On generating characteristic-rich question sets for qa evaluation Y Su, H Sun, B Sadler, M Srivatsa, I Gür, Z Yan, X Yan Proceedings of the 2016 Conference on Empirical Methods in Natural Language …, 2016 | 131 | 2016 |
Dialsql: Dialogue based structured query generation I Gür, S Yavuz, Y Su, X Yan Proceedings of the 56th Annual Meeting of the Association for Computational …, 2018 | 80 | 2018 |
Improving semantic parsing via answer type inference S Yavuz, I Gür, Y Su, M Srivatsa, X Yan Proceedings of the 2016 Conference on Empirical Methods in Natural Language …, 2016 | 62 | 2016 |
User modeling for task oriented dialogues I Gür, D Hakkani-Tür, G Tür, P Shah 2018 IEEE Spoken Language Technology Workshop (SLT), 900-906, 2018 | 60* | 2018 |
A real-world webagent with planning, long context understanding, and program synthesis I Gur, H Furuta, A Huang, M Safdari, Y Matsuo, D Eck, A Faust arXiv preprint arXiv:2307.12856, 2023 | 59 | 2023 |
Environment generation for zero-shot compositional reinforcement learning I Gur, N Jaques, Y Miao, J Choi, M Tiwari, H Lee, A Faust Advances in Neural Information Processing Systems 34, 4157-4169, 2021 | 51* | 2021 |
Learning to Navigate the Web I Gur, U Rueckert, A Faust, D Hakkani-Tur International Conference on Learning Representations, 2019 | 48 | 2019 |
Less is more: Generating grounded navigation instructions from landmarks S Wang, C Montgomery, J Orbay, V Birodkar, A Faust, I Gur, N Jaques, ... Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2022 | 42* | 2022 |
Global relation embedding for relation extraction Y Su, H Liu, S Yavuz, I Gur, H Sun, X Yan arXiv preprint arXiv:1704.05958, 2017 | 41 | 2017 |
Understanding html with large language models I Gur, O Nachum, Y Miao, M Safdari, A Huang, A Chowdhery, S Narang, ... arXiv preprint arXiv:2210.03945, 2022 | 40 | 2022 |
Multimodal web navigation with instruction-finetuned foundation models H Furuta, O Nachum, KH Lee, Y Matsuo, SS Gu, I Gur arXiv preprint arXiv:2305.11854, 2023 | 34* | 2023 |
What it takes to achieve 100% condition accuracy on WikiSQL S Yavuz, I Gür, Y Su, X Yan Proceedings of the 2018 Conference on Empirical Methods in Natural Language …, 2018 | 33 | 2018 |
Accurate supervised and semi-supervised machine reading for long documents I Gur, D Hewlett, L Jones, A Lacoste Proceedings of the 2017 Conference on Empirical Methods in Natural Language …, 2017 | 28* | 2017 |
Assessing post-disaster damage from satellite imagery using semi-supervised learning techniques J Lee, JZ Xu, K Sohn, W Lu, D Berthelot, I Gur, P Khaitan, K Koupparis, ... arXiv preprint arXiv:2011.14004, 2020 | 23 | 2020 |
Modeling individual and group evacuation decisions during wildfires C Nguyen, KJ Schlesinger, F Han, I Gür, JM Carlson Fire technology 55, 517-545, 2019 | 18 | 2019 |
Beyond human data: Scaling self-training for problem-solving with language models A Singh, JD Co-Reyes, R Agarwal, A Anand, P Patil, PJ Liu, J Harrison, ... arXiv preprint arXiv:2312.06585, 2023 | 17 | 2023 |
Recovering question answering errors via query revision S Yavuz, I Gür, Y Su, X Yan Proceedings of the 2017 Conference on Empirical Methods in Natural Language …, 2017 | 9 | 2017 |
Small-scale proxies for large-scale transformer training instabilities M Wortsman, PJ Liu, L Xiao, K Everett, A Alemi, B Adlam, JD Co-Reyes, ... arXiv preprint arXiv:2309.14322, 2023 | 8 | 2023 |
Scaling forecasting algorithms using clustered modeling I Gür, M Güvercin, H Ferhatosmanoglu The VLDB Journal 24, 51-65, 2015 | 7 | 2015 |
Fast inference and transfer of compositional task structures for few-shot task generalization S Sohn, H Woo, J Choi, L Qiang, I Gur, A Faust, H Lee Uncertainty in Artificial Intelligence, 1857-1865, 2022 | 6 | 2022 |