https://github.com/prachiisc/SHARC
Supervised Hierarchical Clustering using Graph Neural Networks for Speaker Diarization
Papers with Code 아카이브
Privacy Preserving
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/proroklab/gnn_pathplanning
Graph Neural Networks for Decentralized Multi-Robot Path Planning
https://github.com/pulkit1joshi/SimGNN
SimGNN: A Neural Network Approach to Fast Graph Similarity Computation
https://github.com/pwc-1/Paper-9/tree/main/6/Cybertron
Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks
https://github.com/pwc-1/Paper-9/tree/main/7/RGNN
EEG-Based Emotion Recognition Using Regularized Graph Neural Networks
https://github.com/pygod-team/pygod
One-Class Graph Neural Networks for Anomaly Detection in Attributed Networks
https://github.com/q3erf/GLAM
Joint Graph Learning and Matching for Semantic Feature Correspondence
https://github.com/qiaoyu-tan/s2gae
MGAE: Masked Autoencoders for Self-Supervised Learning on Graphs
https://github.com/qiwang233/dailydvs-200
HARDVS: Revisiting Human Activity Recognition with Dynamic Vision Sensors
https://github.com/rafa-cxg/PySGG-cxg
Bipartite Graph Network with Adaptive Message Passing for Unbiased Scene Graph Generation
https://github.com/rahn80643/federated-learning-pytorch-he-smap
FedML-HE: An Efficient Homomorphic-Encryption-Based Privacy-Preserving Federated Learning System
https://github.com/randlbem/early_icu_mortality_prediction_with_deep_fl
FLICU: A Federated Learning Workflow for Intensive Care Unit Mortality Prediction
https://github.com/razvanc92/enhancenet
https://github.com/realmoana/proxyexplainer
GNNExplainer: Generating Explanations for Graph Neural Networks
https://github.com/richardobi/medigan
Enhancing the Utility of Privacy-Preserving Cancer Classification using Synthetic Data
https://github.com/ricolike/mmgnn_textvqa
Multi-Modal Graph Neural Network for Joint Reasoning on Vision and Scene Text
https://github.com/rish-16/rna-backbone-design
https://github.com/rogerwwww/PCA-GM
SuperGlue: Learning Feature Matching with Graph Neural Networks
https://github.com/ronuchit/LOFT_IROS_2021
https://github.com/rui-ye/fedllm-bench
OpenFedLLM: Training Large Language Models on Decentralized Private Data via Federated Learning
https://github.com/ruotianluo/zsl-gcn-pth
Zero-shot Recognition via Semantic Embeddings and Knowledge Graphs
https://github.com/ruveydayilmaz0/bvdm
https://github.com/safe-graph/DGFraud/tree/master/algorithms/GEM
Heterogeneous Graph Neural Networks for Malicious Account Detection
https://github.com/safe-graph/DGFraud/tree/master/algorithms/SemiGNN
A Semi-supervised Graph Attentive Network for Financial Fraud Detection
https://github.com/safe-graph/dgfraud-tf2
Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged Fraudsters
https://github.com/safreita1/malnet-image
https://github.com/saikat107/Devign
https://github.com/sampl-weizmann/deepcut
DeepCut: Unsupervised Segmentation using Graph Neural Networks Clustering
https://github.com/sapmlresearch/DiffPrivate_FedLearning
Differentially Private Federated Learning: A Client Level Perspective
https://github.com/scicompmod/memilio
https://github.com/sciueferrara/fcf-huawei
Federated Collaborative Filtering for Privacy-Preserving Personalized Recommendation System
https://github.com/serengil/cipherface
https://github.com/serengil/deepface
https://github.com/serengil/lightphe
https://github.com/seyoungahn/JSAC_FedDif
Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classification
https://github.com/seyoungahn/feddif
Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classification
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.