| Natural language processing (almost) from scratch R Collobert, J Weston, L Bottou, M Karlen, K Kavukcuoglu, P Kuksa Journal of machine learning research 12, 2493− 2537, 2011 | 10157 | 2011 |
| A unified architecture for natural language processing: Deep neural networks with multitask learning R Collobert, J Weston Proceedings of the 25th international conference on Machine learning, 160-167, 2008 | 7587 | 2008 |
| Curriculum learning Y Bengio, J Louradour, R Collobert, J Weston Proceedings of the 26th annual international conference on machine learning …, 2009 | 6161 | 2009 |
| Torch7: A matlab-like environment for machine learning R Collobert, K Kavukcuoglu, C Farabet BigLearn, NIPS workshop, 2011 | 1979 | 2011 |
| wav2vec: Unsupervised pre-training for speech recognition S Schneider, A Baevski, R Collobert, M Auli arXiv preprint arXiv:1904.05862, 2019 | 1449 | 2019 |
| Deep learning via semi-supervised embedding J Weston, F Ratle, R Collobert Proceedings of the 25th international conference on Machine learning, 1168-1175, 2008 | 1309 | 2008 |
| SVMTorch: Support vector machines for large-scale regression problems R Collobert, S Bengio Journal of machine learning research 1 (Feb), 143-160, 2001 | 1237 | 2001 |
| Learning structured embeddings of knowledge bases A Bordes, J Weston, R Collobert, Y Bengio Proceedings of the AAAI conference on artificial intelligence 25 (1), 301-306, 2011 | 1111 | 2011 |
| Learning to refine object segments PO Pinheiro, TY Lin, R Collobert, P Dollár ECCV, 2016 | 1106 | 2016 |
| Learning to segment object candidates PO O Pinheiro, R Collobert, P Dollár Advances in neural information processing systems 28, 2015 | 1049 | 2015 |
| Recurrent convolutional neural networks for scene labeling P Pinheiro, R Collobert International conference on machine learning, 82-90, 2014 | 1037 | 2014 |
| From image-level to pixel-level labeling with convolutional networks PO Pinheiro, R Collobert Proceedings of the IEEE conference on computer vision and pattern …, 2015 | 841 | 2015 |
| Unsupervised cross-lingual representation learning for speech recognition A Conneau, A Baevski, R Collobert, A Mohamed, M Auli arXiv preprint arXiv:2006.13979, 2020 | 706 | 2020 |
| Torch: a modular machine learning software library R Collobert, S Bengio, J Mariéthoz Idiap, 2002 | 698 | 2002 |
| Large scale transductive SVMS. R Collobert, F Sinz, J Weston, L Bottou, T Joachims Journal of Machine Learning Research 7 (8), 2006 | 631 | 2006 |
| Libri-light: A benchmark for asr with limited or no supervision J Kahn, M Riviere, W Zheng, E Kharitonov, Q Xu, PE Mazaré, J Karadayi, ... ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and …, 2020 | 572 | 2020 |
| A parallel mixture of SVMs for very large scale problems R Collobert, S Bengio, Y Bengio Advances in Neural Information Processing Systems 14, 2001 | 545 | 2001 |
| Video (language) modeling: a baseline for generative models of natural videos MA Ranzato, A Szlam, J Bruna, M Mathieu, R Collobert, S Chopra arXiv preprint arXiv:1412.6604, 2014 | 524 | 2014 |
| Deep learning from temporal coherence in video H Mobahi, R Collobert, J Weston Proceedings of the 26th annual international conference on machine learning …, 2009 | 443 | 2009 |
| Trading convexity for scalability R Collobert, F Sinz, J Weston, L Bottou Proceedings of the 23rd international conference on Machine learning, 201-208, 2006 | 441 | 2006 |
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