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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/risilab/Autobahn

공식 코드

Autobahn: Automorphism-based Graph Neural Nets

https://github.com/ritatsousa/expressionKGplus

공식 코드

Multi-dataset and Transfer Learning Using Gene Expression Knowledge Graphs

https://github.com/rmanluo/np-fkgc

공식 코드

Normalizing Flow-based Neural Process for Few-Shot Knowledge Graph Completion

https://github.com/rmazzier/OpenSetGaitRecognition_PCAA

공식 코드

Open-Set Gait Recognition from Sparse mmWave Radar Point Clouds

https://github.com/robbertreijnen/gs-modac

공식 코드

Graph-Supported Dynamic Algorithm Configuration for Multi-Objective Combinatorial Optimization

https://github.com/rochesterxugroup/DSGPM

공식 코드

Graph Neural Network Based Coarse-Grained Mapping Prediction

https://github.com/rochesterxugroup/HAM_dataset

공식 코드

Graph Neural Network Based Coarse-Grained Mapping Prediction

https://github.com/rollingstonezz/sdm24_manifold_spatial_networks

공식 코드

Non-Euclidean Spatial Graph Neural Network

https://github.com/romeov/ml4co-competition

공식 코드

Instance-wise algorithm configuration with graph neural networks

https://github.com/romm32/sagnn

공식 코드

Wireless Link Scheduling with State-Augmented Graph Neural Networks

https://github.com/rongrongma/cos-gnn

공식 코드

Harnessing Collective Structure Knowledge in Data Augmentation for Graph Neural Networks

https://github.com/rongrongma/mosgnn

공식 코드

Imbalanced Graph Classification with Multi-scale Oversampling Graph Neural Networks

https://github.com/rookitkitlee/bpmr

공식 코드

Behavior Pattern Mining-based Multi-Behavior Recommendation

https://github.com/roth-andreas/pprgnn

공식 코드

Transforming PageRank into an Infinite-Depth Graph Neural Network

https://github.com/royson/reefl

공식 코드

Recurrent Early Exits for Federated Learning with Heterogeneous Clients

https://github.com/rrgitan/LEGNN

공식 코드

Training a Label-Noise-Resistant GNN with Reduced Complexity

https://github.com/rrr-uom-projects/contour_auto_qatool

공식 코드

Automatic identification of segmentation errors for radiotherapy using geometric learning

https://github.com/rrrussell/caugnn

공식 코드

Multivariate Time Series Forecasting with Transfer Entropy Graph

https://github.com/rsalganik1123/facctrec2022

공식 코드

Analyzing the Effect of Sampling in GNNs on Individual Fairness

https://github.com/rsinghlab/gnn-tumor-seg

공식 코드

A Joint Graph and Image Convolution Network for Automatic Brain Tumor Segmentation

https://github.com/rsummers11/CADLab

공식 코드

Enhancing chest X-ray datasets with privacy-preserving large language models and multi-type annotations: a data-driven approach for improved classification

https://github.com/rtaiello/pp_dipy

공식 코드

Privacy Preserving Image Registration

https://github.com/rtaiello/pp_image_registration

공식 코드

Privacy Preserving Image Registration

https://github.com/rtealwitter/openstreets

공식 코드

I Open at the Close: A Deep Reinforcement Learning Evaluation of Open Streets Initiatives

https://github.com/rucaibox/cogcl

공식 코드

Enhancing Graph Contrastive Learning with Reliable and Informative Augmentation for Recommendation

https://github.com/rucaibox/ppgm

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Privacy-Preserved Neural Graph Similarity Learning

https://github.com/rucaibox/recbole2.0

공식 코드

RecBole 2.0: Towards a More Up-to-Date Recommendation Library

https://github.com/rucaibox/safe

공식 코드

Great Truths are Always Simple: A Rather Simple Knowledge Encoder for Enhancing the Commonsense Reasoning Capacity of Pre-Trained Models

https://github.com/ruclty/Daisy

공식 코드

Relational Data Synthesis using Generative Adversarial Networks: A Design Space Exploration

https://github.com/rui-yan/ssl-fl

공식 코드

Label-Efficient Self-Supervised Federated Learning for Tackling Data Heterogeneity in Medical Imaging

https://github.com/rui-ye/openfedllm

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OpenFedLLM: Training Large Language Models on Decentralized Private Data via Federated Learning

https://github.com/ruijie-wang-uzh/gnn2r

공식 코드

GNN2R: Weakly-Supervised Rationale-Providing Question Answering over Knowledge Graphs

https://github.com/ruizehan/par

공식 코드

Panoramic Human Activity Recognition

https://github.com/ruizhang-ai/SIGN-Detecting-Beneficial-Feature-Interactions-for-Recommender-Systems

공식 코드

Detecting Beneficial Feature Interactions for Recommender Systems

https://github.com/runzewang/IST-SQL

공식 코드

Tracking Interaction States for Multi-Turn Text-to-SQL Semantic Parsing

https://github.com/ruqizhang/dpfastmh

공식 코드

DP-Fast MH: Private, Fast, and Accurate Metropolis-Hastings for Large-Scale Bayesian Inference

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