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

https://github.com/zhouhaocv/RLM-Net

공식 코드

Visual Relationship Detection with Relative Location Mining

https://github.com/zhu00121/wavrx

공식 코드

WavRx: a Disease-Agnostic, Generalizable, and Privacy-Preserving Speech Health Diagnostic Model

https://github.com/zhuangdingyi/stzinb

공식 코드

Uncertainty Quantification of Sparse Travel Demand Prediction with Spatial-Temporal Graph Neural Networks

https://github.com/zhuhm1996/bgnn

공식 코드

Bilinear Graph Neural Network with Neighbor Interactions

https://github.com/zhyczy/fedtp

공식 코드

FedTP: Federated Learning by Transformer Personalization

https://github.com/zihangxiang/-practical-differentially-private-and-byzantine-resilient-federated

공식 코드

Practical Differentially Private and Byzantine-resilient Federated Learning

https://github.com/zilongzheng/visdial-gnn

공식 코드

Reasoning Visual Dialogs with Structural and Partial Observations

https://github.com/zixianreid/semantic_graph_code_code_clone

공식 코드

AST-Enhanced or AST-Overloaded? The Surprising Impact of Hybrid Graph Representations on Code Clone Detection

https://github.com/zjl12138/uldgnn

공식 코드

Fragmented Layer Grouping in GUI Designs Through Graph Learning Based on Multimodal Information

https://github.com/zju-llms/awesome-loras

공식 코드

A Survey on LoRA of Large Language Models

https://github.com/zjukg/MEAformer

공식 코드

ASGEA: Exploiting Logic Rules from Align-Subgraphs for Entity Alignment

https://github.com/zjukg/maker

공식 코드

Meta-Learning Based Knowledge Extrapolation for Knowledge Graphs in the Federated Setting

https://github.com/zjunet/EvoNet

공식 코드

Time-Series Event Prediction with Evolutionary State Graph

https://github.com/zjunet/G-Tuning

공식 코드

Fine-tuning Graph Neural Networks by Preserving Graph Generative Patterns

https://github.com/zjunet/graphadapter

공식 코드

Can GNN be Good Adapter for LLMs?

https://github.com/zknus/iclr2024-frond

공식 코드

Unleashing the Potential of Fractional Calculus in Graph Neural Networks with FROND

https://github.com/zknus/neurips-2024-dragon

공식 코드

Distributed-Order Fractional Graph Operating Network

https://github.com/zknus/torchfde

공식 코드

Unleashing the Potential of Fractional Calculus in Graph Neural Networks with FROND

https://github.com/zkxinxin/hcmgnn

공식 코드

Heterogeneous Causal Metapath Graph Neural Network for Gene-Microbe-Disease Association Prediction

https://github.com/zkz-brain/ci-gnn

공식 코드

CI-GNN: A Granger Causality-Inspired Graph Neural Network for Interpretable Brain Network-Based Psychiatric Diagnosis

https://github.com/zlinaf/powergear

공식 코드

PowerGear: Early-Stage Power Estimation in FPGA HLS via Heterogeneous Edge-Centric GNNs

https://github.com/zohairshafi/Graph-SCP

공식 코드

Graph-SCP: Accelerating Set Cover Problems with Graph Neural Networks

https://github.com/zongyi-li/graph-pde

공식 코드

Multipole Graph Neural Operator for Parametric Partial Differential Equations

https://github.com/zorzi-s/polyworldpretrainednetwork

공식 코드

PolyWorld: Polygonal Building Extraction with Graph Neural Networks in Satellite Images

https://github.com/zrgcityu/pegfan

공식 코드

Permutation Equivariant Graph Framelets for Heterophilous Graph Learning

https://github.com/zwangeo/autogel

공식 코드

AutoGEL: An Automated Graph Neural Network with Explicit Link Information

https://github.com/zxlovely/FedKF

공식 코드

Handling Data Heterogeneity in Federated Learning via Knowledge Distillation and Fusion

https://github.com/zyl199710/hipgnn

공식 코드

Rethinking Cancer Gene Identification through Graph Anomaly Analysis

https://github.com/zylMozart/HiGNN

공식 코드

Learn from Heterophily: Heterophilous Information-enhanced Graph Neural Network

https://github.com/zyli93/saecon

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Powering Comparative Classification with Sentiment Analysis via Domain Adaptive Knowledge Transfer

https://github.com/zzhang1987/Factor-Graph-Neural-Network

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Factor Graph Neural Network

https://github.com/zzy979/gnn-recommendation

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RHCO: A Relation-aware Heterogeneous Graph Neural Network with Contrastive Learning for Large-scale Graphs

https://gitlab.cern.ch/gdl4hep/hls4ml-etx4velo

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Comparative Analysis of FPGA and GPU Performance for Machine Learning-Based Track Reconstruction at LHCb

https://gitlab.com/compromise3/co-defend

공식 코드

CO-DEFEND: Continuous Decentralized Federated Learning for Secure DoH-Based Threat Detection

https://gitlab.com/compromise3/desrutge

공식 코드

Realistic Urban Traffic Generator using Decentralized Federated Learning for the SUMO simulator

https://gitlab.com/europeanspaceagency/pylattice2d

공식 코드

Differentiable graph-structured models for inverse design of lattice materials

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