| 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 | 6992 | 2023 |
| Gemini 2.5: Pushing the frontier with advanced reasoning, multimodality, long context, and next generation agentic capabilities G Comanici, E Bieber, M Schaekermann, I Pasupat, N Sachdeva, I Dhillon, ... arXiv preprint arXiv:2507.06261, 2025 | 1337 | 2025 |
| 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 | 852 | 2018 |
| Controlled decoding from language models S Mudgal, J Lee, H Ganapathy, YG Li, T Wang, Y Huang, Z Chen, ... ICML'24: Proceedings of the 41st International Conference on Machine …, 2024 | 148 | 2024 |
| Generalizing word embeddings using bag of subwords J Zhao, S Mudgal, Y Liang Proceedings of the 2018 Conference on Empirical Methods in Natural Language …, 2018 | 65 | 2018 |
| 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 | 29 | 2019 |
| 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 Proceedings of the 2021 Conference on Empirical Methods in Natural Language …, 2021 | 27 | 2021 |
| 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 | 17 | 2017 |
| Continuous learning for large-scale personalized domain classification H Li, J Lee, S Mudgal, R Sarikaya, YB Kim Proceedings of the 2019 Conference of the North American Chapter of the …, 2019 | 6 | 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 | 5 | 2021 |
| 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, J AHN, A Bailey, L Schelin, ... US Patent App. 18/136,634, 2024 | 1 | 2024 |
| 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 |
| 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 |
