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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/hi-weiyuan/ptf-fedrec

공식 코드

Hide Your Model: A Parameter Transmission-free Federated Recommender System

https://github.com/hifipsysta/wdp

공식 코드

Wasserstein Differential Privacy

https://github.com/hikopensource/davar-lab-ocr

공식 코드

VSR: A Unified Framework for Document Layout Analysis combining Vision, Semantics and Relations

https://github.com/hilbert9221/INFUNE

공식 코드

A Novel Framework with Information Fusion and Neighborhood Enhancement for User Identity Linkage

https://github.com/hipgraph/isplib

공식 코드

iSpLib: A Library for Accelerating Graph Neural Networks using Auto-tuned Sparse Operations

https://github.com/histocartography/hact-net

공식 코드

Hierarchical Graph Representations in Digital Pathology

https://github.com/hit-ices/tnnls-mtgn

공식 코드

Who Should I Engage with At What Time? A Missing Event Aware Temporal Graph Neural Network

https://github.com/hiyuchang/fed_sel_tune

공식 코드

Exploring Selective Layer Fine-Tuning in Federated Learning

https://github.com/hiyuchang/mimic_codes

공식 코드

MimiC: Combating Client Dropouts in Federated Learning by Mimicking Central Updates

https://github.com/hku-medai/higt

공식 코드

HIGT: Hierarchical Interaction Graph-Transformer for Whole Slide Image Analysis

https://github.com/hku-medai/mult-ehr

공식 코드

Multi-task Heterogeneous Graph Learning on Electronic Health Records

https://github.com/hkuds/dgnn

공식 코드

Disentangled Graph Social Recommendation

https://github.com/hkuds/graphaug

공식 코드

Graph Augmentation for Recommendation

https://github.com/hkuds/lightgcl

공식 코드

LightGCL: Simple Yet Effective Graph Contrastive Learning for Recommendation

https://github.com/hkuds/lightgnn

공식 코드

LightGNN: Simple Graph Neural Network for Recommendation

https://github.com/hkuds/selfgnn

공식 코드

SelfGNN: Self-Supervised Graph Neural Networks for Sequential Recommendation

https://github.com/hkust-knowcomp/eventground

공식 코드

EventGround: Narrative Reasoning by Grounding to Eventuality-centric Knowledge Graphs

https://github.com/hkust-knowcomp/privategraphencoder

공식 코드

Independent Distribution Regularization for Private Graph Embedding

https://github.com/hkust-knowcomp/privatengdb

공식 코드

Privacy-Preserved Neural Graph Databases

https://github.com/hkust-knowcomp/privlm-bench

공식 코드

PrivLM-Bench: A Multi-level Privacy Evaluation Benchmark for Language Models

https://github.com/hlr/drgn

공식 코드

Dynamic Relevance Graph Network for Knowledge-Aware Question Answering

https://github.com/hm-choi/blind-match

공식 코드

Blind-Match: Efficient Homomorphic Encryption-Based 1:N Matching for Privacy-Preserving Biometric Identification

https://github.com/hmthanh/GCAT

공식 코드

Graph Collaborative Attention Network for Link Prediction in Knowledge Graphs

https://github.com/hoenchioma/depaint

공식 코드

DePAint: A Decentralized Safe Multi-Agent Reinforcement Learning Algorithm considering Peak and Average Constraints

https://github.com/hope-rita/thlm

공식 코드

Pretraining Language Models with Text-Attributed Heterogeneous Graphs

https://github.com/horanshi/ldc-mia

공식 코드

Learning-Based Difficulty Calibration for Enhanced Membership Inference Attacks

https://github.com/hoseinzadeehsan/gait

공식 코드

Graph Neural Network Approach to Semantic Type Detection in Tables

https://github.com/howardzh01/ppma

공식 코드

Learning Human Action Recognition Representations Without Real Humans

https://github.com/hoyunchoi/GNRK

공식 코드

GNRK: Graph Neural Runge-Kutta method for solving partial differential equations

https://github.com/hpcclab/SAED-Security-At-Edge

공식 코드

SAED: Edge-Based Intelligence for Privacy-Preserving Enterprise Search on the Cloud

https://github.com/hreynaud/echonet-synthetic

공식 코드

EchoNet-Synthetic: Privacy-preserving Video Generation for Safe Medical Data Sharing

https://github.com/hsipingli/chigad

공식 코드

Chi-Square Wavelet Graph Neural Networks for Heterogeneous Graph Anomaly Detection

https://github.com/htcr/sam_road

공식 코드

Segment Anything Model for Road Network Graph Extraction

https://github.com/htwang14/PA-HMDB51

공식 코드

Privacy-Preserving Deep Action Recognition: An Adversarial Learning Framework and A New Dataset

https://github.com/huaizepeng2020/m2gnn_git

공식 코드

M2GNN: Metapath and Multi-interest Aggregated Graph Neural Network for Tag-based Cross-domain Recommendation

https://github.com/huangjunjie95/SiGAT

공식 코드

Signed Graph Attention Networks

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