Papers with Code 아카이브

Privacy Preserving

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코드

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

https://github.com/alexandrehuat/dp-gan

Differentially Private Releasing via Deep Generative Model (Technical Report)

https://github.com/alge24/eigenpooling

Graph Convolutional Networks with EigenPooling

https://github.com/amazon-research/panrep

PanRep: Graph neural networks for extracting universal node embeddings in heterogeneous graphs

https://github.com/amazon-science/co-with-gnns-example

Combinatorial Optimization with Physics-Inspired Graph Neural Networks

https://github.com/amblee0306/Simplicial-Graph-Attention-Network

SGAT: Simplicial Graph Attention Network

https://github.com/amzn/differential-privacy-bayesian-optimization

Automatic Discovery of Privacy-Utility Pareto Fronts

https://github.com/anaezquerro/incpar

Strongly Incremental Constituency Parsing with Graph Neural Networks

https://github.com/anchit1704/PytorchReimplementation_Robust_and_Privacy_preserving_Text_Representations_KaggleNotebook

Towards Robust and Privacy-preserving Text Representations

https://github.com/annehartebrodt/fc-federated-svd

The FeatureCloud AI Store for Federated Learning in Biomedicine and Beyond

https://github.com/anonymous-sourcecode/DifNN

Get Rid of Suspended Animation Problem: Deep Diffusive Neural Network on Graph Semi-Supervised Classification

https://github.com/anonymous-sourcecode/GResNet

GResNet: Graph Residual Network for Reviving Deep GNNs from Suspended Animation

https://github.com/anonymous-sourcecode/Graph-Bert

Graph-Bert: Only Attention is Needed for Learning Graph Representations

https://github.com/anshul3899/GNNExplainer-Experiments

GNNExplainer: Generating Explanations for Graph Neural Networks

https://github.com/ansonb/RECON

RECON: Relation Extraction using Knowledge Graph Context in a Graph Neural Network

https://github.com/antoniopurificato/sheaf4rec

Sheaf Neural Networks with Connection Laplacians

https://github.com/arianemarandon/linkpredconf

Link Prediction Based on Graph Neural Networks

https://github.com/ashishdandekar/Privacy-at-risk

Differential Privacy at Risk: Bridging Randomness and Privacy Budget

https://github.com/asrtoufa/wikispeedia-paths-dual-hypergraph-features

Extrapolating paths with graph neural networks

https://github.com/astrazeneca/polypharmacy-ddi-synergy-survey

MOOMIN: Deep Molecular Omics Network for Anti-Cancer Drug Combination Therapy

https://github.com/atomicarchitects/dens

Equiformer: Equivariant Graph Attention Transformer for 3D Atomistic Graphs

https://github.com/atomicarchitects/equiformer_v2

The Open Catalyst 2020 (OC20) Dataset and Community Challenges

https://github.com/automl-4paradigm/snag

Simplifying Architecture Search for Graph Neural Network

https://github.com/automl-research/snag

Simplifying Architecture Search for Graph Neural Network

https://github.com/avakanski/Graph_NNs_for_Multi_Omics_Integration

Comparative Analysis of Multi-Omics Integration Using Advanced Graph Neural Networks for Cancer Classification

https://github.com/awslabs/privacy-preserving-xgboost-inference

Privacy-Preserving XGBoost Inference

https://github.com/awslabs/sagemaker-privacy-for-nlp

Privacy- and Utility-Preserving Textual Analysis via Calibrated Multivariate Perturbations

https://github.com/bachnguyente/temporal-mgn

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

https://github.com/bahmanmdd/HDLMF_GIN-GA

A hybrid deep-learning-metaheuristic framework for bi-level network design problems

https://github.com/basiralab/RG-Select

Hierarchical Graph Representation Learning with Differentiable Pooling

https://github.com/basiralab/Reproducible-Generative-Learning

Hierarchical Graph Representation Learning with Differentiable Pooling

https://github.com/basiralab/reproduciblefedgnn

Hierarchical Graph Representation Learning with Differentiable Pooling

https://github.com/benedekrozemberczki/SimGNN

SimGNN: A Neural Network Approach to Fast Graph Similarity Computation

https://github.com/benedekrozemberczki/benedekrozemberczki

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

https://github.com/benedekrozemberczki/spatiotemporal_datasets

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

https://github.com/bhli168/MDD_GNN

Heterogeneous graph neural network for power allocation in multicarrier-division duplex cell-free massive MIMO systems

https://github.com/binarybrain-009/MobAppRS

SAppKG: Mobile App Recommendation Using Knowledge Graph and Side Information-A Secure Framework

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