant Graph Neural Network on Sparsely and Noisily Labeled Graphs
https://github.com/Eric-Lan0/FedES
공식 코드Communication Efficient and Privacy-Preserving Federated Learning Based on Evolution Strategies
https://github.com/FFrankyy/DrBC
공식 코드https://github.com/FYP-Air-Signature/Air-Signature
공식 코드Air Signing and Privacy-Preserving Signature Verification for Digital Documents
https://github.com/Fang6ang/KBGNN
공식 코드Kernel-based Substructure Exploration for Next POI Recommendation
https://github.com/FedML-AI/FedGraphNN
공식 코드FedGraphNN: A Federated Learning System and Benchmark for Graph Neural Networks
https://github.com/FedML-AI/FedML
공식 코드FedML-HE: An Efficient Homomorphic-Encryption-Based Privacy-Preserving Federated Learning System
https://github.com/FederatedAI/FATE-LLM
공식 코드FATE-LLM: A Industrial Grade Federated Learning Framework for Large Language Models
https://github.com/FederatedAI/research/tree/main/publications/FedCG
공식 코드https://github.com/FederatedSTAR/FedSTAR
공식 코드Privacy-preserving Speech Emotion Recognition through Semi-Supervised Federated Learning
https://github.com/Feiyu-Yao/Full-Body-Motion-Reconstruction-with-Sparse-Sensing-from-Graph-Perspective
공식 코드Full-Body Motion Reconstruction with Sparse Sensing from Graph Perspective
https://github.com/FengHZ/KD3A
공식 코드KD3A: Unsupervised Multi-Source Decentralized Domain Adaptation via Knowledge Distillation
https://github.com/FerranAlet/modular-metalearning
공식 코드https://github.com/FilippoMB/Benchmark_dataset_for_graph_classification
공식 코드Spectral Clustering with Graph Neural Networks for Graph Pooling
https://github.com/FilippoMB/Pyramidal-Reservoir-Graph-Nerual-Networks
공식 코드https://github.com/FilippoMB/Spectral-Clustering-with-Graph-Neural-Networks-for-Graph-Pooling
공식 코드Spectral Clustering with Graph Neural Networks for Graph Pooling
https://github.com/Frank-Wang-oss/FCSTGNN
공식 코드Fully-Connected Spatial-Temporal Graph for Multivariate Time-Series Data
https://github.com/Friger/GAGNN
공식 코드Group-Aware Graph Neural Network for Nationwide City Air Quality Forecasting
https://github.com/Friger/HighAir
공식 코드HighAir: A Hierarchical Graph Neural Network-Based Air Quality Forecasting Method
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