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Pascal Vincent Artificial Intelligence (expert systems - machine learning - robotics)

Pascal Vincent’s current research interests in the statistical machine learning field include unsupervised feature learning, manifold modeling, alternative parameter estimation techniques for energy based models, semi-supervised learning and learning of deep neural-network architectures. His current main focus is on fundamental principles and techniques for extracting meaningful high level distributed representations from raw high dimensional sensory inputs. His work on regularizing auto-encoders (denoising and contractive variants) for unsupervised feature and deep network learning were at the heart of the strategy that won the 2011 Unsupervised and Transfer Learning Challenge.

Relevant Publications

S. Rifai et al, "The Manifold Tangent Classifier," Advances in Neural Information Processing Systems, vol. 24 (NIPS2011), pp. 2294-2302, 2011.

G. Mesnil et al, "Unsupervised and Transfer Learning Challenge: a Deep Learning Approach," ICML 2011 Workshop on Unsupervised and Transfer Learning, in JMLR: Workshop and Conference Proceedings, vol. 7, pp. 1–15, 2011.



Associate Fellow Learning in Machines & Brains


Université de MontréalDepartment of Computer Science and Operations Research


BEng Ecole Supérieure d'Ingénieurs en Electrotechnique et Electronique



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