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Publications associées (161)

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Modeling Scenes with Local Descriptors and Latent Aspects

Daniel Gatica-Perez, Jean-Marc Odobez, Florent Monay Michaud, Pedro Manuel Da Silva Quelhas

We present a new approach to model visual scenes in image collections, based on local invariant features and probabilistic latent space models. Our formulation provides answers to three open questions:(1) whether the invariant local features are suitable f ...

2005

Spectral Entropy Feature in Full-Combination Multi-Stream for Robust ASR

Hervé Bourlard, Hemant Misra

In a recent paper, we reported promising automatic speech recognition results obtained by appending spectral entropy features to PLP features. In the present paper, spectral entropy features are used along with PLP features in the framework of multi-stream ...

2005

Spectral Entropy Feature in Full-Combination Multi-stream for Robust ASR

Hervé Bourlard, Hemant Misra

IDIAP2005

Modeling Scenes with Local Descriptors and Latent Aspects

Daniel Gatica-Perez, Jean-Marc Odobez, Florent Monay Michaud, Pedro Manuel Da Silva Quelhas

IDIAP2004

Variational Information Maximization in Gaussian Channels

Recently, we introduced a simple variational bound on mutual information, that resolves some of the difficulties in the application of information theory to machine learning. Here we study a specific application to Gaussian channels. It is well known that ...

IDIAP2004

Variational Information Maximization for Population Coding

The goal of neural processing assemblies is varied, and in many cases still rather unclear. However, a possibly reasonable subgoal is that sensory information may be encoded efficiently in a population of neurons. In this context, Mutual Information is a l ...

IDIAP2004

Sparse Gaussian Process Classification With Multiple Classes

Matthias Seeger

Sparse approximations to Bayesian inference for nonparametric Gaussian Process models scale linearly in the number of training points, allowing for the application of these powerful kernel-based models to large datasets. We show how to generalize the binar ...

Department of Statistics, University of Berkeley, CA2004

New Entropy Based Combination Rules in HMM/ANN Multi-stream ASR

Hervé Bourlard, Hemant Misra, Vivek Tyagi

Classifier performance is often enhanced through combining multiple streams of information. In the context of multi-stream HMM/ANN systems in ASR, a confidence measure widely used in classifier combination is the entropy of the posteriors distribution outp ...

2003

Handwritten Digit Recognition with Binary Optical Perceptron

Binary weights are favored in electronic and optical hardware implementations of neural networks as they lead to improved system speeds. Optical neural networks based on fast ferroelectric liquid crystal binary level devices can benefit from the many order ...

Springer-Verlag1997

Handwritten Digit Recognition with Binary Optical Perceptron

IDIAP1997