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Inductive Learning

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

https://github.com/qitianwu/FATE

공식 코드

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

https://github.com/qkrdmsghk/textssl

공식 코드

Sparse Structure Learning via Graph Neural Networks for Inductive Document Classification

https://github.com/rafelps/RRULES-rule-based-classifier

공식 코드

RRULES: An improvement of the RULES rule-based classifier

https://github.com/ssgood/mmgl

공식 코드

Multi-modal Graph Learning for Disease Prediction

https://github.com/suzgunmirac/lstm-eval

공식 코드

On Evaluating the Generalization of LSTM Models in Formal Languages

https://github.com/vitae-transformer/vos-llb

공식 코드

Learning to Learn Better for Video Object Segmentation

https://github.com/xiaofei05/tsst

공식 코드

Transductive Learning for Unsupervised Text Style Transfer

https://github.com/xslangley/gtea

공식 코드

GTEA: Inductive Representation Learning on Temporal Interaction Graphs via Temporal Edge Aggregation

https://github.com/xuchensjtu/signnap-master-online

공식 코드

Learning Node Representations against Perturbations

https://github.com/yasminsalehi/physgnn

공식 코드

PhysGNN: A Physics-Driven Graph Neural Network Based Model for Predicting Soft Tissue Deformation in Image-Guided Neurosurgery

https://github.com/zhengchuanpan/increase

공식 코드

INCREASE: Inductive Graph Representation Learning for Spatio-Temporal Kriging

https://github.com/zhw12/BERTRL

공식 코드

Inductive Relation Prediction by BERT

https://github.com/zifengding/fitcarl

공식 코드

Improving Few-Shot Inductive Learning on Temporal Knowledge Graphs using Confidence-Augmented Reinforcement Learning

https://zenodo.org/record/8336156

공식 코드

Chronosymbolic Learning: Efficient CHC Solving with Symbolic Reasoning and Inductive Learning

https://github.com/0xpayne/deep-hyperedges

Deep Hyperedges: a Framework for Transductive and Inductive Learning on Hypergraphs

https://github.com/Aminsheykh98/INCREASE-pytorch

INCREASE: Inductive Graph Representation Learning for Spatio-Temporal Kriging

https://github.com/BinWang28/InductivE

Inductive Learning on Commonsense Knowledge Graph Completion

https://github.com/GraphSAINT/GraphACT

GraphSAINT: Graph Sampling Based Inductive Learning Method

https://github.com/MindCode-4/code-12/tree/main/GTE

GTEA: Inductive Representation Learning on Temporal Interaction Graphs via Temporal Edge Aggregation

https://github.com/Niousha12/Text_Classification_via_GNN

Every Document Owns Its Structure: Inductive Text Classification via Graph Neural Networks

https://github.com/PurdueMINDS/Equivalence

On the Equivalence between Positional Node Embeddings and Structural Graph Representations

https://github.com/SY1706203/GATNE

Representation Learning for Attributed Multiplex Heterogeneous Network

https://github.com/Sam131112/Deep-Graph-Infomax-Implementation

Deep Graph Infomax

https://github.com/Sean-Bin-Yang/Path-InfoMax

Deep Graph Infomax

https://github.com/Shuilin123/ADB

A New Interpolation Approach and Corresponding Instance-Based Learning

https://github.com/SpaceLearner/DisenGraphRep

Deep Graph Infomax

https://github.com/THUDM/GATNE

Representation Learning for Attributed Multiplex Heterogeneous Network

https://github.com/YuweiCao-UIC/KPGNN

Knowledge-Preserving Incremental Social Event Detection via Heterogeneous GNNs

https://github.com/abojchevski/graph2gauss

Deep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via Ranking

https://github.com/amin-salehi/GATE

Graph Attention Auto-Encoders

https://github.com/ashutosh1919/neuro-symbolic-sudoku-solver

Neural Logic Machines

https://github.com/benedekrozemberczki/karateclub

Learning Role-based Graph Embeddings

https://github.com/benedekrozemberczki/role2vec

Learning Role-based Graph Embeddings

https://github.com/chronosymbolic/chronosymbolic-learning

Chronosymbolic Learning: Efficient CHC Solving with Symbolic Reasoning and Inductive Learning

https://github.com/dfdazac/dgi

Deep Graph Infomax

https://github.com/dmlc/dgl/tree/master/examples/pytorch/GATNE-T

Representation Learning for Attributed Multiplex Heterogeneous Network

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