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/Junyoungpark/CGS

공식 코드

Convergent Graph Solvers

https://github.com/K-Wu/pytorch-direct_dgl

공식 코드

PyTorch-Direct: Enabling GPU Centric Data Access for Very Large Graph Neural Network Training with Irregular Accesses

https://github.com/Kaimaoge/IGNNK

공식 코드

Inductive Graph Neural Networks for Spatiotemporal Kriging

https://github.com/KasraAhmadi/FL-Privacy-LLM

공식 코드

An Interactive Framework for Implementing Privacy-Preserving Federated Learning: Experiments on Large Language Models

https://github.com/KnowledgeDiscovery/StrGNN

공식 코드

Structural Temporal Graph Neural Networks for Anomaly Detection in Dynamic Graphs

https://github.com/Koukyosyumei/AIJack

공식 코드

FEDLAD: Federated Evaluation of Deep Leakage Attacks and Defenses

https://github.com/LARS-research/HL-GNN

공식 코드

Heuristic Learning with Graph Neural Networks: A Unified Framework for Link Prediction

https://github.com/LIONS-EPFL/dynamic-MIS

공식 코드

Maximum Independent Set: Self-Training through Dynamic Programming

https://github.com/LLNL/FAST

공식 코드

Improved Protein-ligand Binding Affinity Prediction with Structure-Based Deep Fusion Inference

https://github.com/LTS4/gnn_statistics

공식 코드

On the choice of graph neural network architectures

https://github.com/LUMII-Syslab/Matrix-SE

공식 코드

Matrix Shuffle-Exchange Networks for Hard 2D Tasks

https://github.com/LUMII-Syslab/Switchblade

공식 코드

Matrix Shuffle-Exchange Networks for Hard 2D Tasks

https://github.com/LUOyk1999/NodeID

공식 코드

Node Identifiers: Compact, Discrete Representations for Efficient Graph Learning

https://github.com/LaRiffle/AriaNN

공식 코드

ARIANN: Low-Interaction Privacy-Preserving Deep Learning via Function Secret Sharing

https://github.com/LaRiffle/collateral-learning

공식 코드

Partially Encrypted Machine Learning using Functional Encryption

https://github.com/LeiBAI/AGCRN

공식 코드

Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting

https://github.com/LetterLiGo/SafeEar

공식 코드

SafeEar: Content Privacy-Preserving Audio Deepfake Detection

https://github.com/Lhongpei/ipps-drl

공식 코드

Solving Integrated Process Planning and Scheduling Problem via Graph Neural Network Based Deep Reinforcement Learning

https://github.com/LiXinran6/LSDGNN_ICL

공식 코드

Long-Short Distance Graph Neural Networks and Improved Curriculum Learning for Emotion Recognition in Conversation

https://github.com/LifengFan/Human-Gaze-Communication

공식 코드

Understanding Human Gaze Communication by Spatio-Temporal Graph Reasoning

https://github.com/Lihaogx/DNDN

공식 코드

Dynamic Neural Dowker Network: Approximating Persistent Homology in Dynamic Directed Graphs

https://github.com/Lili1013/FedPCL_CDR

공식 코드

FedPCL-CDR: A Federated Prototype-based Contrastive Learning Framework for Privacy-Preserving Cross-domain Recommendation

https://github.com/LinXueyuanStdio/EchoEA

공식 코드

EchoEA: Echo Information between Entities and Relations for Entity Alignment

https://github.com/LinguisticAnomalies/small-lm-sliding-windows

공식 코드

Bigger But Not Better: Small Neural Language Models Outperform Large Language Models in Detection of Thought Disorder

https://github.com/LiuHeBJTU/GLAM

공식 코드

Joint Graph Learning and Matching for Semantic Feature Correspondence

https://github.com/Lixy1997/Group-WSSS

공식 코드

Group-Wise Semantic Mining for Weakly Supervised Semantic Segmentation

https://github.com/Lollo0900/Plumbed_3-Manifolds

공식 코드

Detecting Homeomorphic 3-manifolds via Graph Neural Networks

https://github.com/LuChang-CS/sherbet

공식 코드

Self-Supervised Graph Learning with Hyperbolic Embedding for Temporal Health Event Prediction

https://github.com/MIT-SPARK/PD-MeshNet

공식 코드

Primal-Dual Mesh Convolutional Neural Networks

https://github.com/MaKaili/Contrast-Reg

공식 코드

Calibrating and Improving Graph Contrastive Learning

https://github.com/MaiqiVerse/AutoGNR

공식 코드

Automated Heterogeneous Network learning with Non-Recursive Message Passing

https://github.com/ManlioWu/ESTAG

공식 코드

Equivariant Spatio-Temporal Attentive Graph Networks to Simulate Physical Dynamics

https://github.com/Maryeon/InvMM

공식 코드

An Inversion-based Measure of Memorization for Diffusion Models

https://github.com/Masuyama-lab/FCAC

공식 코드

Privacy-preserving Continual Federated Clustering via Adaptive Resonance Theory

https://github.com/McGill-DMaS/Authorship-Anonymization

공식 코드

ER-AE: Differentially Private Text Generation for Authorship Anonymization

https://github.com/McGill-DMaS/AuthorshipAnonymization

공식 코드

ER-AE: Differentially Private Text Generation for Authorship Anonymization

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