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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/siddhant-doshi/Dr-COVID

공식 코드

Dr-COVID: Graph Neural Networks for SARS-CoV-2 Drug Repurposing

https://github.com/siddhant-doshi/spin

공식 코드

Graph Neural Networks with Parallel Neighborhood Aggregations for Graph Classification

https://github.com/sifattaj/amalgam

공식 코드

Amalgam: A Framework for Obfuscated Neural Network Training on the Cloud

https://github.com/sigeisler/s2gnn

공식 코드

Spatio-Spectral Graph Neural Networks

https://github.com/silkekaiser/GNNUI

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Spatio-Temporal Graph Neural Network for Urban Spaces: Interpolating Citywide Traffic Volume

https://github.com/siml3/ru-net

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RU-Net: Regularized Unrolling Network for Scene Graph Generation

https://github.com/simonakocour/anything_left

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Is there anything left? Measuring semantic residuals of objects removed from 3D Gaussian Splatting

https://github.com/simonvino/DCRNN_brain_connectivity

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A Graph Neural Network Framework for Causal Inference in Brain Networks

https://github.com/simran-arora/focus

공식 코드

Can Foundation Models Help Us Achieve Perfect Secrecy?

https://github.com/sinanyildirim/bayesian_dp_dist_lr

공식 코드

Differentially Private Distributed Bayesian Linear Regression with MCMC

https://github.com/sinanyildirim/smc_dp_adatr

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Differentially Private Online Bayesian Estimation With Adaptive Truncation

https://github.com/siomvas/ARIA

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ARIA: On the Interaction Between Architectures, Initialization and Aggregation Methods for Federated Visual Classification

https://github.com/siquanhuang/Multi-metrics_against_backdoors_in_FL

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Multi-metrics adaptively identifies backdoors in Federated learning

https://github.com/sisaman/lpgnn

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Locally Private Graph Neural Networks

https://github.com/sisinflab/graph-rss-reproducibility

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Challenging the Myth of Graph Collaborative Filtering: a Reasoned and Reproducibility-driven Analysis

https://github.com/sisl/DICG

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Deep Implicit Coordination Graphs for Multi-agent Reinforcement Learning

https://github.com/sisyphus-project/sisyphus-ppml

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Sisyphus: A Cautionary Tale of Using Low-Degree Polynomial Activations in Privacy-Preserving Deep Learning

https://github.com/sjmeis/DPNONDP

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Thinking Outside of the Differential Privacy Box: A Case Study in Text Privatization with Language Model Prompting

https://github.com/sjmeis/diffractor

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1-Diffractor: Efficient and Utility-Preserving Text Obfuscation Leveraging Word-Level Metric Differential Privacy

https://github.com/sjyucnel/brain-and-information-bottleneck

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BrainIB: Interpretable Brain Network-based Psychiatric Diagnosis with Graph Information Bottleneck

https://github.com/sklin93/rebraid

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Deep Representations for Time-varying Brain Datasets

https://github.com/sldgroup/mobilevigv2

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Scaling Graph Convolutions for Mobile Vision

https://github.com/sleepymalc/Travel-the-Same-Path

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Travel the Same Path: A Novel TSP Solving Strategy

https://github.com/slslslrhfem/song_structure_graph

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Combinatorial music generation model with song structure graph analysis

https://github.com/slz-ai/hfedms

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HFedMS: Heterogeneous Federated Learning with Memorable Data Semantics in Industrial Metaverse

https://github.com/smahdavi4/clrs

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Towards Better Out-of-Distribution Generalization of Neural Algorithmic Reasoning Tasks

https://github.com/smartcameras/PrivEdge

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PrivEdge: From Local to Distributed Private Training and Prediction

https://github.com/smartcameras/privatenet

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Training privacy-preserving video analytics pipelines by suppressing features that reveal information about private attributes

https://github.com/smile-ffg/he-man-concrete

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HE-MAN -- Homomorphically Encrypted MAchine learning with oNnx models

https://github.com/smrhadou/fed-surf

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Stochastic Unrolled Federated Learning

https://github.com/snap-stanford/lamp

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Learning Controllable Adaptive Simulation for Multi-resolution Physics

https://github.com/snap-stanford/relbench

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RelBench: A Benchmark for Deep Learning on Relational Databases

https://github.com/snap-stanford/relbench-user-study

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RelBench: A Benchmark for Deep Learning on Relational Databases

https://github.com/snap-stanford/relgt

공식 코드

Relational Graph Transformer

https://github.com/snu-arc/ginex

공식 코드

Ginex: SSD-enabled Billion-scale Graph Neural Network Training on a Single Machine via Provably Optimal In-memory Caching

https://github.com/socialrecsys/smin

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Social Recommendation with Self-Supervised Metagraph Informax Network

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