Theano: A Python framework for fast computation of mathematical expressions R Al-Rfou, G Alain, A Almahairi, C Angermueller, D Bahdanau, N Ballas, ... arXiv, arXiv: 1605.02688, 2016 | 755* | 2016 |

Emonets: Multimodal deep learning approaches for emotion recognition in video SE Kahou, X Bouthillier, P Lamblin, C Gulcehre, V Michalski, K Konda, ... Journal on Multimodal User Interfaces 10 (2), 99-111, 2016 | 312 | 2016 |

Combining modality specific deep neural networks for emotion recognition in video SE Kahou, C Pal, X Bouthillier, P Froumenty, Ç Gülçehre, R Memisevic, ... Proceedings of the 15th ACM on International conference on multimodal …, 2013 | 290 | 2013 |

Dropout as data augmentation X Bouthillier, K Konda, P Vincent, R Memisevic arXiv preprint arXiv:1506.08700, 2015 | 60 | 2015 |

Efficient exact gradient update for training deep networks with very large sparse targets P Vincent, A De Brébisson, X Bouthillier Advances in Neural Information Processing Systems 28, 1108-1116, 2015 | 42 | 2015 |

Fast approximate natural gradient descent in a kronecker factored eigenbasis T George, C Laurent, X Bouthillier, N Ballas, P Vincent Advances in Neural Information Processing Systems 31, 9550-9560, 2018 | 35 | 2018 |

Unreproducible research is reproducible X Bouthillier, C Laurent, P Vincent International Conference on Machine Learning, 725-734, 2019 | 20 | 2019 |

Survey of machine-learning experimental methods at NeurIPS2019 and ICLR2020 X Bouthillier, G Varoquaux Inria Saclay Ile de France, 2020 | 7 | 2020 |

Orıon: Asynchronous Distributed Hyperparameter Optimization X Bouthillier, C Tsirigotis, F Corneau-Tremblay, P Delaunay, ... | 7* | 2019 |

An evaluation of fisher approximations beyond kronecker factorization C Laurent, T George, X Bouthillier, N Ballas, P Vincent | 1 | 2018 |

Improved Deep Learning Workflows Through Hyperparameter Optimization with Oríon X Bouthillier | | |

Improving Reproducibility of Benchmarks X Bouthillier | | |