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Privacy Preserving
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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/chromaprim/SHGCL_DTI_GWT
공식 코드https://github.com/chroneus/stylematte
공식 코드https://github.com/chrsmrrs/speqnets
공식 코드SpeqNets: Sparsity-aware Permutation-equivariant Graph Networks
https://github.com/chrsmrrs/tudataset
공식 코드TUDataset: A collection of benchmark datasets for learning with graphs
https://github.com/chunbaobao/genet
공식 코드GeNet: A Graph Neural Network-based Anti-noise Task-Oriented Semantic Communication Paradigm
https://github.com/chung-ju/unitrans
공식 코드https://github.com/chymaera96/neuralsampleid
공식 코드Refining music sample identification with a self-supervised graph neural network
https://github.com/ciam-group/nco_code
공식 코드https://github.com/cis-group/molnet
공식 코드MolNet: A Chemically Intuitive Graph Neural Network for Prediction of Molecular Properties
https://github.com/cjbaq-origami/origami_inference
공식 코드Privacy-Preserving Inference in Machine Learning Services Using Trusted Execution Environments
https://github.com/cjpcool/PR-GAT
공식 코드https://github.com/cjpcool/customgnn
공식 코드https://github.com/cjx96/c2dsr
공식 코드https://github.com/ckano/pfcr
공식 코드Prompt-enhanced Federated Content Representation Learning for Cross-domain Recommendation
https://github.com/clatfd/GNN-ART-LABEL
공식 코드Automated Intracranial Artery Labeling using a Graph Neural Network and Hierarchical Refinement
https://github.com/claudiunz/tnwahi-appakrcs
공식 코드https://github.com/clear-nus/TactileSGNet
공식 코드TactileSGNet: A Spiking Graph Neural Network for Event-based Tactile Object Recognition
https://github.com/cleverhans-lab/decaph
공식 코드Decentralised, Collaborative, and Privacy-preserving Machine Learning for Multi-Hospital Data
https://github.com/cliffordlab/edgecameratracking
공식 코드https://github.com/clin0212/awesome-federated-llm-learning
공식 코드https://github.com/cmry/reap
공식 코드Adversarial Stylometry in the Wild: Transferable Lexical Substitution Attacks on Author Profiling
https://github.com/cmu-safari/pygim
공식 코드PyGim: An Efficient Graph Neural Network Library for Real Processing-In-Memory Architectures
https://github.com/coarse-graining/cgnet
공식 코드Coarse Graining Molecular Dynamics with Graph Neural Networks
https://github.com/cobypenso/local-differential-private-conformal-prediction
공식 코드Privacy-Preserving Conformal Prediction Under Local Differential Privacy
https://github.com/codexhammer/CUL
공식 코드https://github.com/codexxxl/GraphVQA
공식 코드GraghVQA: Language-Guided Graph Neural Networks for Graph-based Visual Question Answering
https://github.com/cognitive-robots/sem-gat
공식 코드SEM-GAT: Explainable Semantic Pose Estimation using Learned Graph Attention
https://github.com/coleygroup/polymer-chemprop-data
공식 코드A graph representation of molecular ensembles for polymer property prediction
https://github.com/colorfulfuture/gc-vrnn
공식 코드Uncovering the Missing Pattern: Unified Framework Towards Trajectory Imputation and Prediction
https://github.com/compasszzn/degnn
공식 코드Relaxing Continuous Constraints of Equivariant Graph Neural Networks for Physical Dynamics Learning
https://github.com/computationalprivacy/querycheetah
공식 코드QueryCheetah: Fast Automated Discovery of Attribute Inference Attacks Against Query-Based Systems
https://github.com/conditionwang/flnk
공식 코드Federated Learning with New Knowledge: Fundamentals, Advances, and Futures
https://github.com/cooperativenetworklearning/cooperative-network-learning
공식 코드Cooperative Network Learning for Large-Scale and Decentralized Graphs
https://github.com/corail-research/SeaPearl.jl
공식 코드Learning a Generic Value-Selection Heuristic Inside a Constraint Programming Solver
https://github.com/corail-research/learning-generic-csp
공식 코드Towards a Generic Representation of Combinatorial Problems for Learning-Based Approaches
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.