Up-to-date list of publications
For a more complete and up-to-date list of publications, please see my
Google Scholar profile
Publications by type
Refereed journal papers
- G. Alain, Y. Bengio, L. Yao, J. Yosinski, E. Thibodeau-Laufer, S. Zhang, P. Vincent,
GSNs: generative stochastic networks, Information and Inference, March 2016. ( doi: 10.1093/imaiai/iaw003 )
- S.
Ebrahimi Kahou, X. Bouthillier, P. Lamblin, C. Gulcehre, V. Michalski,
K. Konda, S. Jean, P. Froumenty, Y. Dauphin, N. Boulanger-Lewandowski,
R. Chandias Ferrari, M. Mirza, D. Warde-Farley, A. Courville, P.
Vincent, R. Memisevic, C. Pal, Y. Bengio, EmoNets: Multimodal deep learning approaches for emotion recognition in video, Journal on Multimodal User Interfaces, August 2015.
- G. Mesnil, S. Rifai, A. Bordes, X. Glorot, Y. Bengio, P. Vincent, Unsupervised Learning of Semantics of Object Detections for Scene Categorization, Advances in Intelligent Systems and Computing: Pattern Recognition Applications and Methods, 318:209–324, 2014.
- Y. Bengio, A. Courville, P. Vincent, Unsupervised Feature Learning and Deep Learning: A Review and New Perspectives.
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), Special Issue, 2013.
- P. Vincent, A
Connection between Score
Matching and Denoising Autoencoders,
Neural Computation, 23(7):1661--1674, 2011.
(Preprint version available as Technical Report 1358,
Dept. IRO,
Université de Montréal, December 2010).
- Y. Bengio, O. Breuleux and P. Vincent, Quickly
Generating Representative
Samples from an RBM-Derived Process,
Neural Computation. 23(8): 2058-2073, 2011.
- C. Dugas, N. Chapados, R. Ducharme, X. Saint-Mleux and P. Vincent, A High-Order Feature Synthesis and
Selection Algorithm Applied to Insurance Risk Modelling, International Journal of Business
Intelligence and Data Mining, 6(3):237--258, 2011.
- P. Vincent, H. Larochelle, I. Lajoie, Y. Bengio and P.A.
Manzagol, Stacked
Denoising Autoencoders: Learning Useful Representations in a Deep
Network with a Local Denoising Criterion, Journal of Machine
Learning Research, 11:3371--3408, 2010.
- D. Erhan, Y.
Bengio, A. Courville, P.A. Manzagol, P. Vincent
and S. Bengio, Why Does Unsupervised Pre-training
Help Deep Learning?
Journal of Machine Learning
Research, 11:625--660, 2010.
- S. Sonnenburg, M.L. Braun, C.S. Ong, S. Bengio, L.
Bottou, G. Holmes, Y. LeCun, K.R. Müller, F. Pereira, C.E. Rasmussen,
G. Rätsch, B. Schölkopf, A. Smola, P. Vincent, J. Weston, R.C.
Williamson, The
Need for Open Source Software in Machine Learning, Journal of Machine
Learning Research, 8:2443--2466, 2007.
- Y. Bengio, O. Delalleau, N. Le Roux, J.-F. Paiement, P.
Vincent and M. Ouimet, Learning
Eigenfunctions Links Spectral Embedding and Kernel PCA, Neural Computation, 16(10):2197--2219, 2004.
- Yoshua Bengio, Réjean Ducharme, Pascal Vincent and
Christian Jauvin, A Neural
Probabilistic Language Model, Journal of Machine Learning Research,
3:1137-1155, 2003.
- Pascal Vincent and Yoshua Bengio, Kernel Matching
Pursuit, Machine Learning Journal, 48(1):165-187, 2002.
Refereed conference papers
- A Touati, PL Bacon, D Precup, P Vincent, Convergent Tree-Backup and Retrace with Function Approximation,
International Conference on Machine Learning (ICML 2018),
2018.
-
S. Honari, P. Molchanov, S. Tyree, P. Vincent, C. Pal, J. Kautz,
Improving Landmark Localization with Semi-Supervised Learning,
The IEEE Conference on Computer Vision and
Pattern Recognition (CVPR 2018), 2018.
-
F. Bordes, S. Honari, P. Vincent,
Learning to generate samples from noise through infusion training,
International Conference on Learning Representations (ICLR 2017) , 2017.
- S. Honari, J. Yosinski, P. Vincent, C. Pal, Learning Recombined Layers in Deep Convolutional Networks: Experiments on Facial Keypoint Localization, The IEEE Conference on Computer Vision and
Pattern Recognition (CVPR 2016), 2016.
- A. de Brébisson, P. Vincent, An Exploration of Softmax Alternatives Belonging to the Spherical Loss Family, International Conference on Learning Representations (ICLR 2016), 2016.
- P. Vincent, A. de Brébisson, X. Bouthillier, Efficient Exact Gradient Update for training Deep Networks with Very Large Sparse Targets,
Advances in Neural Information Processing Systems 28 (NIPS 2015), 2015. Oral presentation at NIPS 2015.
- A. de Brébisson, E. Simon, A. Auvolat, P. Vincent, Y. Bengio, Artificial Neural Networks Applied to Taxi Destination Prediction, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD) 2015,
winning entry of the Taxi Trajectory Prediction challenge
- Y. Bengio, L. Yao, G. Alain, P. Vincent, Generalized Denoising Auto-Encoders as Generative Models,
Advances in Neural Information Processing Systems 26 (NIPS 2013), 2013.
- S. Ebrahimi Kanou, C.J. Pal, X. Bouthillier, P. Froumenty, C.
Gülçehre, R. Memisevic, P. Vincent, A. Courville, Y. Bengio, R.
Chandias Ferrari, M. Mirza, S. Jean, P.L. Carrier, Y. Dauphin, N.
Boulanger-Lewandowski, A. Aggarwal, J. Zumer, P. Lamblin, J-P. Raymond,
G. Desjardins, R. Pascanu, D. Warde-Farley, A. Torabi, A. Sharma, E.
Bengio, K. Reddy Konda, Z. Wu, Combining modality specific deep neural networks for emotion recognition in video, Proceedings of the 15th ACM International Conference on Multimodal
Interaction (ICMI 2013), 2013.
Winning team of the "Grand Challenge on Emotion Recognition In The Wild" 2013
- H. Almousli, P. Vincent, Semi Supervised Autoencoders: Better Focusing Model Capacity during Feature Extraction, Proceeding of the 20th International Conference on Neural Information Processing (ICONIP 2013), 2013.
- N. Boulanger-Lewandowski, Y. Bengio, P. Vincent, High-dimensional Sequence Transduction, Proceedings of the 38th International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2013), 2013.
- N. Boulanger-Lewandowski, Y. Bengio, P. Vincent, Audio Chord Recognition with Recurrent Neural Networks, Proceedings of the 14th International Society for Music Information Retrieval Conference (ISMIR 2013), 2013.
- G. Mesnil, S. Rifai}, A. Bordes, X. Glorot, Y. Bengio, P. Vincent, Unsupervised and Transfer Learning under Uncertainty: from Object Detections to Scene Categorization, Proceedings of the 3 rd International Conference on Pattern Recognition Applications and Methods (ICPRAM 2013), 2013.
- N. Boulanger-Lewandowski, Y. Bengio, P. Vincent, Discriminative Non-negative Matrix Factorization for Multiple Pitch Estimation,
Proceedings of the 13 th International Society for Music Information Retrieval Conference (ISMIR 2012), 2012.
- S. Rifai, Y. Bengio, A. Courville, P. Vincent, M. Mirza, Disentangling Factors of Variation for Facial Expression Recognition, 12th European Conference on Computer Vision (ECCV 2012), 2012.
- N. Boulanger-Lewandowski, Y. Bengio, P. Vincent, Modeling Temporal Dependencies in High-Dimensional Sequences: Application to Polyphonic Music Generation and Transcription, Proceedings of the 29th International Conference on Machine Learning
(ICML 2012), 2012.
Best student-paper award at ICML 2012.
- S. Rifai, Y. Bengio, Y.N. Dauphin, P. Vincent, A Generative Process for Sampling Contractive Auto-Encoders, Proceedings of the 29th International Conference on Machine Learning (ICML 2012), 2012.
- S. Rifai, Y.N. Dauphin, P. Vincent, Y. Bengio, X. Muller, The Manifold Tangent Classifier,
In Advances in Neural Information Processing Systems 24 (NIPS2011), pages 2294--2302, 2011.
Presented as an oral at NIPS 2011, won an award (honorable mention) in the outstanding student paper category.
- S. Rifai, G. Mesnil, P. Vincent, X. Muller, Y. Bengio, Y.N. Dauphin, X. Glorot, Higher Order Contractive Auto-Encoder, Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD 2011), Lecture Notes in Computer Science, 6912/2011:645-660, Springer, 2011.
- S. Rifai, P. Vincent, X. Muller, X. Glorot, Y. Bengio, Contractive Auto-Encoders: Explicit
Invariance During Feature Extraction, Proceedings of the
28th International Conference on Machine Learning (ICML'2011), pages 833-840, ACM, 2011.
- D. Erhan, A.
Courville, Y. Bengio and P. Vincent,
Why Does Unsupervised Pre-training Help Deep Learning?,
In Proceedings of the Thirteenth
International Conference on Artificial
Intelligence and Statistics (AISTATS 2010), Chia Laguna Resort,
Sardinia, Italy, pages 201-208, 2010.
- G. Desjardins, A.
Courville, Y. Bengio, P. Vincent
and O. Delalleau
Tempered Markov Chain Monte Carlo for
Training of Restricted Boltzmann Machine,
In Proceedings of the Thirteenth
International Conference on Artificial
Intelligence and Statistics (AISTATS 2010), Chia Laguna Resort,
Sardinia, Italy, pages 145--152, 2010
- D. Erhan, P.A.
Manzagol, Y. Bengio, S. Bengio and P. Vincent,
The Difficulty of Training Deep
Architectures and the Effect of Unsupervised Pre-Training,
In Proceedings of the Twelfth
International Conference on Artificial
Intelligence and Statistics (AISTATS 2009), Clearwater
(Florida), USA,
pages 153--160, 2009.
- Hugo Larochelle, Dumitru
Erhan and Pascal Vincent,
Deep Learning using Robust
Interdependent Codes,
In Proceedings of the Twelfth
International Conference on Artificial
Intelligence and Statistics (AISTATS 2009), Clearwater
(Florida), USA,
pages 312--319, 2009.
- N. Chapados, C. Dugas, P. Vincent, R. Ducharme,
Scoring Models for Insurance Risk Sharing Pool Optimization,
ICDM'08
Workshop Proceedings of the 8th IEEE International Conference on Data
Mining (ICDM 2008), pages 97-105, IEEE Computer Society, 2008.
- P. Vincent, H. Larochelle, Y. Bengio, P.A. Manzagol,
Extracting
and Composing Robust Features with Denoising Autoencoders
In Proceedings of the
25th International Conference on Machine Learning (ICML'2008), 2008.
- Y. Bengio, H. Larochelle, P. Vincent,
Non-Local Manifold Parzen Windows
In Advances in Neural Information Processing Systems 18,
2006.
- Y. Bengio, N. Le Roux, P. Vincent, O. Delalleau, P.
Marcotte,
Convex Neural Networks
In Advances in Neural Information Processing Systems 18,
pages 123--130, MIT Press, 2006.
- Y. Bengio, J.F. Paiement, P. Vincent, O. Delalleau, N. Le
Roux and M. Ouimet,
Out-of-sample
extensions for LLE, Isomap, MDS, eigenmaps, and Spectral Clustering
In Advances in Neural Information Processing Systems 16, 2004.
- Pascal Vincent and Yoshua Bengio,
Manifold
Parzen Windows
In Advances in Neural Information Processing Systems 15,2003.
See also the following addendum:
Mathematical
derivation of LocalGaussian computation for Manifold Parzen, and errata,
Technical Report 1259, Département
d'Informatique et Recherche Opérationnelle,
Université de Montréal, 2005.
- Pascal Vincent and Yoshua Bengio,
K-Local
Hyperplane and Convex Distance Nearest Neighbor Algorithms,
In Advances in Neural Information Processing Systems 14,
2002.
- N. Chapados, Y. Bengio, P. Vincent, J. Ghosn, C. Dugas, I.
Takeuchi, L. Meng,
Estimating
Car Insurance Premia: a Case Study in High-Dimensional Data Inference,
In Advances in Neural Information Processing Systems 14,
2002.
- Yoshua Bengio, Réjean Ducharme and Pascal Vincent,
A Neural
Probabilistic Language Model,
In Advances in Neural Information Processing Systems 13,
2001.
- Léon Bottou, Patrick Haffner, Yann Le Cun, Paul Howard,
Pascal Vincent, Bill Riemers,
DjVu: Un Système de
Compression d'Images pour la Distribution Réticulaire de Documents
Numérisés,
(DjVu: An image compression system for distributing scanned document on
the Internet).
In Actes de la Conférence Internationale Francophone sur
l'Ecrit et le Document, Lyon, France, July 2000.
- Pascal Vincent and Yoshua Bengio,
A Neural
Support Vector Network Architecture with Adaptive Kernels,
In Proceedings of the International Joint Conference on
Neural Networks, Como, Italy, July 2000.
- Patrick Haffner, Yann Le Cun, Léon Bottou, Paul Howard,
Pascal Vincent, Bill Riemers,
Color Documents on
the Web with DjVu
In Proceedings of the International Conference on Image
Processing, vol 1, pp 239-243, Kobe, Japan, October 1999.
Book chapters
- Y. Bengio, O.
Delalleau, N. Le Roux, J.F. Paiement, P. Vincent
and M. Ouimet,
Spectral Dimensionality Reduction,
In Feature Extraction, Foundations
and Applications, Springer, 2006
- C. Dugas, N. Chapados, Y. Bengio, P. Vincent, G. Denoncourt
et C. Fournier.
Neural Networks
Applied to Automobile Insurance Ratemaking
In Intelligent and Other Computational Techniques in
Insurance: Theory and Applications,
edited by L. Jain and A.F. Shapiro, World Scientific, 2003.
Thesis (in French)
Other publications and presentations
- C. Dugas, Y. Bengio, N. Chapados, P. Vincent, G.
Denoncourt, and C. Fournier,
Statistical Learning Algorithms Applied to Automobile
Insurance Ratemaking
CAS Forum, 1(1):179-214, Winter 2003.
- Yoshua Bengio, Nicolas Chapados, Charles Dugas, Joumana
Ghosn, Ichiro Takeuchi, and Pascal Vincent,
High-dimensional data inference for automobile insurance premia
estimation.
Presented at the 2001 MITACS Annual Meeting,
Montreal, 2001.
Technical reports
- P. Vincent,
A
connection between Score Matching and Denoising Autoencoders
Technical Report 1358, Département
d'Informatique et Recherche Opérationnelle,
Université de Montréal, December 2010.
- O. Breuleux, Y.
Bengio and P. Vincent,
Unlearning for Better Mixing
Technical Report 1349, Département
d'Informatique et Recherche Opérationnelle,
Université de Montréal, November 2010.
- G. Desjardins, A.
Courville, Y. Bengio, P. Vincent and O. Delalleau,
Tempered Markov Chain Monte Carlo for
training of Restricted Boltzmann Machines,
Technical Report 1345,Département d'Informatique et de
Recherche Opérationnelle, Université de Montréal, 2009
- P. Vincent, H. Larochelle, Y. Bengio and P.A. Manzagol,
Extracting
and Composing Robust Features with Denoising Autoencoders,
Technical Report 1316, Département
d'Informatique et Recherche Opérationnelle,
Université de Montréal, February 2008.
- Y. Bengio, N. Le Roux, P. Vincent, O. Delalleau and P.
Marcotte,
Convex
Neural Networks,
Technical Report 1263, Département
d'Informatique et Recherche Opérationnelle,
Université de Montréal, 2005.
- Pascal Vincent,
Mathematical
derivation of LocalGaussian computation for Manifold Parzen, and errata,
Technical Report 1259, Département
d'Informatique et Recherche Opérationnelle,
Université de Montréal, 2005.
- Y. Bengio, J-F. Paiement, and P. Vincent,
Out-of-Sample
Extensions for LLE, Isomap, MDS, Eigenmaps, and Spectral Clustering.
Technical Report 1238, Département d'informatique et recherche
opérationnelle, Université de Montréal, 2003.
- Y. Bengio, P. Vincent, J-F. Paiement,
O. Delalleau, M. Ouimet, and N. Le Roux,
Spectral
Clustering and Kernel PCA are Learning Eigenfunctions.
Technical Report 1239, Département d'informatique et recherche
opérationnelle, Université de Montréal, 2003.
- Y. Bengio, P. Vincent, and J.F. Paiement,
Learning
Eigenfunctions of Similarity: Linking Spectral Clustering and Kernel PCA.
Technical Report 1232, Département d'informatique et recherche
opérationnelle, Université de Montréal, 2003.
- P. Vincent and Y. Bengio,
Locally
Weighted Full Covariance Gaussian Density Estimation.
Technical Report 1240, Département d'informatique et recherche
opérationnelle, Université de Montréal, 2003.
- Nicolas Chapados, Yoshua Bengio, Pascal Vincent, Joumana
Ghosn, Charles Dugas, Ichiro Takeuchi, and Linyan Meng.
Estimating car insurance premia: a case study in high-dimensional data
inference.
Technical Report 1199, Département d'informatique et recherche
opérationnelle, Université de Montréal, 2001.
- Pascal Vincent and Yoshua Bengio,
K-Local
Hyperplane and Convex Distance Nearest Neighbor Algorithms.
Technical Report 1197, Département d'informatique et recherche
opérationnelle, Université de Montréal, 2001.
- Yoshua Bengio, Réjean Ducharme, and Pascal Vincent,
A
Neural Probabilistic Language Model.
Technical Report 1178, Département d'informatique et recherche
opérationnelle, Université de Montréal, 2000.
- Pascal Vincent and Yoshua Bengio,
Kernel
Matching Pursuit,
Technical Report 1179, Département
d'Informatique et Recherche Opérationnelle,
Université de Montréal, 2000.
- P. Haffner, L. Bottou, J. Bromley, C.J.C. Burges, T.
Cauble, Y. Le Cun, C. Nohl, C. Stanton, C. Stenard, P. Vincent,
The HCAR50 check amount reading system
Technical Report Lucent Technologies, Bell
Labs Innovation, 1996.
Past highlights
- Our paper The Manifold Tangent Classifier (S. Rifai, Y.N. Dauphin, P. Vincent, Y. Bengio, X. Muller, NIPS2011) was presented as an oral at NIPS 2011, and won an award (honorable mention) in the outstanding student paper category.
- Our lab won Phase 2
of the Unsupervised
and Transfer Learning Challenge (ranking 4th in Phase 1). Denoising autoencoders and contractive autoencoders were at
the heart of our winning strategy! See our paper describing the approach:
Unsupervised and Transfer Learning Challenge: a Deep Learning Approach.
G. Mesnil, Y. Dauphin, X. Glorot, S. Rifai, Y. Bengio, I. Goodfellow,
E. Lavoie, X. Muller, G. Desjardins, D. Warde-Farley, P. Vincent, A.
Courville and J. Bergstra. ICML 2011 Workshop on Unsupervisedans Transfer Learning, in JMLR: Workshop and Conference Proceedings 7 (2011) 1–15.