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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://github.com/amrmalkhatib/ignh

공식 코드

Interpretable Graph Neural Networks for Heterogeneous Tabular Data

https://github.com/amrmalkhatib/ignnet

공식 코드

Interpretable Graph Neural Networks for Tabular Data

https://github.com/anasemad11/clap

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Collaborative Learning of Anomalies with Privacy (CLAP) for Unsupervised Video Anomaly Detection: A New Baseline

https://github.com/anders-wikum/GNN-OBM

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MAGNOLIA: Matching Algorithms via GNNs for Online Value-to-go Approximation

https://github.com/anders1123/PAGE

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PAGE: Parametric Generative Explainer for Graph Neural Network

https://github.com/andreeaiana/graph_confrec

공식 코드

GraphConfRec: A Graph Neural Network-Based Conference Recommender System

https://github.com/anirbanmukherjeexd/rid-twin

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RID-TWIN: An end-to-end pipeline for automatic face de-identification in videos

https://github.com/annabana17/naturesplitlearning

공식 코드

Feasibility Study of Multi-Site Split Learning for Privacy-Preserving Medical Systems under Data Imbalance Constraints in COVID-19, X-Ray, and Cholesterol Dataset

https://github.com/annoymous-submissions/ot_cost

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A Universal Metric of Dataset Similarity for Cross-silo Federated Learning

https://github.com/anonymoustool/NeurSA

공식 코드

Learning a Static Bug Finder from Data

https://github.com/anoushkavyas/dglr

공식 코드

Dynamic Structure Learning through Graph Neural Network for Forecasting Soil Moisture in Precision Agriculture

https://github.com/anton-jeran/L2S

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Listen2Scene: Interactive material-aware binaural sound propagation for reconstructed 3D scenes

https://github.com/anyleopeace/DURLECA

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Reinforced Epidemic Control: Saving Both Lives and Economy

https://github.com/apozas/private-tn

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Privacy-preserving machine learning with tensor networks

https://github.com/appfl/appfl

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APPFL: Open-Source Software Framework for Privacy-Preserving Federated Learning

https://github.com/apple/corenet

공식 코드

Bytes Are All You Need: Transformers Operating Directly On File Bytes

https://github.com/apple/ml-cvnets

공식 코드

Bytes Are All You Need: Transformers Operating Directly On File Bytes

https://github.com/apple/ml-dprecs

공식 코드

Randomized algorithms for precise measurement of differentially-private, personalized recommendations

https://github.com/aprbw/ood_electricity_forecasting_during_covid-19

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Navigating Out-of-Distribution Electricity Load Forecasting during COVID-19: Benchmarking energy load forecasting models without and with continual learning

https://github.com/ar-roy/dct-cryptonets

공식 코드

DCT-CryptoNets: Scaling Private Inference in the Frequency Domain

https://github.com/arakotom/dp_swd

공식 코드

Differentially Private Sliced Wasserstein Distance

https://github.com/arashsajjadi/TGraphX

공식 코드

TGraphX: Tensor-Aware Graph Neural Network for Multi-Dimensional Feature Learning

https://github.com/aravi11/funcGNN

공식 코드

funcGNN: A Graph Neural Network Approach to Program Similarity

https://github.com/aravindsankar28/Inf-VAE

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Inf-VAE: A Variational Autoencoder Framework to Integrate Homophily and Influence in Diffusion Prediction

https://github.com/archettialberto/federated_survival_forests

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Federated Survival Forests

https://github.com/arclab-hku/supereio

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SuperEIO: Self-Supervised Event Feature Learning for Event Inertial Odometry

https://github.com/ardhendubehera/sr-gnn

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SR-GNN: Spatial Relation-aware Graph Neural Network for Fine-Grained Image Categorization

https://github.com/arjunsubramonian/link_bias_amplification

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Networked Inequality: Preferential Attachment Bias in Graph Neural Network Link Prediction

https://github.com/arkadiyd/phylognn

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Solving the Tree Containment Problem Using Graph Neural Networks

https://github.com/armanbehnam/cxgnn

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Graph Neural Network Causal Explanation via Neural Causal Models

https://github.com/arnold-jun/fed-munet

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Fed-MUnet: Multi-modal Federated Unet for Brain Tumor Segmentation

https://github.com/arprl/arprl

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Learning Robust and Privacy-Preserving Representations via Information Theory

https://github.com/arthur-heng/spoiler-detection

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Detecting Spoilers in Movie Reviews with External Movie Knowledge and User Networks

https://github.com/arupmondal-cs/scotch

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SCOTCH: An Efficient Secure Computation Framework for Secure Aggregation

https://github.com/aryan-at-ul/aics_2023_submission

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Connecting the Dots: Graph Neural Network Powered Ensemble and Classification of Medical Images

https://github.com/asifiqbal8739/splitloadforecasting

공식 코드

Privacy-Preserving Collaborative Split Learning Framework for Smart Grid Load Forecasting

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