| Neural text generation with unlikelihood training S Welleck, I Kulikov, S Roller, E Dinan, K Cho, J Weston arXiv preprint arXiv:1908.04319, 2019 | 369 | 2019 |
| Don't say that! making inconsistent dialogue unlikely with unlikelihood training M Li, S Roller, I Kulikov, S Welleck, YL Boureau, K Cho, J Weston arXiv preprint arXiv:1911.03860, 2019 | 146 | 2019 |
| Importance of search and evaluation strategies in neural dialogue modeling I Kulikov, AH Miller, K Cho, J Weston arXiv preprint arXiv:1811.00907, 2018 | 105* | 2018 |
| RETURNN: The RWTH extensible training framework for universal recurrent neural networks P Doetsch, A Zeyer, P Voigtlaender, I Kulikov, R Schlüter, H Ney 2017 IEEE International Conference on Acoustics, Speech and Signal …, 2017 | 77 | 2017 |
| The RWTH/UPB/FORTH system combination for the 4th CHiME challenge evaluation T Menne Deutsche Nationalbibliothek, 2016 | 49 | 2016 |
| Consistency of a recurrent language model with respect to incomplete decoding S Welleck, I Kulikov, J Kim, RY Pang, K Cho arXiv preprint arXiv:2002.02492, 2020 | 44 | 2020 |
| Optimization of memory operations in generalized search trees of PostgreSQL A Borodin, S Mirvoda, I Kulikov, S Porshnev Beyond Databases, Architectures and Structures. Towards Efficient Solutions …, 2017 | 43 | 2017 |
| Vehicle communication strategies for simulated highway driving C Resnick, I Kulikov, K Cho, J Weston arXiv preprint arXiv:1804.07178, 2018 | 11* | 2018 |
| UnitY: Two-pass Direct Speech-to-speech Translation with Discrete Units H Inaguma, S Popuri, I Kulikov, PJ Chen, C Wang, YA Chung, Y Tang, ... arXiv preprint arXiv:2212.08055, 2022 | 9 | 2022 |
| Simple and effective unsupervised speech translation C Wang, H Inaguma, PJ Chen, I Kulikov, Y Tang, WN Hsu, M Auli, J Pino arXiv preprint arXiv:2210.10191, 2022 | 8 | 2022 |
| Uncertainty Determines the Adequacy of the Mode and the Tractability of Decoding in Sequence-to-Sequence Models F Stahlberg, I Kulikov, S Kumar arXiv preprint arXiv:2204.00471, 2022 | 4 | 2022 |
| Characterizing and addressing the issue of oversmoothing in neural autoregressive sequence modeling I Kulikov, M Eremeev, K Cho arXiv preprint arXiv:2112.08914, 2021 | 4 | 2021 |
| Multi-turn beam search for neural dialogue modeling I Kulikov, J Lee, K Cho arXiv preprint arXiv:1906.00141, 2019 | 4 | 2019 |
| Improving Speech-to-Speech Translation Through Unlabeled Text XP Nguyen, S Popuri, C Wang, Y Tang, I Kulikov, H Gong ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and …, 2023 | 3 | 2023 |
| Mode recovery in neural autoregressive sequence modeling I Kulikov, S Welleck, K Cho arXiv preprint arXiv:2106.05459, 2021 | 2 | 2021 |
| Faster sequence training A Zeyer, I Kulikov, R Schlüter, H Ney 2017 IEEE International Conference on Acoustics, Speech and Signal …, 2017 | 2 | 2017 |
| SeamlessM4T-Massively Multilingual & Multimodal Machine Translation L Barrault, YA Chung, MC Meglioli, D Dale, N Dong, PA Duquenne, ... arXiv preprint arXiv:2308.11596, 2023 | 1 | 2023 |
| Electronic device for obtaining sentence corresponding to context information and operating method thereof Y Choi, KIM Jaedeok, I Kulikov, S Welleck, P Yuanzhe, CHO KyungHyun US Patent 11,669,694, 2023 | 1 | 2023 |
| Named Entity Detection and Injection for Direct Speech Translation M Gaido, Y Tang, I Kulikov, R Huang, H Gong, H Inaguma ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and …, 2023 | | 2023 |
| Characterizing and Resolving Degeneracies in Neural Autoregressive Text Generation I Kulikov New York University, 2022 | | 2022 |
Kyunghyun ChoNew York University, GenentechVerified email at nyu.edu
