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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/dblanm/gnn-manip

공식 코드

Manipulation of Granular Materials by Learning Particle Interactions

https://github.com/dccspeed/rgpm

공식 코드

Graph Pattern Mining and Learning through User-defined Relations (Extended Version)

https://github.com/deepmind/deepmind-research/tree/master/wikigraphs

공식 코드

WikiGraphs: A Wikipedia Text - Knowledge Graph Paired Dataset

https://github.com/deepmind/jax_privacy

공식 코드

Unlocking Accuracy and Fairness in Differentially Private Image Classification

https://github.com/deepsphere/deepsphere-tf1

공식 코드

DeepSphere: towards an equivariant graph-based spherical CNN

https://github.com/degraphcs/DeGraphCS

공식 코드

deGraphCS: Embedding Variable-based Flow Graph for Neural Code Search

https://github.com/dengyh16code/deformable-gnn

공식 코드

Learning visual-based deformable object rearrangement with local graph neural networks

https://github.com/deno-v/heterfc

공식 코드

Heterogeneous Graph Reasoning for Fact Checking over Texts and Tables

https://github.com/dfdazac/mpqe

공식 코드

Message Passing Query Embedding

https://github.com/dfki-av/sg-pgm

공식 코드

SG-PGM: Partial Graph Matching Network with Semantic Geometric Fusion for 3D Scene Graph Alignment and Its Downstream Tasks

https://github.com/diagnijmegen/spgnn

공식 코드

Structure and position-aware graph neural network for airway labeling

https://github.com/diaoenmao/Decentralized-Multi-Target-Cross-Domain-Recommendation-for-Multi-Organization-Collaborations

공식 코드

Decentralized Multi-Target Cross-Domain Recommendation for Multi-Organization Collaborations

https://github.com/didi/hetsann

공식 코드

An Attention-based Graph Neural Network for Heterogeneous Structural Learning

https://github.com/dimitriStoidis/GenGAN

공식 코드

Generating gender-ambiguous voices for privacy-preserving speech recognition

https://github.com/dimneurolab/academic_network_project

공식 코드

Modeling Social Media Recommendation Impacts Using Academic Networks: A Graph Neural Network Approach

https://github.com/diningphil/gnn-comparison

공식 코드

A Fair Comparison of Graph Neural Networks for Graph Classification

https://github.com/diogodcarvalho/gns-sheet-model

공식 코드

Learning the dynamics of a one-dimensional plasma model with graph neural networks

https://github.com/dirsampling/privatedps

공식 코드

Dirichlet Mechanism for Differentially Private KL Divergence Minimization

https://github.com/divelab/DIG

공식 코드

ComENet: Towards Complete and Efficient Message Passing for 3D Molecular Graphs

https://github.com/divelab/DeeperGNN

공식 코드

Towards Deeper Graph Neural Networks

https://github.com/divelab/MoleculeX

공식 코드

Fast Quantum Property Prediction via Deeper 2D and 3D Graph Networks

https://github.com/divelab/graphbp

공식 코드

Generating 3D Molecules for Target Protein Binding

https://github.com/djordjened92/cca-gnnclust

공식 코드

Cross-Camera Data Association via GNN for Supervised Graph Clustering

https://github.com/dl-wg/gcn-lstm-wildfire

공식 코드

Explainable Global Wildfire Prediction Models using Graph Neural Networks

https://github.com/dmirlab-group/mgnn

공식 코드

Motif Graph Neural Network

https://github.com/dmlc/dgl

공식 코드

DistDGL: Distributed Graph Neural Network Training for Billion-Scale Graphs

https://github.com/dmlc/dgl/tree/master/examples/pytorch/hilander

공식 코드

Learning Hierarchical Graph Neural Networks for Image Clustering

https://github.com/dogregor/graph-fakenewsnet

공식 코드

Exploring Fake News Detection with Heterogeneous Social Media Context Graphs

https://github.com/dojeon-ai/strap

공식 코드

ST-RAP: A Spatio-Temporal Framework for Real Estate Appraisal

https://github.com/domiwk/privacy-preserving-ad-detection

공식 코드

Prosody-Driven Privacy-Preserving Dementia Detection

https://github.com/donlapark/dirichlet-mechanism

공식 코드

Dirichlet Mechanism for Differentially Private KL Divergence Minimization

https://github.com/dpbayes/collaborative-learning-with-dp-synthetic-twin-data

공식 코드

Collaborative Learning From Distributed Data With Differentially Private Synthetic Twin Data

https://github.com/dpclip/dpclip

공식 코드

Safeguarding Data in Multimodal AI: A Differentially Private Approach to CLIP Training

https://github.com/draym28/lsgnn

공식 코드

LSGNN: Towards General Graph Neural Network in Node Classification by Local Similarity

https://github.com/drmeerkat/Deep-Graph-Value-Network

공식 코드

Towards Sample Efficient Agents through Algorithmic Alignment

https://github.com/drndr/bert_ent_attack

공식 코드

Memorization of Named Entities in Fine-tuned BERT Models

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