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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/uidmice/placement-rl

공식 코드

GiPH: Generalizable Placement Learning for Adaptive Heterogeneous Computing

https://github.com/ujeong1/bluetempnet-ieee-data-2024

공식 코드

BlueTempNet: A Temporal Multi-network Dataset of Social Interactions in Bluesky Social

https://github.com/ukaukaaaa/GazeGNN

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GazeGNN: A Gaze-Guided Graph Neural Network for Chest X-ray Classification

https://github.com/ulab-uiuc/gor

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Graph of Records: Boosting Retrieval Augmented Generation for Long-context Summarization with Graphs

https://github.com/um-dsp/PRICURE

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PRICURE: Privacy-Preserving Collaborative Inference in a Multi-Party Setting

https://github.com/umassos/flowsense

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FlowSense: Monitoring Airflow in Building Ventilation Systems Using Audio Sensing

https://github.com/umdatasciencelab/federated_gaussian_process

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Federated Gaussian Process: Convergence, Automatic Personalization and Multi-fidelity Modeling

https://github.com/unites-lab/mew

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Mew: Multiplexed Immunofluorescence Image Analysis through an Efficient Multiplex Network

https://github.com/unstable-zeros/grnn-comms-codesign

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Communication Topology Co-Design in Graph Recurrent Neural Network Based Distributed Control

https://github.com/uoe-agents/epymarl

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Pareto Actor-Critic for Equilibrium Selection in Multi-Agent Reinforcement Learning

https://github.com/uoe-agents/po-gpl

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A General Learning Framework for Open Ad Hoc Teamwork Using Graph-based Policy Learning

https://github.com/ur-whitelab/chem-dual-use

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Censoring chemical data to mitigate dual use risk

https://github.com/urielsinger/tbdfs

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tBDFS: Temporal Graph Neural Network Leveraging DFS

https://github.com/usail-hkust/bkdfedgcn

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Bkd-FedGNN: A Benchmark for Classification Backdoor Attacks on Federated Graph Neural Network

https://github.com/usail-hkust/causality-inspired-gnns

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When Graph Neural Network Meets Causality: Opportunities, Methodologies and An Outlook

https://github.com/usccolumbia/deeperGATGNN

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Scalable deeper graph neural networks for high-performance materials property prediction

https://github.com/usccolumbia/materialsuq

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Materials Property Prediction with Uncertainty Quantification: A Benchmark Study

https://github.com/user683/DRGO

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Distributionally Robust Graph Out-of-Distribution Recommendation via Diffusion Model

https://github.com/usydnlp/inducttgcn

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InducT-GCN: Inductive Graph Convolutional Networks for Text Classification

https://github.com/uta-smile/RetroXpert

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RetroXpert: Decompose Retrosynthesis Prediction like a Chemist

https://github.com/utkd/cogcn

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Graph Neural Network to Dilute Outliers for Refactoring Monolith Application

https://github.com/uvm-plaid/backpropagation-clipping

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Backpropagation Clipping for Deep Learning with Differential Privacy

https://github.com/uvm-plaid/private_llm_generation

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Differentially Private Learning Needs Better Model Initialization and Self-Distillation

https://github.com/uzh-dqbm-cmi/graphnn

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DDoS: A Graph Neural Network based Drug Synergy Prediction Algorithm

https://github.com/uzl-privsec/dp-mondrian-clustering

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DPM: Clustering Sensitive Data through Separation

https://github.com/vaikkunth/PrivacyFL

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PrivacyFL: A simulator for privacy-preserving and secure federated learning

https://github.com/valentin-hartmann-research/s5

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Secure Summation via Subset Sums: A New Primitive for Privacy-Preserving Distributed Machine Learning

https://github.com/vangiel/WheresTheFellow

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Multi-camera Torso Pose Estimation using Graph Neural Networks

https://github.com/vankesteren/privreg

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Privacy-Preserving Generalized Linear Models using Distributed Block Coordinate Descent

https://github.com/vdasu/deduplication

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Privacy-Preserving Data Deduplication for Enhancing Federated Learning of Language Models (Extended Version)

https://github.com/vdvchen/sgmnet

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Learning to Match Features with Seeded Graph Matching Network

https://github.com/ventr1c/infattack

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Efficient, Direct, and Restricted Black-Box Graph Evasion Attacks to Any-Layer Graph Neural Networks via Influence Function

https://github.com/vgsatorras/few-shot-gnn

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Few-Shot Learning with Graph Neural Networks

https://github.com/vgsatorras/hybrid-inference

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Combining Generative and Discriminative Models for Hybrid Inference

https://github.com/victordarvariu/solving-graph-pgg

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Solving Graph-based Public Good Games with Tree Search and Imitation Learning

https://github.com/victorzxy/graphac

공식 코드

Task-Agnostic Graph Neural Network Evaluation via Adversarial Collaboration

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