| Distributed representations of words and phrases and their compositionality T Mikolov, I Sutskever, K Chen, GS Corrado, J Dean Neural information processing systems, 2013 | 40630 | 2013 |
| Efficient estimation of word representations in vector space T Mikolov, K Chen, G Corrado, J Dean arXiv preprint arXiv:1301.3781, 2013 | 37472 | 2013 |
| Distributed representations of sentences and documents Q Le, T Mikolov International conference on machine learning, 1188-1196, 2014 | 11525 | 2014 |
| Enriching word vectors with subword information P Bojanowski, E Grave, A Joulin, T Mikolov Transactions of the association for computational linguistics 5, 135-146, 2017 | 10906 | 2017 |
| Recurrent neural network based language model. T Mikolov, M Karafiát, L Burget, J Cernocký, S Khudanpur Interspeech 2 (3), 1045-1048, 2010 | 7380 | 2010 |
| On the difficulty of training recurrent neural networks R Pascanu, T Mikolov, Y Bengio International conference on machine learning, 1310-1318, 2013 | 6452 | 2013 |
| Bag of tricks for efficient text classification A Joulin, E Grave, P Bojanowski, T Mikolov arXiv preprint arXiv:1607.01759, 2016 | 5321 | 2016 |
| Linguistic regularities in continuous space word representations T Mikolov, W Yih, G Zweig Proceedings of the 2013 conference of the north american chapter of the …, 2013 | 4794 | 2013 |
| Devise: A deep visual-semantic embedding model A Frome, GS Corrado, J Shlens, S Bengio, J Dean, MA Ranzato, ... Advances in neural information processing systems 26, 2013 | 2957 | 2013 |
| Exploiting similarities among languages for machine translation T Mikolov, QV Le, I Sutskever arXiv preprint arXiv:1309.4168, 2013 | 1746 | 2013 |
| Learning word vectors for 157 languages E Grave, P Bojanowski, P Gupta, A Joulin, T Mikolov arXiv preprint arXiv:1802.06893, 2018 | 1580 | 2018 |
| Extensions of recurrent neural network language model T Mikolov, S Kombrink, L Burget, J Černocký, S Khudanpur 2011 IEEE international conference on acoustics, speech and signal …, 2011 | 1573 | 2011 |
| Advances in pre-training distributed word representations T Mikolov, E Grave, P Bojanowski, C Puhrsch, A Joulin arXiv preprint arXiv:1712.09405, 2017 | 1528 | 2017 |
| Fasttext. zip: Compressing text classification models A Joulin, E Grave, P Bojanowski, M Douze, H Jégou, T Mikolov arXiv preprint arXiv:1612.03651, 2016 | 1304 | 2016 |
| Towards ai-complete question answering: A set of prerequisite toy tasks J Weston, A Bordes, S Chopra, AM Rush, B Van Merriënboer, A Joulin, ... arXiv preprint arXiv:1502.05698, 2015 | 1162 | 2015 |
| One billion word benchmark for measuring progress in statistical language modeling C Chelba, T Mikolov, M Schuster, Q Ge, T Brants, P Koehn, T Robinson arXiv preprint arXiv:1312.3005, 2013 | 1149 | 2013 |
| Zero-shot learning by convex combination of semantic embeddings M Norouzi, T Mikolov, S Bengio, Y Singer, J Shlens, A Frome, GS Corrado, ... arXiv preprint arXiv:1312.5650, 2013 | 1017 | 2013 |
| Statistical Language Models Based on Neural Networks T Mikolov Ph. D. thesis, Brno University of Technology, 2012 | 876 | 2012 |
| Understanding the exploding gradient problem R Pascanu, T Mikolov, Y Bengio CoRR, abs/1211.5063 2 (417), 1, 2012 | 720 | 2012 |
| Context dependent recurrent neural network language model T Mikolov, G Zweig 2012 IEEE Spoken Language Technology Workshop (SLT), 234-239, 2012 | 717 | 2012 |
Jeff DeanGoogle Chief Scientist, Google Research and Google DeepMindVerified email at google.com
