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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/nliulab/FairFML

공식 코드

FairFML: Fair Federated Machine Learning with a Case Study on Reducing Gender Disparities in Cardiac Arrest Outcome Prediction

https://github.com/nliulab/FedScore

공식 코드

FedScore: A privacy-preserving framework for federated scoring system development

https://github.com/nliulab/fl-benchmark

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Federated Learning for Clinical Structured Data: A Benchmark Comparison of Engineering and Statistical Approaches

https://github.com/nnzhan/Graph-WaveNet

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Graph WaveNet for Deep Spatial-Temporal Graph Modeling

https://github.com/nnzhan/MTGNN

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Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks

https://github.com/nnzhan/traversenet

공식 코드

TraverseNet: Unifying Space and Time in Message Passing for Traffic Forecasting

https://github.com/nobrowning/seq_hgnn

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Seq-HGNN: Learning Sequential Node Representation on Heterogeneous Graph

https://github.com/nogrady/ldp-dl

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Locally Differentially Private Distributed Deep Learning via Knowledge Distillation

https://github.com/nokpil/rain

공식 코드

Learning Heterogeneous Interaction Strengths by Trajectory Prediction with Graph Neural Network

https://github.com/non-void/localmocap

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A Locality-based Neural Solver for Optical Motion Capture

https://github.com/nsidn98/informarl

공식 코드

Scalable Multi-Agent Reinforcement Learning through Intelligent Information Aggregation

https://github.com/ntkien1904/DegFairGNN

공식 코드

On Generalized Degree Fairness in Graph Neural Networks

https://github.com/ntuhep-qml/qcgnn

공식 코드

Jet Discrimination with Quantum Complete Graph Neural Network

https://github.com/nusioraprivacy/eaas-privacy

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Split-and-Denoise: Protect large language model inference with local differential privacy

https://github.com/nust-machine-intelligence-laboratory/hgpu

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Hierarchical Graph Pattern Understanding for Zero-Shot VOS

https://github.com/nutcrtnk/dhgnet

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Cross-lingual Transfer for Text Classification with Dictionary-based Heterogeneous Graph

https://github.com/nvidia/nvflare

공식 코드

NVIDIA FLARE: Federated Learning from Simulation to Real-World

https://github.com/nvlabs/gnn-decoder

공식 코드

Graph Neural Networks for Channel Decoding

https://github.com/nyu-dl/dl4chem-geometry

공식 코드

Molecular geometry prediction using a deep generative graph neural network

https://github.com/nyushcs/moleculargpt

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MolecularGPT: Open Large Language Model (LLM) for Few-Shot Molecular Property Prediction

https://github.com/ocram17/engagementgnn

공식 코드

Predicting Tweet Engagement with Graph Neural Networks

https://github.com/octopusli/neurips2022-traffic4cast

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Multi-task Learning for Sparse Traffic Forecasting

https://github.com/odor-pair/odor-pair

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Olfactory Label Prediction on Aroma-Chemical Pairs

https://github.com/oj9040/GraphSAGE_RL

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Advancing GraphSAGE with A Data-Driven Node Sampling

https://github.com/ol-mega/ppca

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Long-term Conversation Analysis: Exploring Utility and Privacy

https://github.com/omarerak/clad

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Contrastive Learning and Adversarial Disentanglement for Task-Oriented Semantic Communications

https://github.com/omicsml/dance

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Graph Neural Networks for Multimodal Single-Cell Data Integration

https://github.com/onlinehuazai/evit

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EViT: Privacy-Preserving Image Retrieval via Encrypted Vision Transformer in Cloud Computing

https://github.com/oopshell/Pixel-Blocks-Mixing-For-Image-Privacy-Preservation

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A Lightweight Privacy-Preserving Scheme Using Label-based Pixel Block Mixing for Image Classification in Deep Learning

https://github.com/openai/emoclassifiers

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Investigating Affective Use and Emotional Well-being on ChatGPT

https://github.com/opendrivelab/toponet

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Graph-based Topology Reasoning for Driving Scenes

https://github.com/openperceptionx/hdgt

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HDGT: Heterogeneous Driving Graph Transformer for Multi-Agent Trajectory Prediction via Scene Encoding

https://github.com/orion-orion/FedDCSR

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FedDCSR: Federated Cross-domain Sequential Recommendation via Disentangled Representation Learning

https://github.com/orion-orion/fedhcdr

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FedHCDR: Federated Cross-Domain Recommendation with Hypergraph Signal Decoupling

https://github.com/osu-srml/upcycled-fl

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Federated Learning with Reduced Information Leakage and Computation

https://github.com/osvaldfrisk/dp-not-all-noise-is-equal

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Not all noise is accounted equally: How differentially private learning benefits from large sampling rates

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