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Privacy Preserving

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

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코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/xjtuwxliang/R-GSN/tree/main

R-GSN: The Relation-based Graph Similar Network for Heterogeneous Graph

https://github.com/xk-huang/yet-another-vectornet

VectorNet: Encoding HD Maps and Agent Dynamics from Vectorized Representation

https://github.com/xlearning-scu/2023-iccv-common

SuperGlue: Learning Feature Matching with Graph Neural Networks

https://github.com/xqx12/daily-info

TenSEAL: A Library for Encrypted Tensor Operations Using Homomorphic Encryption

https://github.com/xuzijian629/combopt-zero

Solving NP-Hard Problems on Graphs with Extended AlphaGo Zero

https://github.com/xxlya/Fed_ABIDE

Multi-site fMRI Analysis Using Privacy-preserving Federated Learning and Domain Adaptation: ABIDE Results

https://github.com/xxxnhb/few-shot-gnn

Few-Shot Learning with Graph Neural Networks

https://github.com/xxxnhb/fewshot-egnn

Edge-labeling Graph Neural Network for Few-shot Learning

https://github.com/yangfeifei02/HOSP

High-order structure preserving graph neural network for few-shot learning

https://github.com/yangyucheng000/Paper-3/tree/main/DPSUR-Implemented-by-Mindspore

DPSUR: Accelerating Differentially Private Stochastic Gradient Descent Using Selective Update and Release

https://github.com/yanliang3612/nucleusdiff

Pocket2Mol: Efficient Molecular Sampling Based on 3D Protein Pockets

https://github.com/yassersouri/task-driven-object-detection

What Object Should I Use? - Task Driven Object Detection

https://github.com/yaya-yns/tart

Graph HyperNetworks for Neural Architecture Search

https://github.com/ydzhang-stormstout/HAT

Hyperbolic Graph Attention Network

https://github.com/yeounoh/lc_nl2sql

CHESS: Contextual Harnessing for Efficient SQL Synthesis

https://github.com/yjt2018/fewshot-egnn

Edge-labeling Graph Neural Network for Few-shot Learning

https://github.com/yuhangchen0/fedheal

Fair Federated Learning under Domain Skew with Local Consistency and Domain Diversity

https://github.com/yule-buaa/dyglib

TCL: Transformer-based Dynamic Graph Modelling via Contrastive Learning

https://github.com/yzhangcs/parser

Strongly Incremental Constituency Parsing with Graph Neural Networks

https://github.com/z-qiyu/KGNN-LS

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

https://github.com/zachysun/taxi_traffic_benchmark

Graph WaveNet for Deep Spatial-Temporal Graph Modeling

https://github.com/zaixizhang/protgnn

ProtGNN: Towards Self-Explaining Graph Neural Networks

https://github.com/zhoujf620/Motif-based-inductive-GNN-training

Inductive Matrix Completion Based on Graph Neural Networks

https://github.com/zhuwenxing/Deep_Learning_Based_Power_Control

A Graph Neural Network Approach for Scalable Wireless Power Control

https://github.com/zju-dbl/metakg

KGAT: Knowledge Graph Attention Network for Recommendation

https://github.com/zju3dv/LoFTR

SuperGlue: Learning Feature Matching with Graph Neural Networks

https://github.com/zjukg/morse

Inductive Relation Prediction by Subgraph Reasoning

https://github.com/zjunet/amnet

Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged Fraudsters

https://gitlab.com/cedric_sanders/masterarbeit

Hierarchical Graph Representation Learning with Differentiable Pooling

https://gitlab.com/korovin/iprover

Machine Learning Meets The Herbrand Universe

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