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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/lins-lab/collabedit

공식 코드

CollabEdit: Towards Non-destructive Collaborative Knowledge Editing

https://github.com/lipingyi/fedgh

공식 코드

FedGH: Heterogeneous Federated Learning with Generalized Global Header

https://github.com/lipingyi/fedssa

공식 코드

FedSSA: Semantic Similarity-based Aggregation for Efficient Model-Heterogeneous Personalized Federated Learning

https://github.com/lishenghui/ARFL

공식 코드

Auto-weighted Robust Federated Learning with Corrupted Data Sources

https://github.com/litong99/xpath

공식 코드

Towards Fine-Grained Explainability for Heterogeneous Graph Neural Network

https://github.com/liu-jc/mgnni

공식 코드

MGNNI: Multiscale Graph Neural Networks with Implicit Layers

https://github.com/liuchuang0059/gradformer

공식 코드

Gradformer: Graph Transformer with Exponential Decay

https://github.com/liuchuang0059/hi-gmae

공식 코드

Hi-GMAE: Hierarchical Graph Masked Autoencoders

https://github.com/liuhua811/lspia

공식 코드

LSPI: Heterogeneous Graph Neural Network Classification Aggregation Algorithm Based on Size Neighbor Path Identification

https://github.com/liujl11git/GNN-LP

공식 코드

On Representing Linear Programs by Graph Neural Networks

https://github.com/liun-online/HeCo

공식 코드

Self-supervised Heterogeneous Graph Neural Network with Co-contrastive Learning

https://github.com/liuxz1011/todynet

공식 코드

TodyNet: Temporal Dynamic Graph Neural Network for Multivariate Time Series Classification

https://github.com/liuzh-19/esg

공식 코드

Learning the Evolutionary and Multi-scale Graph Structure for Multivariate Time Series Forecasting

https://github.com/livable01/livable

공식 코드

LIVABLE: Exploring Long-Tailed Classification of Software Vulnerability Types

https://github.com/liwk16/StackVAE

공식 코드

StackVAE-G: An efficient and interpretable model for time series anomaly detection

https://github.com/liyifan-123/3dvlap

공식 코드

Weakly-Supervised 3D Scene Graph Generation via Visual-Linguistic Assisted Pseudo-labeling

https://github.com/liyuechao1998/scgsl

공식 코드

Graph Structure Learning for Tumor Microenvironment with Cell Type Annotation from non-spatial scRNA-seq data

https://github.com/llnl/graphite

공식 코드

Score dynamics: scaling molecular dynamics with picoseconds timestep via conditional diffusion model

https://github.com/locuslab/cfd-gcn

공식 코드

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

https://github.com/lok-18/ignet

공식 코드

Learning a Graph Neural Network with Cross Modality Interaction for Image Fusion

https://github.com/loserchen/wsdm_cup_rec_2022

공식 코드

A Practical Two-stage Ranking Framework for Cross-market Recommendation

https://github.com/louzounlab/pygon

공식 코드

Planted Dense Subgraphs in Dense Random Graphs Can Be Recovered using Graph-based Machine Learning

https://github.com/lrank/Robust_and_Privacy_preserving_Text_Representations

공식 코드

Towards Robust and Privacy-preserving Text Representations

https://github.com/lucaswithai/visgraph

공식 코드

End-to-end video instance segmentation via spatial-temporal graph neural networks

https://github.com/lucky9-cyou/S2-NeRF

공식 코드

$S^2$NeRF: Privacy-preserving Training Framework for NeRF

https://github.com/luckyos-code/dataset-analysis-ppml

공식 코드

Assessing the Impact of Image Dataset Features on Privacy-Preserving Machine Learning

https://github.com/luckyos-code/privacy-preserving-smartwatch-health-data-generation-using-dp-gans

공식 코드

Generating Synthetic Health Sensor Data for Privacy-Preserving Wearable Stress Detection

https://github.com/luis-mueller/wl-transformers

공식 코드

Aligning Transformers with Weisfeiler-Leman

https://github.com/lukasjf/contrastive-gnn-explanation

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Contrastive Graph Neural Network Explanation

https://github.com/lumia-group/subtree-attention

공식 코드

Tailoring Self-Attention for Graph via Rooted Subtrees

https://github.com/lunaproject22/aar

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Inferring Past Human Actions in Homes with Abductive Reasoning

https://github.com/lunarlab-gatech/morphsym-hgnn

공식 코드

Morphological-Symmetry-Equivariant Heterogeneous Graph Neural Network for Robotic Dynamics Learning

https://github.com/luo-junyu/gala

공식 코드

GALA: Graph Diffusion-based Alignment with Jigsaw for Source-free Domain Adaptation

https://github.com/luo-lorry/conformal-load-forecasting

공식 코드

Conformal Load Prediction with Transductive Graph Autoencoders

https://github.com/luost26/equivariant-orientedmp

공식 코드

Equivariant Point Cloud Analysis via Learning Orientations for Message Passing

https://github.com/luxtu/OCTA-graph

공식 코드

Interpretable Retinal Disease Prediction Using Biology-Informed Heterogeneous Graph Representations

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