https://github.com/philine-bommer/dl4s2s
공식 코드Deep Learning Meets Teleconnections: Improving S2S Predictions for European Winter Weather
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Entity resolution (also known as entity matching, record linkage, or duplicate detection) is the task of finding records that refer to the same real-world entity across different data sources (e.g., data files, books, websites, and databases). (Source: Wikipedia)
Blocking is a crucial step in any entity resolution pipeline because a pair-wise comparison of all records across two data sources is infeasible. Blocking applies a computationally cheap method to generate a smaller set of candidate record pairs reducing the workload of the matcher. During matching a more expensive pair-wise matcher generates a final set of matching record pairs.
Survey on blocking:
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
Deep Learning Meets Teleconnections: Improving S2S Predictions for European Winter Weather
Queue management for slo-oriented large language model serving
Concentration inequality for U-statistics of order two for uniformly ergodic Markov chains
DynamicViT: Efficient Vision Transformers with Dynamic Token Sparsification
Linking Cryptoasset Attribution Tags to Knowledge Graph Entities: An LLM-based Approach
Multi-Channel Deep Networks for Block-Based Image Compressive Sensing
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WearMask: Fast In-browser Face Mask Detection with Serverless Edge Computing for COVID-19
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SC-Block: Supervised Contrastive Blocking within Entity Resolution Pipelines
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출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.