| Google’s neural machine translation system: Bridging the gap between human and machine translation Y Wu arXiv preprint arXiv:1609.08144, 2016 | 8937 | 2016 |
| Universal transformers M Dehghani, S Gouws, O Vinyals, J Uszkoreit, Ł Kaiser International Conference on Learning Representations (ICLR), 2019 | 959 | 2019 |
| Tensor2tensor for neural machine translation A Vaswani, S Bengio, E Brevdo, F Chollet, AN Gomez, S Gouws, L Jones, ... arXiv preprint arXiv:1803.07416, 2018 | 635 | 2018 |
| Bilbowa: Fast bilingual distributed representations without word alignments S Gouws, Y Bengio, G Corrado International Conference on Machine Learning (ICML), 2015 | 452 | 2015 |
| Generating high-quality and informative conversation responses with sequence-to-sequence models Y Shao, S Gouws, D Britz, A Goldie, B Strope, R Kurzweil Proceedings of the 2017 Conference on Empirical Methods in Natural Language …, 2017 | 250 | 2017 |
| Simple task-specific bilingual word embeddings S Gouws, A Søgaard Proceedings of the 2015 Conference of the North American Chapter of the …, 2015 | 174 | 2015 |
| XGAN: Unsupervised Image-to-Image Translation for Many-to-Many Mappings A Royer, K Bousmalis, S Gouws, F Bertsch, I Moressi, F Cole, K Murphy arXiv preprint arXiv:1711.05139, 2017 | 158 | 2017 |
| Contextual bearing on linguistic variation in social media S Gouws, D Metzler, C Cai, E Hovy Proceedings of the workshop on language in social media (LSM 2011), 20-29, 2011 | 147 | 2011 |
| Google’s neural machine translation system: Bridging the gap between human and machine translation. CoRR abs/1609.08144 (2016) Y Wu, M Schuster, Z Chen, QV Le, M Norouzi, W Macherey, M Krikun, ... arXiv preprint arXiv:1609.08144, 2016 | 103 | 2016 |
| Fidelity-weighted learning M Dehghani, A Mehrjou, S Gouws, J Kamps, B Schölkopf arXiv preprint arXiv:1711.02799, 2017 | 79 | 2017 |
| & Dean, J.(2016). Google's neural machine translation system: Bridging the gap between human and machine translation Y Wu, M Schuster, Z Chen, QV Le, M Norouzi, W Macherey, M Krikun, ... arXiv preprint arXiv:1609.08144, 0 | 79 | |
| Google’s neural machine translation system: Bridging the gap between human and machine translation. CoRR abs/1609.08144 Y Wu, M Schuster, Z Chen, QV Le, M Norouzi, W Macherey, M Krikun, ... | 77 | 2016 |
| Generating long and diverse responses with neural conversation models L Shao, S Gouws, D Britz, A Goldie, B Strope, R Kurzweil arXiv preprint arXiv:1701.03185, 2017 | 76 | 2017 |
| Unsupervised mining of lexical variants from noisy text S Gouws, D Hovy, D Metzler Proceedings of the First workshop on Unsupervised Learning in NLP, 82-90, 2011 | 70 | 2011 |
| Measuring conceptual similarity by spreading activation over Wikipedia’s hyperlink structure S Gouws, GJ van Rooyen, HA Engelbrecht Proceedings of the 2nd Workshop on The People’s Web Meets NLP …, 2010 | 41 | 2010 |
| Universal transformers M Dehghani, S Gouws, O Vinyals, JD Uszkoreit, LM Kaiser US Patent 10,740,433, 2020 | 10 | 2020 |
| Semantically-consistent image style transfer S Gouws, F Bertsch, K Bousmalis, A Royer, KP Murphy US Patent 11,380,034, 2022 | 9 | 2022 |
| Learning structural correspondences across different linguistic domains with synchronous neural language models S Gouws, GJ Van Rooyen, MIH Medialab, Y Bengio Proc. of the xLite Workshop on Cross-Lingual Technologies, NIPS, 2012 | 9 | 2012 |
| Deep unsupervised feature learning for natural language processing S Gouws Proceedings of the NAACL HLT 2012 Student Research Workshop, 48-53, 2012 | 7 | 2012 |
| Universal transformers M Dehghani, S Gouws, O Vinyals, JD Uszkoreit, LM Kaiser US Patent 11,860,969, 2024 | 3 | 2024 |
Łukasz KaiserOpenAI & CNRSVerified email at openai.com
