Papers with Code 아카이브

Privacy Preserving

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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/shakshi12/Rumor-Spreaders-using-GNN-approach-PHEME-dataset-

Identifying Possible Rumor Spreaders on Twitter: A Weak Supervised Learning Approach

https://github.com/shawnwang-tech/PM2.5-GNN

PM2.5-GNN: A Domain Knowledge Enhanced Graph Neural Network For PM2.5 Forecasting

https://github.com/shchur/gnn-benchmark

Pitfalls of Graph Neural Network Evaluation

https://github.com/shi27feng/transformers.satisfy

PDP: A General Neural Framework for Learning Constraint Satisfaction Solvers

https://github.com/shtuplus/pysgg

Bipartite Graph Network with Adaptive Message Passing for Unbiased Scene Graph Generation

https://github.com/simonschoelly/GraphDatasets.jl

TUDataset: A collection of benchmark datasets for learning with graphs

https://github.com/simonvino/graphwavenet_brain_connectivity

Graph WaveNet for Deep Spatial-Temporal Graph Modeling

https://github.com/simran-arora/privacy_fm

Position: Considerations for Differentially Private Learning with Large-Scale Public Pretraining

https://github.com/sjj0403/GSCS

Improved Code Summarization via a Graph Neural Network

https://github.com/sjtu-ipads/powerinfer

PowerInfer-2: Fast Large Language Model Inference on a Smartphone

https://github.com/skywalker023/confaide

Can LLMs Keep a Secret? Testing Privacy Implications of Language Models via Contextual Integrity Theory

https://github.com/smduan/Fed-TDA

Fed-TGAN: Federated Learning Framework for Synthesizing Tabular Data

https://github.com/smiles724/protmd

Molecular Mechanics-Driven Graph Neural Network with Multiplex Graph for Molecular Structures

https://github.com/snap-stanford/neural-subgraph-learning-gnn

Deep Graph Matching Consensus

https://github.com/snap-stanford/relgnn

RelBench: A Benchmark for Deep Learning on Relational Databases

https://github.com/songzeceng/KGNNLS_DEMO

Knowledge-aware Graph Neural Networks with Label Smoothness Regularization for Recommender Systems

https://github.com/soominkwon/DP-dSNE

AdaCliP: Adaptive Clipping for Private SGD

https://github.com/sreycodes/YLR

Link Prediction Based on Graph Neural Networks

https://github.com/sseung0703/IEPKT

Interpretable Embedding Procedure Knowledge Transfer via Stacked Principal Component Analysis and Graph Neural Network

https://github.com/statyui/GRU-PFG

GRU-PFG: Extract Inter-Stock Correlation from Stock Factors with Graph Neural Network

https://github.com/superlouis/GATGNN

Global Attention based Graph Convolutional Neural Networks for Improved Materials Property Prediction

https://github.com/suvarna-kadam/Oct2018_Demo

Few-Shot Learning with Graph Neural Networks

https://github.com/swtheing/Multiview-Link-Representation-Learning

Inductive Matrix Completion Using Graph Autoencoder

https://github.com/sxwee/GNNsIMPL/tree/main/H2GCN

Beyond Homophily in Graph Neural Networks: Current Limitations and Effective Designs

https://github.com/szbonaldo/fedmlp

FedNoRo: Towards Noise-Robust Federated Learning by Addressing Class Imbalance and Label Noise Heterogeneity

https://github.com/taojiang-github/GNN-IRS-Beamforming-Reflection

Learning to Reflect and to Beamform for Intelligent Reflecting Surface with Implicit Channel Estimation

https://github.com/team-tud/ctab-gan

CTAB-GAN+: Enhancing Tabular Data Synthesis

https://github.com/team-tud/ctab-gan-plus

CTAB-GAN+: Enhancing Tabular Data Synthesis

https://github.com/teddykoker/e3nn.c

Higher-Order Equivariant Neural Networks for Charge Density Prediction in Materials

https://github.com/tencentarc/opencompatible

Privacy-Preserving Model Upgrades with Bidirectional Compatible Training in Image Retrieval

https://github.com/thehimalayanleo/Private-Generative-Models

DP-EM: Differentially Private Expectation Maximization

https://github.com/threatrace-detector/threatrace

threaTrace: Detecting and Tracing Host-based Threats in Node Level Through Provenance Graph Learning

https://github.com/thudm/graphalign

GraphAlign: Pretraining One Graph Neural Network on Multiple Graphs via Feature Alignment

https://github.com/tiago-roxo/asdnb

End-to-End Active Speaker Detection

https://github.com/tiago-roxo/bias

End-to-End Active Speaker Detection

https://github.com/tmdt-buw/schlably

Learning to Dispatch for Job Shop Scheduling via Deep Reinforcement Learning

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