Affiliation:
1. AT&T Interactive Research
2. AT&T Labs-Research
Subject
General Earth and Planetary Sciences,Water Science and Technology,Geography, Planning and Development
Cited by
42 articles.
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1. Efficient Mixture of Experts based on Large Language Models for Low-Resource Data Preprocessing;Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining;2024-08-24
2. Enriching Relations with Additional Attributes for ER;Proceedings of the VLDB Endowment;2024-07
3. Split incremental clustering algorithm of mixed data stream;Progress in Artificial Intelligence;2024-03
4. Splitting Tuples of Mismatched Entities;Proceedings of the ACM on Management of Data;2023-12-08
5. Making It Tractable to Catch Duplicates and Conflicts in Graphs;Proceedings of the ACM on Management of Data;2023-05-26