Gemini: a family of highly capable multimodal models G Team, R Anil, S Borgeaud, JB Alayrac, J Yu, R Soricut, J Schalkwyk, ... arXiv preprint arXiv:2312.11805, 2023 | 2209 | 2023 |
Deep learning for entity matching: A design space exploration S Mudgal, H Li, T Rekatsinas, AH Doan, Y Park, G Krishnan, R Deep, ... Proceedings of the 2018 international conference on management of data, 19-34, 2018 | 714 | 2018 |
Generalizing word embeddings using bag of subwords J Zhao, S Mudgal, Y Liang arXiv preprint arXiv:1809.04259, 2018 | 59 | 2018 |
Controlled decoding from language models S Mudgal, J Lee, H Ganapathy, YG Li, T Wang, Y Huang, Z Chen, ... arXiv preprint arXiv:2310.17022, 2023 | 46 | 2023 |
Human-in-the-loop challenges for entity matching: A midterm report AH Doan, A Ardalan, J Ballard, S Das, Y Govind, P Konda, H Li, S Mudgal, ... Proceedings of the 2nd workshop on human-in-the-loop data analytics, 1-6, 2017 | 35 | 2017 |
Entity matching meets data science: A progress report from the magellan project Y Govind, P Konda, P Suganthan GC, P Martinkus, P Nagarajan, H Li, ... Proceedings of the 2019 International Conference on Management of Data, 389-403, 2019 | 27 | 2019 |
Toward a system building agenda for data integration AH Doan, A Ardalan, JR Ballard, S Das, Y Govind, P Konda, H Li, ... arXiv preprint arXiv:1710.00027, 2017 | 20 | 2017 |
A scalable framework for learning from implicit user feedback to improve natural language understanding in large-scale conversational ai systems S Park, H Li, A Patel, S Mudgal, S Lee, YB Kim, S Matsoukas, R Sarikaya arXiv preprint arXiv:2010.12251, 2020 | 17 | 2020 |
Continuous learning for large-scale personalized domain classification H Li, J Lee, S Mudgal, R Sarikaya, YB Kim arXiv preprint arXiv:1905.00921, 2019 | 5 | 2019 |
Deep learning for semantic matching: A survey H Li, Y Govind, S Mudgal, T Rekatsinas, AH Doan Journal of Computer Science and Cybernetics 37 (4), 365-402, 2021 | 4 | 2021 |
Using machine translation to localize task oriented nlg output S Roy, C Brunk, KY Kim, J Zhao, M Freitag, M Kale, G Bansal, S Mudgal, ... arXiv preprint arXiv:2107.04512, 2021 | 1 | 2021 |
Blockwise controlled decoding of natural language (nl) based output generated using a large language model (llm) to reduce latency in rendering thereof S Mudgal, A Beirami, J Chen, A Beutel, H Ganapathy, Y Li, T Wang, ... US Patent App. 18/225,990, 2024 | | 2024 |
Streaming of natural language (nl) based output generated using a large language model (llm) to reduce latency in rendering thereof M Baeuml, Y Huang, W Jia, C Lan, Y Xu, AHN Junwhan, A Bailey, ... US Patent App. 18/136,634, 2024 | | 2024 |
Is Your Web Server Suffering from Undue Stress due to Duplicate Requests? FA Arshad, AK Maji, S Mudgal, S Bagchi 11th International Conference on Autonomic Computing (ICAC 14), 105-111, 2014 | | 2014 |