| Neural architectures for named entity recognition G Lample, M Ballesteros, S Subramanian, K Kawakami, C Dyer NAACL 2016, 2016 | 4412 | 2016 |
| Cross-lingual language model pretraining G Lample, A Conneau NIPS 2019, 2019 | 2303* | 2019 |
| Word translation without parallel data G Lample, A Conneau, MA Ranzato, L Denoyer, H Jégou ICLR 2018, 2018 | 1566* | 2018 |
| Unsupervised Machine Translation Using Monolingual Corpora Only G Lample, A Conneau, L Denoyer, MA Ranzato ICLR 2018, 2018 | 1118 | 2018 |
| XNLI: Evaluating cross-lingual sentence representations A Conneau, G Lample, R Rinott, A Williams, SR Bowman, H Schwenk, ... EMNLP 2018, 2018 | 898 | 2018 |
| What you can cram into a single vector: Probing sentence embeddings for linguistic properties A Conneau, G Kruszewski, G Lample, L Barrault, M Baroni ACL 2018, 2018 | 742 | 2018 |
| Phrase-Based & Neural Unsupervised Machine Translation G Lample, M Ott, A Conneau, L Denoyer, MA Ranzato EMNLP 2018, 2018 | 662 | 2018 |
| Playing FPS Games with Deep Reinforcement Learning. G Lample, DS Chaplot AAAI 2017, 2017 | 605 | 2017 |
| Fader Networks: Manipulating Images by Sliding Attributes G Lample, N Zeghidour, N Usunier, A Bordes, L Denoyer, MA Ranzato NIPS 2017, 2017 | 535 | 2017 |
| Llama: Open and efficient foundation language models H Touvron, T Lavril, G Izacard, X Martinet, MA Lachaux, T Lacroix, ... arXiv preprint arXiv:2302.13971, 2023 | 363 | 2023 |
| Deep learning for symbolic mathematics G Lample, F Charton ICLR 2020, 2019 | 319 | 2019 |
| Massively multilingual word embeddings W Ammar, G Mulcaire, Y Tsvetkov, G Lample, C Dyer, NA Smith arXiv preprint arXiv:1602.01925, 2016 | 313 | 2016 |
| Multiple-Attribute Text Rewriting G Lample, S Subramanian, E Smith, L Denoyer, YL Boureau ICLR 2019, 2018 | 275* | 2018 |
| The flores evaluation datasets for low-resource machine translation: Nepali-english and sinhala-english F Guzmán, PJ Chen, M Ott, J Pino, G Lample, P Koehn, V Chaudhary, ... arXiv preprint arXiv:1902.01382, 2019 | 215 | 2019 |
| Unsupervised translation of programming languages MA Lachaux, B Roziere, L Chanussot, G Lample arXiv preprint arXiv:2006.03511, 2020 | 195* | 2020 |
| Evaluation of word vector representations by subspace alignment Y Tsvetkov, M Faruqui, W Ling, G Lample, C Dyer EMNLP 2015, 2015 | 175 | 2015 |
| Large Memory Layers with Product Keys G Lample, A Sablayrolles, MA Ranzato, L Denoyer, H Jégou NIPS 2019, 2019 | 102 | 2019 |
| Augmenting self-attention with persistent memory S Sukhbaatar, E Grave, G Lample, H Jegou, A Joulin arXiv preprint arXiv:1907.01470, 2019 | 80 | 2019 |
| Polyglot neural language models: A case study in cross-lingual phonetic representation learning Y Tsvetkov, S Sitaram, M Faruqui, G Lample, P Littell, D Mortensen, ... NAACL 2016, 2016 | 62 | 2016 |
| Dobf: A deobfuscation pre-training objective for programming languages B Roziere, MA Lachaux, M Szafraniec, G Lample arXiv preprint arXiv:2102.07492, 2021 | 54* | 2021 |
Sandeep SubramanianNVIDIAVerified email at umontreal.ca
