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- Rami Al-Rfou', Guillaume Alain, +109 authors Ying Zhang
- ArXiv
- 2016

Theano is a Python library that allows to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. Since its introduction, it has been one of the most used CPU and GPU mathematical compilers - especially in the machine learning community - and has shown steady performance improvements. Theano is being actively… (More)

We introduce a novel training principle for probabilistic models that is an alternative to maximum likelihood. The proposed Generative Stochastic Networks (GSN) framework is based on learning the transition operator of a Markov chain whose stationary distribution estimates the data distribution. The transition distribution of the Markov chain is conditional… (More)

- Yoshua Bengio, Li Yao, Guillaume Alain, Pascal Vincent
- NIPS
- 2013

Recent work has shown how denoising and contractive autoencoders implicitly capture the structure of the data-generating density, in the case where the corruption noise is Gaussian, the reconstruction error is the squared error, and the data is continuous-valued. This has led to various proposals for sampling from this implicitly learned density function,… (More)

- Guillaume Alain, Yoshua Bengio
- Journal of Machine Learning Research
- 2014

What do auto-encoders learn about the underlying data generating distribution? Recent work suggests that some auto-encoder variants do a good job of capturing the local manifold structure of data. This paper clarifies some of these previous observations by showing that minimizing a particular form of regularized reconstruction error yields a reconstruction… (More)

- Tapani Raiko, Mathias Berglund, Guillaume Alain, Laurent Dinh
- ArXiv
- 2014

Stochastic binary hidden units in a multi-layer perceptron (MLP) network give at least three potential benefits when compared to deterministic MLP networks. (1) They allow to learn one-to-many type of mappings. (2) They can be used in struc-tured prediction problems, where modeling the internal structure of the output is important. (3) Stochasticity has… (More)

Humans are able to accelerate their learning by selecting training materials that are the most informative and at the appropriate level of difficulty. We propose a framework for distributing deep learning in which one set of workers search for the most informative examples in parallel while a single worker updates the model on examples selected by… (More)

- Guillaume Alain, Yoshua Bengio
- ArXiv
- 2016

Neural network models have a reputation for being black boxes. We propose a new method to understand better the roles and dynamics of the intermediate layers. This has direct consequences on the design of such models and it enables the expert to be able to justify certain heuristics (such as the auxiliary heads in the Inception model). Our method uses… (More)

- Guillaume Alain, Yoshua Bengio, Salah Rifai
- ArXiv
- 2012

What do auto-encoders learn about the underlying data generating distribution? Recent work suggests that some auto-encoder variants do a good job of capturing the local manifold structure of the unknown data generating density. This paper clarifies some of these previous intuitive observations by showing that minimizing a particular form of regularized… (More)

- G Alain, J Tousignant, E Rozenfarb
- International journal of dermatology
- 1993

- Sohrab P. Shah, K-John Cheung, +5 authors Kevin P. Murphy
- Bioinformatics
- 2009

MOTIVATION
Analysis of array comparative genomic hybridization (aCGH) data for recurrent DNA copy number alterations from a cohort of patients can yield distinct sets of molecular signatures or profiles. This can be due to the presence of heterogeneous cancer subtypes within a supposedly homogeneous population.
RESULTS
We propose a novel statistical… (More)