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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/oval-group/GNN_branching
공식 코드https://github.com/ozolotareva/Flimma
공식 코드Flimma: a federated and privacy-preserving tool for differential gene expression analysis
https://github.com/p-fclus/p-fclus
공식 코드https://github.com/p3eft-iclr-2025/p3eft-iclr-2025
공식 코드Label Privacy in Split Learning for Large Models with Parameter-Efficient Training
https://github.com/papachristoumarios/dp-social-learning
공식 코드https://github.com/papercodeeric/lfh
공식 코드Learning from Heterogeneity: A Dynamic Learning Framework for Hypergraphs
https://github.com/parajain/dynamic_context
공식 코드Conversational Semantic Parsing using Dynamic Context Graphs
https://github.com/parksandrecfan/bignet-car
공식 코드Automating Style Analysis and Visualization With Explainable AI -- Case Studies on Brand Recognition
https://github.com/parksandrecfan/bignet-phone
공식 코드Automating Style Analysis and Visualization With Explainable AI -- Case Studies on Brand Recognition
https://github.com/pasalab/autoac
공식 코드AutoAC: Towards Automated Attribute Completion for Heterogeneous Graph Neural Network
https://github.com/pathpy/pathpyg
공식 코드Using Time-Aware Graph Neural Networks to Predict Temporal Centralities in Dynamic Graphs
https://github.com/paulhausner/neural-incomplete-factorization
공식 코드https://github.com/pavanravva/enhanced-mmasd
공식 코드https://github.com/payalkamboj/cupkl
공식 코드Generating customized prompts for Zero-Shot Rare Event Medical Image Classification using LLM
https://github.com/pc-ng/rss_HumanHuman
공식 코드COVID-19 and Your Smartphone: BLE-based Smart Contact Tracing
https://github.com/pc-ng/rss_smartphone
공식 코드COVID-19 and Your Smartphone: BLE-based Smart Contact Tracing
https://github.com/pengxin-guo/fedprllm
공식 코드https://github.com/pengxin-guo/hyperfl
공식 코드A New Federated Learning Framework Against Gradient Inversion Attacks
https://github.com/pengxingang/pocket2mol
공식 코드Pocket2Mol: Efficient Molecular Sampling Based on 3D Protein Pockets
https://github.com/penlu/bespoke-gnn4do
공식 코드https://github.com/perceivelab/feder
공식 코드FedER: Federated Learning through Experience Replay and Privacy-Preserving Data Synthesis
https://github.com/perceivelab/plan
공식 코드A Privacy-Preserving Walk in the Latent Space of Generative Models for Medical Applications
https://github.com/persist-lab/syntheticportalgen
공식 코드https://github.com/petitioner/IDASH2017
공식 코드Privacy-Preserving Logistic Regression Training with A Faster Gradient Variant
https://github.com/petitioner/he.cnninfer
공식 코드Volley Revolver: A Novel Matrix-Encoding Method for Privacy-Preserving Neural Networks (Inference)
https://github.com/petitioner/he.cnntraining
공식 코드Privacy-Preserving CNN Training with Transfer Learning: Multiclass Logistic Regression
https://github.com/petitioner/he.lr
공식 코드Privacy-Preserving Logistic Regression Training with A Faster Gradient Variant
https://github.com/petrosrapto/pakton
공식 코드PAKTON: A Multi-Agent Framework for Question Answering in Long Legal Agreements
https://github.com/pfnet-research/chainer-chemistry
공식 코드https://github.com/pg2455/Hybrid-learn2branch
공식 코드https://github.com/philipph77/DAPE-Framework
공식 코드Domain-Invariant Representation Learning from EEG with Private Encoders
https://github.com/pietrobarbiero/digital-patient
공식 코드Graph representation forecasting of patient's medical conditions: towards a digital twin
https://github.com/pin-ai/god-model
공식 코드GOD model: Privacy Preserved AI School for Personal Assistant
https://github.com/pkang2017/tactilelsn
공식 코드Boost Event-Driven Tactile Learning with Location Spiking Neurons
https://github.com/pkuyzy/TLC-GNN
공식 코드Link Prediction with Persistent Homology: An Interactive View
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.