All publications
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2010
| , , , , , , , , and , Deep Learning on GPUs with Theano, 2010 |
| , , , , , , , , , , , , , , , and , Deep Self-Taught Learning for Handwritten Character Recognition, University of Montréal, number 1353, 2010 |
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| , Échantillonnage dynamique de champs markoviens, Université de Montréal, 2010 |
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| and , GPU Programming with Theano, {SHARCNET} Research Day, 2010 |
| , , , and , Image classification with complex cell neural networks, in: Computational and systems neuroscience (COSYNE), Salt Lake City, 2010 |
[DOI] [URL] |
| and , Important Gains from Supervised Fine-Tuning of Deep Architectures on Large Labeled Sets, NIPS'2010 Deep Learning and Unsupervised Feature Learning Workshop, 2010 |
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| and , Learning features from music audio with deep belief networks, in: Proceedings of the 11th {I}nternational {S}ociety for {M}usic {I}nformation {R}etrieval {C}onference ({ISMIR}), Utrecht, The Netherlands, pages 339--344, 2010 |
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| , and , Learning tags that vary within a song, in: In Proceedings of the 11th International Conference on Music Information Retrieval (ISMIR), pages 399--404, 2010 |
[URL] |
| , Non-negative matrix decomposition approaches to frequency domain analysis of music audio signals, Université de Montréal, 2010 |
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| , and , Scalable Genre and Tag Prediction with Spectral Covariance, in: Proceedings of the 11th {I}nternational {S}ociety for {M}usic {I}nformation {R}etrieval {C}onference ({ISMIR}), Utrecht, The Netherlands., pages 507--512, 2010 |
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| , Sequential Machine learning Approaches for Portfolio Management, Université de Montréal, 2010 |
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| , , , and , Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion (2010), in: Journal of Machine Learning Research, 11(3371--3408) |
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| , , , and , Tempered Markov Chain Monte Carlo for training of Restricted Boltzmann Machine, in: JMLR W\&CP: Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS 2010), Chia Laguna Resort, Sardinia, Italy, pages 145--152, 2010 |
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| , and , The Spike and Slab Restricted Boltzmann Machine, Deep Learning and Unsupervised Feature Learning Workshop, {NIPS}, 2010 |
[URL] |
, , , , , , , and , Theano: a CPU and GPU Math Expression Compiler, in: Proceedings of the Python for Scientific Computing Conference ({SciPy}), Austin, TX, 2010
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| , and , Tractable Multivariate Binary Density Estimation and the Restricted Boltzmann Forest (2010), in: Neural Computation, 22:9(2285--2307) |
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| , Understanding deep architectures and the effect of unsupervised pre-training, Université de Montréal, 2010 |
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| , and , Understanding Representations Learned in Deep Architectures, Université de Montréal/DIRO, number 1355, 2010 |
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| and , Understanding the difficulty of training deep feedforward neural networks, in: JMLR W\&CP: Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS 2010), Chia Laguna Resort, Sardinia, Italy, pages 249-256, 2010 |
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| , and , Unlearning for Better Mixing, Université de Montréal/DIRO, number 1349, 2010 |
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| , , , , and , Why Does Unsupervised Pre-training Help Deep Learning? (2010), in: Journal of Machine Learning Research, 11(625--660) |
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| , , and , Why Does Unsupervised Pre-training Help Deep Learning?, in: JMLR W\&CP: Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS 2010), Chia Laguna Resort, Sardinia, Italy, pages 201-208, 2010 |
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| , and , Word representations: A simple and general method for semi-supervised learning, in: Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics(ACL2010), Uppsala, Sweden, pages 384--394, Association for Computational Linguistics, 2010 |
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2009
| and , A Hybrid Pareto Mixture for Conditional Asymmetric Fat-Tailed Distribution (2009), in: IEEE Transactions on Neural Networks, 20:7(1087--1101) |
[DOI] |
| and , A Hybrid Pareto Model for Asymmetric Fat-Tailed Data: the univariate case (2009), in: Extremes, 12:1(53--76) |
[DOI] |
| 1-25 | 26-50 | 51-75 | 76-100 | 101-125 | 126-150 | 151-175 | 176-200 | 201-225 | 226-250 | 251-275 | 276-300 | 301-325 | 326-350 | 351-375 | 376-400 | 401-425 | 426-450 | 451-475 | 476-500 | 501-525 | 526-526 |
