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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/pnnl/FragNet

공식 코드

FragNet: A Graph Neural Network for Molecular Property Prediction with Four Levels of Interpretability

https://github.com/pnnl/chemreasoner

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ChemReasoner: Heuristic Search over a Large Language Model's Knowledge Space using Quantum-Chemical Feedback

https://github.com/pnnl/downstream_mol_gnn

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Reducing Down(stream)time: Pretraining Molecular GNNs using Heterogeneous AI Accelerators

https://github.com/pnnl/mol_dgnn

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Geometric learning of the conformational dynamics of molecules using dynamic graph neural networks

https://github.com/polaris-jz/fellrec

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A Federated Framework for LLM-based Recommendation

https://github.com/polimi-ispl/magcrn

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Back to the Future: GNN-based NO$_2$ Forecasting via Future Covariates

https://github.com/poonpura/DPAgg-TI

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Differentially Private Adaptation of Diffusion Models via Noisy Aggregated Embeddings

https://github.com/pps-lab/fl-analysis

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RoFL: Robustness of Secure Federated Learning

https://github.com/pps-lab/rofl-project-code

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RoFL: Robustness of Secure Federated Learning

https://github.com/pqros/fastcover

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FastCover: An Unsupervised Learning Framework for Multi-Hop Influence Maximization in Social Networks

https://github.com/prajwalpanzade/medblindtuner

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MedBlindTuner: Towards Privacy-preserving Fine-tuning on Biomedical Images with Transformers and Fully Homomorphic Encryption

https://github.com/prash29/lager

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Towards Few-shot Entity Recognition in Document Images: A Graph Neural Network Approach Robust to Image Manipulation

https://github.com/princeton-vl/attach-juxtapose-parser

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Strongly Incremental Constituency Parsing with Graph Neural Networks

https://github.com/projectultra/GCBLANE

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GCBLANE: A graph-enhanced convolutional BiLSTM attention network for improved transcription factor binding site prediction

https://github.com/prometheusXN/LADAN

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Distinguish Confusing Law Articles for Legal Judgment Prediction

https://github.com/prometheusXN/MR-GNN

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MR-GNN: Multi-Resolution and Dual Graph Neural Network for Predicting Structured Entity Interactions

https://github.com/proroklab/ModGNN

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ModGNN: Expert Policy Approximation in Multi-Agent Systems with a Modular Graph Neural Network Architecture

https://github.com/proroklab/gnngls

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Graph Neural Network Guided Local Search for the Traveling Salesperson Problem

https://github.com/proroklab/hetgppo

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Heterogeneous Multi-Robot Reinforcement Learning

https://github.com/proroklab/vectorizedmultiagentsimulator

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Heterogeneous Multi-Robot Reinforcement Learning

https://github.com/provenanceanalytics/kairos

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Kairos: Practical Intrusion Detection and Investigation using Whole-system Provenance

https://github.com/publiccoderepo/SMP_GNN_Quant

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Low-bit Quantization for Deep Graph Neural Networks with Smoothness-aware Message Propagation

https://github.com/pxqian/fedafa

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Personalized Federated Learning on Long-Tailed Data via Adversarial Feature Augmentation

https://github.com/pyg-team/pytorch_geometric

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Do We Need Anisotropic Graph Neural Networks?

https://github.com/pyt-team/TopoBenchmarkX

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TopoTune : A Framework for Generalized Combinatorial Complex Neural Networks

https://github.com/q-rz/kdd23-ditto

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Reconstructing Graph Diffusion History from a Single Snapshot

https://github.com/q1park/spacetime

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Moment-Matching Graph-Networks for Causal Inference

https://github.com/qcydm/vgaer

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VGAER: Graph Neural Network Reconstruction based Community Detection

https://github.com/qiankunmu/hdhgn

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Heterogeneous Directed Hypergraph Neural Network over abstract syntax tree (AST) for Code Classification

https://github.com/qiaoyran/login

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LOGIN: A Large Language Model Consulted Graph Neural Network Training Framework

https://github.com/qiaoyu-tan/E2GNN

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E2GNN: Efficient Graph Neural Network Ensembles for Semi-Supervised Classification

https://github.com/qimingwu/fedida

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Toward Fair Federated Learning under Demographic Disparities and Data Imbalance

https://github.com/qiongwu86/drl-bfssl

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DRL-Based Resource Allocation for Motion Blur Resistant Federated Self-Supervised Learning in IoV

https://github.com/qiongwu86/optimizing-aoi-in-vec-with-federated-graph-neural-network-multi-agent-reinforcement-learning

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Optimizing Age of Information in Vehicular Edge Computing with Federated Graph Neural Network Multi-Agent Reinforcement Learning

https://github.com/qiongxiu/PrivacyOptimizationSubspace

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Privacy-Preserving Distributed Optimization via Subspace Perturbation: A General Framework

https://github.com/qitianwu/FATE

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Towards Open-World Feature Extrapolation: An Inductive Graph Learning Approach

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