Papers with Code 아카이브

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태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

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하위 태스크

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보존된 평가 기록

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코드

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

https://github.com/Lin-Group-at-UMass/FBGNN-MBE

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

https://github.com/LouisDumont/GCN---re-implementation

Semi-Supervised Classification with Graph Convolutional Networks

https://github.com/MUmarJaved/MultiAgent-Distributed-Reinforcement-Learning

Neural Message Passing for Quantum Chemistry

https://github.com/MehmetBarutcu/Streaming-Algorithm-for-Monotone-k-Submodular-Maximization-with-Cardinality-Constraints

Graph Attention Networks

https://github.com/Messham87/COVID_GAT

Graph Attention Networks

https://github.com/Microsoft/gated-graph-neural-network-samples

Neural Message Passing for Quantum Chemistry

https://github.com/Mind23-2/MindCode-4/tree/main/gat

Graph Attention Networks

https://github.com/MindSpore-scientific-2/code-4/tree/main/GR-HSTU

Graph Attention Networks

https://github.com/Namkyeong/NoisyNodes_Pytorch

Simple GNN Regularisation for 3D Molecular Property Prediction & Beyond

https://github.com/Negus25/GAT-Mindspore

Graph Attention Networks

https://github.com/PumpkinYing/GAT

Graph Attention Networks

https://github.com/PurdueMINDS/RelationalPooling

How Powerful are Graph Neural Networks?

https://github.com/Samyu0304/thought-propagation

Neural Message Passing for Quantum Chemistry

https://github.com/Saro00/DGN

Neural Message Passing for Quantum Chemistry

https://github.com/SylwiaNowakowska/LLM_Fine_Tuning_Molecular_Properties

ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction

https://github.com/Tony-Y/cgnn

Neural Message Passing for Quantum Chemistry

https://github.com/TyngJiunKuo/deep-learning-project

Graph Attention Networks

https://github.com/WantingZhao/my_GAT

Graph Attention Networks

https://github.com/XuHangkun/Compound-Protein-Interaction

ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction

https://github.com/Yindong-Zhang/myGAT

Graph Attention Networks

https://github.com/YunseobShin/wiki_GAT

Graph Attention Networks

https://github.com/ZJULearning/graph_level_drug_discovery

MoleculeNet: A Benchmark for Molecular Machine Learning

https://github.com/ZeinabRahbar/Implementation-for-the-paper-of-SEMI-SUPERVISED-CLASSIFICATION-WITH-GRAPH-CONVOLUTIONAL-NETWORKS

Semi-Supervised Classification with Graph Convolutional Networks

https://github.com/abhilash1910/GraphAttentionNetworks

Graph Attention Networks

https://github.com/abi-kothapalli/minimumdominatingsets

Semi-Supervised Classification with Graph Convolutional Networks

https://github.com/ai-hpc-research-team/git-mol

A Molecular Multimodal Foundation Model Associating Molecule Graphs with Natural Language

https://github.com/ai-hpc-research-team/slm4mol

A Molecular Multimodal Foundation Model Associating Molecule Graphs with Natural Language

https://github.com/ajbisberg/gcn

Semi-Supervised Classification with Graph Convolutional Networks

https://github.com/ak24watch/Graph-Deeplearning-GAT

Graph Attention Networks

https://github.com/akash18tripathi/MAGNET-Multi-Label-Text-Classi-cation-using-Attention-based-Graph-Neural-Network

Graph Attention Networks

https://github.com/akianfar/Deep-CBN

https://github.com/akirasosa/pytorch-dimenet

Directional Message Passing for Molecular Graphs

https://github.com/alexOarga/haiku-geometric

Semi-Supervised Classification with Graph Convolutional Networks

https://github.com/alexOarga/haiku-geometric/blob/main/haiku_geometric/nn/conv/gin_conv.py

How Powerful are Graph Neural Networks?

https://github.com/alexOarga/haiku-geometric/blob/main/haiku_geometric/nn/conv/pna_conv.py

Principal Neighbourhood Aggregation for Graph Nets

https://github.com/alililia/ms_contrib/tree/main/ascend_gcn

Semi-Supervised Classification with Graph Convolutional Networks

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