Privacy Preserving
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/software-engineering-amsterdam/MLitB
공식 코드https://github.com/songwenas12/csp-drl
공식 코드Learning Variable Ordering Heuristics for Solving Constraint Satisfaction Problems
https://github.com/songxf1024/gims
공식 코드GIMS: Image Matching System Based on Adaptive Graph Construction and Graph Neural Network
https://github.com/sorarain/routeplacer
공식 코드RoutePlacer: An End-to-End Routability-Aware Placer with Graph Neural Network
https://github.com/sorarain/transplace
공식 코드TransPlace: Transferable Circuit Global Placement via Graph Neural Network
https://github.com/soumyaxyz/Privacy-Preserving-Federated-Learning
공식 코드https://github.com/sourajitdas/state-augmeted-information-routing
공식 코드Learning State-Augmented Policies for Information Routing in Communication Networks
https://github.com/sourasb05/Federated-Frank-Wolfe
공식 코드https://github.com/spatank/curiosity-graphs
공식 코드Intrinsically motivated graph exploration using network theories of human curiosity
https://github.com/spring-epfl/dp_multiplicity
공식 코드Arbitrary Decisions are a Hidden Cost of Differentially Private Training
https://github.com/spring-epfl/privacydecentralizedlearning
공식 코드On the (In)security of Peer-to-Peer Decentralized Machine Learning
https://github.com/spring-epfl/synthetic_data_release
공식 코드https://github.com/springwiz11/fed-gnodeformer
공식 코드Federated Spectral Graph Transformers Meet Neural Ordinary Differential Equations for Non-IID Graphs
https://github.com/sprinter1999/fedlsr
공식 코드Towards Federated Learning against Noisy Labels via Local Self-Regularization
https://github.com/squareroot3/rethinking-anomaly-detection
공식 코드https://github.com/srastegari/mbgnn
공식 코드Co-evolution-based Metal-binding Residue Prediction with Graph Neural Networks
https://github.com/sserranw/abg-nas
공식 코드ABG-NAS: Adaptive Bayesian Genetic Neural Architecture Search for Graph Representation Learning
https://github.com/sshleifer/Graph-WaveNet
공식 코드Incrementally Improving Graph WaveNet Performance on Traffic Prediction
https://github.com/sssohrab/sparsifying_groups_imAmbiguation
공식 코드Privacy-Preserving Image Sharing via Sparsifying Layers on Convolutional Groups
https://github.com/stanford-crfm/biomedlm
공식 코드BioMedLM: A 2.7B Parameter Language Model Trained On Biomedical Text
https://github.com/stanfordcbcl/grom
공식 코드Learning Reduced-Order Models for Cardiovascular Simulations with Graph Neural Networks
https://github.com/stars-cs/black_box_traffic
공식 코드Black-box Adversarial Attacks on Network-wide Multi-step Traffic State Prediction Models
https://github.com/starxingwang/ahstgnn
공식 코드Adaptive Hybrid Spatial-Temporal Graph Neural Network for Cellular Traffic Prediction
https://github.com/stathismegas/celcomen
공식 코드Celcomen: spatial causal disentanglement for single-cell and tissue perturbation modeling
https://github.com/stavrosorf/dt4evs
공식 코드https://github.com/stefanbloemheuvel/gcntimeseriesregression
공식 코드Graph Neural Networks for Multivariate Time Series Regression with Application to Seismic Data
https://github.com/stefanosantaris/vstreamdrls
공식 코드https://github.com/stellargraph/FisherGCN
공식 코드https://github.com/steve30572/dpao
공식 코드Dual Policy Learning for Aggregation Optimization in Graph Neural Network-based Recommender Systems
https://github.com/stevezhuo/fis-one
공식 코드FIS-ONE: Floor Identification System with One Label for Crowdsourced RF Signals
https://github.com/strawhatboy/iagnn
공식 코드Intention Adaptive Graph Neural Network for Category-aware Session-based Recommendation
https://github.com/striderdu/spa
공식 코드Search to Pass Messages for Temporal Knowledge Graph Completion
https://github.com/sttdanonymous/sttd
공식 코드https://github.com/stxyfu/stress_gnn
공식 코드An FEA surrogate model with Boundary Oriented Graph Embedding approach
https://github.com/su-cisec/mlwithhe
공식 코드ML with HE: Privacy Preserving Machine Learning Inferences for Genome Studies
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.