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Privacy Preserving

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

https://gitlab.com/fdlm/olga

공식 코드

Artist Similarity with Graph Neural Networks

https://gitlab.com/hanfengzhai2/gnn-fem-polyplas

공식 코드

Stress Predictions in Polycrystal Plasticity using Graph Neural Networks with Subgraph Training

https://gitlab.com/micronano_public/tb-mlff

공식 코드

Evaluating the Transferability of Machine-Learned Force Fields for Material Property Modeling

https://gitlab.idiap.ch/biometric/icassp2024.dvpf

공식 코드

Deep Variational Privacy Funnel: General Modeling with Applications in Face Recognition

https://gitlab.lrz.de/hctl/Iris-Obfuscation-Benchmark

공식 코드

Trade-offs in Privacy-Preserving Eye Tracking through Iris Obfuscation: A Benchmarking Study

https://huggingface.co/fxtentacle/wav2vec2-xls-r-1b-tevr

공식 코드

TEVR: Improving Speech Recognition by Token Entropy Variance Reduction

https://worksheets.codalab.org/worksheets/0x01838644724a433c932bef4cb5c42fbd

공식 코드

Graph-based, Self-Supervised Program Repair from Diagnostic Feedback

https://github.com/0h-n0/tfdbonas

Deep Neural Architecture Search with Deep Graph Bayesian Optimization

https://github.com/0h-n0/thdbonas

Deep Neural Architecture Search with Deep Graph Bayesian Optimization

https://github.com/2023-MindSpore-4/Code12/tree/main/dengzhaohong/GFS_MindSpore-main

Graph Fuzzy System: Concepts, Models and Algorithms

https://github.com/2023-MindSpore-4/Code16/tree/main/baopeng/HW-GTGCN

Pre-training Enhanced Spatial-temporal Graph Neural Network for Multivariate Time Series Forecasting

https://github.com/ADL-TBB/tbb-bridge-dpi

BridgeDPI: A Novel Graph Neural Network for Predicting Drug-Protein Interactions

https://github.com/AICPS/roadscene2vec

Spatio-Temporal Scene-Graph Embedding for Autonomous Vehicle Collision Prediction

https://github.com/Aditya239233/GNNExplainer

GNNExplainer: Generating Explanations for Graph Neural Networks

https://github.com/Ahren09/dygetviz

Chickenpox Cases in Hungary: a Benchmark Dataset for Spatiotemporal Signal Processing with Graph Neural Networks

https://github.com/AidanRocke/vertex_prediction

Efficient Interactive Annotation of Segmentation Datasets with Polygon-RNN++

https://github.com/AlexDuvalinho/GNNE_eval

GNNExplainer: Generating Explanations for Graph Neural Networks

https://github.com/AlexisGuichemerreCode/survey_hist_wsol_sfda

Source-Free Domain Adaptation via Distribution Estimation

https://github.com/AndrewZhe/Three-Sentences-Are-All-You-Need

Three Sentences Are All You Need: Local Path Enhanced Document Relation Extraction

https://github.com/AshwinRJ/Federated-Learning-PyTorch

Federated Learning: Challenges, Methods, and Future Directions

https://github.com/AstraZeneca/chemicalx

MR-GNN: Multi-Resolution and Dual Graph Neural Network for Predicting Structured Entity Interactions

https://github.com/AwaleSajil/SuperGlue_Demo_Modified1

SuperGlue: Learning Feature Matching with Graph Neural Networks

https://github.com/Ayumu-J-S/Point-JEPA

Fragmented Layer Grouping in GUI Designs Through Graph Learning Based on Multimodal Information

https://github.com/BRAIN-chain/FRAIN

Federated Learning: Challenges, Methods, and Future Directions

https://github.com/BUPT-GAMMA/HeCo

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

https://github.com/BamLubi/Paddle-SIGN

Detecting Beneficial Feature Interactions for Recommender Systems

https://github.com/Berenice2018/PySyft-Bc

A generic framework for privacy preserving deep learning

https://github.com/CHFsky/GCC-paddle

GCC: Graph Contrastive Coding for Graph Neural Network Pre-Training

https://github.com/COPS-IITBHU/HCSFL

Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge

https://github.com/Championchess/Generative-3D-Part-Assembly

Generative 3D Part Assembly via Dynamic Graph Learning

https://github.com/ChandlerBang/SimP-GCN

Graph Structure Learning for Robust Graph Neural Networks

https://github.com/CharlieDinh/FEDL_pytorch

Federated Learning over Wireless Networks: Convergence Analysis and Resource Allocation

https://github.com/CharlieSCC/alpha/tree/main/alpha/model/THGNN

Temporal and Heterogeneous Graph Neural Network for Financial Time Series Prediction

https://github.com/ChenDelong1999/HHForecasting

Significant Wave Height Prediction based on Wavelet Graph Neural Network

https://github.com/ClaireMcKayBowen/Code-for-NIST-PSCR-Differential-Privacy-Synthetic-Data-Challenge

Really Useful Synthetic Data -- A Framework to Evaluate the Quality of Differentially Private Synthetic Data

https://github.com/CoderHun/VectorNet_Implement

VectorNet: Encoding HD Maps and Agent Dynamics from Vectorized Representation

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