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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/russellizadi/ssp

공식 코드

Optimization of Graph Neural Networks with Natural Gradient Descent

https://github.com/rusty1s/deep-graph-matching-consensus

공식 코드

Deep Graph Matching Consensus

https://github.com/rusty1s/pytorch_geometric

공식 코드

Hypergraph Convolution and Hypergraph Attention

https://github.com/ruxueshi/Awesome-Comprehensive-Survey-of-Synthetic-Tabular-Data-Generation

공식 코드

A Comprehensive Survey of Synthetic Tabular Data Generation

https://github.com/ruyilian/checkerpose

공식 코드

CheckerPose: Progressive Dense Keypoint Localization for Object Pose Estimation with Graph Neural Network

https://github.com/ryanliu30/hierarchicalgnn

공식 코드

Hierarchical Graph Neural Networks for Particle Track Reconstruction

https://github.com/ryanwangzf/promptehr

공식 코드

PromptEHR: Conditional Electronic Healthcare Records Generation with Prompt Learning

https://github.com/sYeaLumin/SketchGNN

공식 코드

SketchGNN: Semantic Sketch Segmentation with Graph Neural Networks

https://github.com/sacs-epfl/shatter

공식 코드

Noiseless Privacy-Preserving Decentralized Learning

https://github.com/safe-graph/DGFraud

공식 코드

Alleviating the Inconsistency Problem of Applying Graph Neural Network to Fraud Detection

https://github.com/safe-graph/RioGNN

공식 코드

Reinforced Neighborhood Selection Guided Multi-Relational Graph Neural Networks

https://github.com/safreita1/malnet-graph

공식 코드

A Large-Scale Database for Graph Representation Learning

https://github.com/samadeusfp/sacofa

공식 코드

SaCoFa: Semantics-aware Control-flow Anonymization for Process Mining

https://github.com/samuel-maddock/federated-boosted-dp-trees

공식 코드

Federated Boosted Decision Trees with Differential Privacy

https://github.com/sanxing-chen/linking-tale

공식 코드

A Tale of Two Linkings: Dynamically Gating between Schema Linking and Structural Linking for Text-to-SQL Parsing

https://github.com/sapphire-star/ffmsr

공식 코드

Federated Semantic Learning for Privacy-preserving Cross-domain Recommendation

https://github.com/sara-silvaad/connectome_gcl

공식 코드

Graph Contrastive Learning for Connectome Classification

https://github.com/saranggalada/prism

공식 코드

PRISM: Privacy-preserving Inter-Site MRI Harmonization via Disentangled Representation Learning

https://github.com/saswatd27/icml_linearization_disparate_impact

공식 코드

Disparate Impact on Group Accuracy of Linearization for Private Inference

https://github.com/sathwikchadaga/failure-cascade

공식 코드

Power Failure Cascade Prediction using Graph Neural Networks

https://github.com/saurabac/Wasserstein-Data-Markets

공식 코드

Wasserstein Markets for Differentially-Private Data

https://github.com/sauravpr/hyperbolic_federated_classification

공식 코드

Federated Classification in Hyperbolic Spaces via Secure Aggregation of Convex Hulls

https://github.com/sayakmukherjee/mapl

공식 코드

MAPL: Model Agnostic Peer-to-peer Learning

https://github.com/scfischer/dirk-et-al-2022

공식 코드

Recognition and reconstruction of cell differentiation patterns with deep learning

https://github.com/schalkdaniel/dscwb

공식 코드

Privacy-Preserving and Lossless Distributed Estimation of High-Dimensional Generalized Additive Mixed Models

https://github.com/schmidt-ju/scene

공식 코드

SCENE: Reasoning about Traffic Scenes using Heterogeneous Graph Neural Networks

https://github.com/schneiderkamplab/syntheval

공식 코드

SynthEval: A Framework for Detailed Utility and Privacy Evaluation of Tabular Synthetic Data

https://github.com/scofield666/mbssl

공식 코드

Multi-behavior Self-supervised Learning for Recommendation

https://github.com/scottjiao/slotgat_icml23

공식 코드

SlotGAT: Slot-based Message Passing for Heterogeneous Graph Neural Network

https://github.com/sczhou/IGNN

공식 코드

Cross-Scale Internal Graph Neural Network for Image Super-Resolution

https://github.com/seaocn/motif-backdoor

공식 코드

Motif-Backdoor: Rethinking the Backdoor Attack on Graph Neural Networks via Motifs

https://github.com/seconnds/seconnds_1_25

공식 코드

SecONNds: Secure Outsourced Neural Network Inference on ImageNet

https://github.com/securefederatedai/openfl

공식 코드

Federated Discrete Denoising Diffusion Model for Molecular Generation with OpenFL

https://github.com/seed-vt/tracefl

공식 코드

TraceFL: Interpretability-Driven Debugging in Federated Learning via Neuron Provenance

https://github.com/seijimaekawa/a2dug

공식 코드

A Simple and Scalable Graph Neural Network for Large Directed Graphs

https://github.com/selgroup/hisevent

공식 코드

Hierarchical and Incremental Structural Entropy Minimization for Unsupervised Social Event Detection

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