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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/MindSpore-scientific-2/code-10/tree/main/RGNN

Knowledge-aware Graph Neural Networks with Label Smoothness Regularization for Recommender Systems

https://github.com/MindSpore-scientific-2/code-11/tree/main/RGNN

GNN4EEG: A Benchmark and Toolkit for Electroencephalography Classification with Graph Neural Network

https://github.com/MindSpore-scientific-2/code-8/tree/main/RGNN

LGGNet: Learning from Local-Global-Graph Representations for Brain-Computer Interface

https://github.com/MindSpore-scientific-2/code-8/tree/main/SMOTE

SMOTExT: SMOTE meets Large Language Models

https://github.com/MindSpore-scientific/code-1/tree/main/RGNN

GNN4EEG: A Benchmark and Toolkit for Electroencephalography Classification with Graph Neural Network

https://github.com/MindSpore-scientific/code-3/tree/main/Analysis-of-Black-Hole-Solutions-in-Parabolic-Class-Using-Neural-Networks

Graph Neural Networks for Social Recommendation

https://github.com/MindSpore-scientific/code-8/tree/main/RGNN

EEG-Based Emotion Recognition Using Regularized Graph Neural Networks

https://github.com/MohamedRamzy1/Scan2Mesh

Scan2Mesh: From Unstructured Range Scans to 3D Meshes

https://github.com/NAVER-INTEL-Co-Lab/gaudi-byzantine

Generalized Federated Learning via Sharpness Aware Minimization

https://github.com/Namkyeong/BGRL_Pytorch

Self-supervised Graph Neural Networks without explicit negative sampling

https://github.com/NapierNLP/CAPE

CAPE: Context-Aware Private Embeddings for Private Language Learning

https://github.com/NorthPolesky/GNNpaper

Graph Neural Networks: A Review of Methods and Applications

https://github.com/Onedean/EAC

Expand and Compress: Exploring Tuning Principles for Continual Spatio-Temporal Graph Forecasting

https://github.com/OpenMined/PyDentity

A Distributed Trust Framework for Privacy-Preserving Machine Learning

https://github.com/OpenXAIProject/GNNExplainer-Tutorial

GNNExplainer: Generating Explanations for Graph Neural Networks

https://github.com/PaddlePaddle/PaddleRec/tree/master/models/rank/sign

Detecting Beneficial Feature Interactions for Recommender Systems

https://github.com/PaddlePaddle/PaddleScience/tree/develop/jointContribution/CFDGCN

Combining Differentiable PDE Solvers and Graph Neural Networks for Fluid Flow Prediction

https://github.com/Panichito/GNN-Decision-TSP

Learning to Solve NP-Complete Problems - A Graph Neural Network for Decision TSP

https://github.com/PasqualeAuriemma/GCN-DIFFPOOL

Hierarchical Graph Representation Learning with Differentiable Pooling

https://github.com/Pushkar-v/Generating-Synthetic-Data-using-GANs

G-PATE: Scalable Differentially Private Data Generator via Private Aggregation of Teacher Discriminators

https://github.com/RUCAIBox/RecBole

RecBole 2.0: Towards a More Up-to-Date Recommendation Library

https://github.com/Recognito-Vision/Face-SDK-Linux-Demos

Deep Variational Privacy Funnel: General Modeling with Applications in Face Recognition

https://github.com/Recognito-Vision/Linux-FaceRecognition-FaceLivenessDetection

FedSIS: Federated Split Learning with Intermediate Representation Sampling for Privacy-preserving Generalized Face Presentation Attack Detection

https://github.com/RexYing/diffpool

Hierarchical Graph Representation Learning with Differentiable Pooling

https://github.com/RexYing/graph-pooling

Hierarchical Graph Representation Learning with Differentiable Pooling

https://github.com/RingBDStack/DP-CroSUE/tree/main/DP-CroSUE-mindspore/

Cross-Network Social User Embedding with Hybrid Differential Privacy Guarantees

https://github.com/RyanGoslingsBugle/priv-text

CAPE: Context-Aware Private Embeddings for Private Language Learning

https://github.com/SAP-samples/machine-learning-diff-private-federated-learning

Differentially Private Federated Learning: A Client Level Perspective

https://github.com/SAP/machine-learning-diff-private-federated-learning

Differentially Private Federated Learning: A Client Level Perspective

https://github.com/SamuelGong/FLASHE

FLASHE: Additively Symmetric Homomorphic Encryption for Cross-Silo Federated Learning

https://github.com/SamuelGong/federated-learning-system-literature

Towards Efficient Synchronous Federated Training: A Survey on System Optimization Strategies

https://github.com/SanaAwan5/transfer_learning_on_mnist_spdz

A generic framework for privacy preserving deep learning

https://github.com/Sangs3112/SimGNN

SimGNN: A Neural Network Approach to Fast Graph Similarity Computation

https://github.com/ShaharGottlieb/GNNExplainer_DGL

GNNExplainer: Generating Explanations for Graph Neural Networks

https://github.com/ShaoaAllen/CogQA

Cognitive Graph for Multi-Hop Reading Comprehension at Scale

https://github.com/Sherrylife/Local-Differential-Privacy-for-Deep-Learning

Local Differential Privacy for Deep Learning

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