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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.
https://github.com/lcai2/stea
공식 코드https://github.com/lshowway/ered
공식 코드Ered: Enhanced Text Representations with Entities and Descriptions
https://github.com/miralab-ustc/gcn4kgc
공식 코드Rethinking Graph Convolutional Networks in Knowledge Graph Completion
https://github.com/mvijaikumar/hypertenet
공식 코드HyperTeNet: Hypergraph and Transformer-based Neural Network for Personalized List Continuation
https://github.com/nirlotan/SocialVec
공식 코드https://github.com/nju-websoft/TransEdge
공식 코드TransEdge: Translating Relation-contextualized Embeddings for Knowledge Graphs
https://github.com/nmerkle/sw_journal
공식 코드https://github.com/openai/deeptype
공식 코드DeepType: Multilingual Entity Linking by Neural Type System Evolution
https://github.com/pinterest/atg-research
공식 코드OmniSearchSage: Multi-Task Multi-Entity Embeddings for Pinterest Search
https://github.com/qdin4048/CPL-OT
공식 코드Conflict-Aware Pseudo Labeling via Optimal Transport for Entity Alignment
https://github.com/rstriv/Know-Evolve
공식 코드Know-Evolve: Deep Temporal Reasoning for Dynamic Knowledge Graphs
https://github.com/ruizhang-ai/autoalign
공식 코드AutoAlign: Fully Automatic and Effective Knowledge Graph Alignment enabled by Large Language Models
https://github.com/satya77/Entity_Embedding
공식 코드https://github.com/sunnyUD/research_source_code
공식 코드Capturing Knowledge of Emerging Entities From Extended Search Snippets
https://github.com/syxu828/Crosslingula-KG-Matching
공식 코드Cross-lingual Knowledge Graph Alignment via Graph Matching Neural Network
https://github.com/taokz/FedR
공식 코드https://github.com/tedzhouhk/sedyt
공식 코드https://github.com/thongnt99/dyvo
공식 코드DyVo: Dynamic Vocabularies for Learned Sparse Retrieval with Entities
https://github.com/txsun1997/CoLAKE
공식 코드https://github.com/uhh-lt/dblplink
공식 코드DBLPLink: An Entity Linker for the DBLP Scholarly Knowledge Graph
https://github.com/wyy-code/TFP
공식 코드Gradient Flow of Energy: A General and Efficient Approach for Entity Alignment Decoding
https://github.com/yotam-happy/NEDforNoisyText
공식 코드https://github.com/zhenjia2017/exaqt
공식 코드https://github.com/zjukg/earl
공식 코드Entity-Agnostic Representation Learning for Parameter-Efficient Knowledge Graph Embedding
https://github.com/zjukg/morse
공식 코드Meta-Knowledge Transfer for Inductive Knowledge Graph Embedding
https://github.com/Aditya-Jha2002/Diabetes-Prediction
https://github.com/BinWang28/InductivE
Inductive Learning on Commonsense Knowledge Graph Completion
https://github.com/DataCanvasIO/DeepTables
https://github.com/ElementAI/HypE
https://github.com/HareeshBahuleyan/size-fit-net
A Deep Learning System for Predicting Size and Fit in Fashion E-Commerce
https://github.com/INK-USC/RE-Net
Know-Evolve: Deep Temporal Reasoning for Dynamic Knowledge Graphs
https://github.com/MIRALab-USTC/GraphAKD
Rethinking Graph Convolutional Networks in Knowledge Graph Completion
https://github.com/NeverInAsh/fit-recommendation
A Deep Learning System for Predicting Size and Fit in Fashion E-Commerce
https://github.com/QuantumAbyss/EntityEmbeddings
https://github.com/anonymous-dense-submission/DensE
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.