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Entity Embeddings
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Entity Embeddings is a technique for applying deep learning to tabular data. It involves representing the categorical data of an information systems entity with multiple dimensions.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
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https://github.com/czy1999/MultiTQ
https://github.com/czy1999/SubGTR
https://github.com/deepeshhada/HyperTeNet
HyperTeNet: Hypergraph and Transformer-based Neural Network for Personalized List Continuation
https://github.com/grapestone5321/Kaggle-Abhishek_Thakur
https://github.com/hzli-ucas/StarGraph
StarGraph: Knowledge Representation Learning based on Incomplete Two-hop Subgraph
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ClusterEA: Scalable Entity Alignment with Stochastic Training and Normalized Mini-batch Similarities
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https://github.com/jwallat/knowledge-probing
https://github.com/ljy19970415/unibrain
https://github.com/migrationsKB/MGKB
Scalable Zero-shot Entity Linking with Dense Entity Retrieval
https://github.com/rajashah33/new-fit-recomm
A Deep Learning System for Predicting Size and Fit in Fashion E-Commerce
https://github.com/ruanchaves/BLINK
Scalable Zero-shot Entity Linking with Dense Entity Retrieval
https://github.com/samuelli97/cancer_survivability
https://github.com/tgebhart/sheaf_kg_transind
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.