Papers with Code 아카이브

Privacy Preserving

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법전체 데이터셋

하위 태스크

1

평가표

0

보존된 평가 기록

0

코드

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

https://github.com/jokofa/neurols

공식 코드

Learning to Control Local Search for Combinatorial Optimization

https://github.com/jonasberghansen/power-flow-balancing-with-decentralized-graph-neural-networks

공식 코드

Power Flow Balancing with Decentralized Graph Neural Networks

https://github.com/josephdrahos/sreds

공식 코드

Generalizability and Application of the Skin Reflectance Estimate Based on Dichromatic Separation (SREDS)

https://github.com/joshuachou2018/afs

공식 코드

Audit to Forget: A Unified Method to Revoke Patients' Private Data in Intelligent Healthcare

https://github.com/joshuachou2018/pppml-hmi

공식 코드

Personalized and privacy-preserving federated heterogeneous medical image analysis with PPPML-HMI

https://github.com/jovitalukasik/SVGe

공식 코드

Smooth Variational Graph Embeddings for Efficient Neural Architecture Search

https://github.com/juhyeonkim95/TaxiSimulatorOnGraph

공식 코드

Optimizing Large-Scale Fleet Management on a Road Network using Multi-Agent Deep Reinforcement Learning with Graph Neural Network

https://github.com/jumbo110/eurekha

공식 코드

EUREKHA: Enhancing User Representation for Key Hackers Identification in Underground Forums

https://github.com/jumxglhf/akgnn

공식 코드

Adaptive Kernel Graph Neural Network

https://github.com/jumxglhf/grape

공식 코드

Grape: Knowledge Graph Enhanced Passage Reader for Open-domain Question Answering

https://github.com/justarter/gmocat

공식 코드

GMOCAT: A Graph-Enhanced Multi-Objective Method for Computerized Adaptive Testing

https://github.com/juyebshin/instagram

공식 코드

InstaGraM: Instance-level Graph Modeling for Vectorized HD Map Learning

https://github.com/juzehao/befl

공식 코드

BEFL: Balancing Energy Consumption in Federated Learning for Mobile Edge IoT

https://github.com/jvasso/graph-rl4adaptive-learning

공식 코드

Towards Scalable Adaptive Learning with Graph Neural Networks and Reinforcement Learning

https://github.com/jwu4sml/DEMO-Net

공식 코드

DEMO-Net: Degree-specific Graph Neural Networks for Node and Graph Classification

https://github.com/jwwthu/GNN4Traffic

공식 코드

Graph Neural Network for Traffic Forecasting: A Survey

https://github.com/jwzhanggy/DifNet

공식 코드

Get Rid of Suspended Animation Problem: Deep Diffusive Neural Network on Graph Semi-Supervised Classification

https://github.com/jwzhanggy/Graph-Bert

공식 코드

Graph-Bert: Only Attention is Needed for Learning Graph Representations

https://github.com/jz48/regexplainer

공식 코드

RegExplainer: Generating Explanations for Graph Neural Networks in Regression Tasks

https://github.com/k-s-b/gnn_wco

공식 코드

GraphFC: Customs Fraud Detection with Label Scarcity

https://github.com/kaiwenw/DPDDT

공식 코드

Scalable and Provably Accurate Algorithms for Differentially Private Distributed Decision Tree Learning

https://github.com/kaize0409/s-3-cl

공식 코드

Eliciting Structural and Semantic Global Knowledge in Unsupervised Graph Contrastive Learning

https://github.com/kargaranamir/Wide-AdGraph

공식 코드

Wide-AdGraph: Detecting Ad Trackers with a Wide Dependency Chain Graph

https://github.com/karimamakhlouf/impact_of_ldp_on_fairness

공식 코드

On the Impact of Multi-dimensional Local Differential Privacy on Fairness

https://github.com/karolismart/agentnet

공식 코드

Agent-based Graph Neural Networks

https://github.com/katiebuc/gnnad

공식 코드

Graph Neural Network-Based Anomaly Detection for River Network Systems

https://github.com/kdmsit/TGDMat

공식 코드

Periodic Materials Generation using Text-Guided Joint Diffusion Model

https://github.com/kdmsit/crysgnn

공식 코드

CrysGNN : Distilling pre-trained knowledge to enhance property prediction for crystalline materials

https://github.com/kennynh/mhetgl

공식 코드

Unsupervised Graph Anomaly Detection via Multi-Hypersphere Heterophilic Graph Learning

https://github.com/ketrint/em_showers_segmentation

공식 코드

Segmentation of EM showers for neutrino experiments with deep graph neural networks

https://github.com/kexinhuang12345/SkipGNN

공식 코드

SkipGNN: Predicting Molecular Interactions with Skip-Graph Networks

https://github.com/kg-cc/noisehgnn

공식 코드

NoiseHGNN: Synthesized Similarity Graph-Based Neural Network For Noised Heterogeneous Graph Representation Learning

https://github.com/khachoang1412/SecAgg_not_private

공식 코드

Secure Aggregation is Not Private Against Membership Inference Attacks

https://github.com/khaclinh/fisheyepp4av

공식 코드

FisheyePP4AV: A privacy-preserving method for autonomous vehicles on fisheye camera images

https://github.com/khoaguin/hesplitnet

공식 코드

Split Without a Leak: Reducing Privacy Leakage in Split Learning

https://github.com/khoaguin/pockethhe

공식 코드

GuardML: Efficient Privacy-Preserving Machine Learning Services Through Hybrid Homomorphic Encryption

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