Publications related to Boltzmann machine

Computational Methods For Structured Sparse Component Analysis of Convolutive Speech Mixtures

Hervé Bourlard, Volkan Cevher, Afsaneh Asaei

We cast the under-determined convolutive speech separation as sparse approximation of the spatial spectra of the mixing sources. In this framework we compare and contrast the major practical algorithms for structured sparse recovery of speech signal. Speci ...

2012

Coding and Decoding with Adapting Neurons: A Population Approach to the Peri-Stimulus Time Histogram

Wulfram Gerstner, Richard Naud

The response of a neuron to a time-dependent stimulus, as measured in a Peri-Stimulus-Time-Histogram (PSTH), exhibits an intricate temporal structure that reflects potential temporal coding principles. Here we analyze the encoding and decoding of PSTHs for ...

Public Library of Science2012

Improving Articulatory Feature and Phoneme Recognition using Multitask Learning

Ramya Rasipuram

Speech sounds can be characterized by articulatory features. Articulatory features are typically estimated using a set of multilayer perceptrons (MLPs), i.e., a separate MLP is trained for each articulatory feature. In this paper, we investigate multitask ...

Springer Berlin / Heidelberg2011

MULTITASK LEARNING TO IMPROVE ARTICULATORY FEATURE ESTIMATION AND PHONEME RECOGNITION

Ramya Rasipuram

Speech sounds can be characterized by articulatory features. Articulatory features are typically estimated using a set of multilayer perceptrons (MLPs), i.e., a separate MLP is trained for each articulatory feature. In this report, we investigate multitask ...

Idiap2011

Model-Based Compressive Sensing for Multi-Party Distant Speech Recognition

Hervé Bourlard, Volkan Cevher, Afsaneh Asaei

We leverage the recent algorithmic advances in compressive sensing, and propose a novel source separation algorithm for efficient recovery of convolutive speech mixtures in spectro-temporal domain. Compared to the common sparse component analysis technique ...

Idiap2011

Model-Based Compressive Sensing for Multi-Party Distant Speech Recognition

Hervé Bourlard, Volkan Cevher, Afsaneh Asaei

We leverage the recent algorithmic advances in compressive sensing, and propose a novel source separation algorithm for efficient recovery of convolutive speech mixtures in spectro-temporal domain. Compared to the common sparse component analysis technique ...

IEEE Service Center, 445 Hoes Lane, PO Box 1331, Piscataway, NJ 08855-1331 USA2011

Model-based Compressive Sensing for Multi-party Distant Speech Recognition

Hervé Bourlard, Volkan Cevher, Afsaneh Asaei

We leverage the recent algorithmic advances in compressive sensing, and propose a novel source separation algorithm for efficient recovery of convolutive speech mixtures in spectro-temporal domain. Compared to the common sparse component analysis technique ...

2011

Improved Similarity Measures for Small Sets of Spike Trains

Wulfram Gerstner, Richard Naud, Joao Emanuel Felipe Gerhard, Skander Mensi

Multiple measures have been developed to quantify the similarity between two spike trains. These measures have been used for the quantification of the mismatch between neuron models and experiments as well as for the classification of neuronal responses in ...

Massachusetts Institute of Technology Press2011

Reservoir optimization in recurrent neural networks using properties of Kronecker product

Martin Hasler, Ali Ajdari Rad, Mahdi Jalili

Recurrent neural networks based on reservoir computing are increasingly being used in many applications. Optimization of the topological structure of the reservoir and the internal connection weights for a given task is one of the most important problems i ...

2010

Statistical tests for neural population models - The multivariate time rescaling theorem

Joao Emanuel Felipe Gerhard

The coordinated, collective spiking activity of neuronal populations encodes and processes information. One approach towards understanding such population based computation is to fit statistical models to simultaneously recorded spike trains and use these ...

2010

Boltzmann machine

Computational Methods For Structured Sparse Component Analysis of Convolutive Speech Mixtures

Coding and Decoding with Adapting Neurons: A Population Approach to the Peri-Stimulus Time Histogram

Improving Articulatory Feature and Phoneme Recognition using Multitask Learning

MULTITASK LEARNING TO IMPROVE ARTICULATORY FEATURE ESTIMATION AND PHONEME RECOGNITION

Model-Based Compressive Sensing for Multi-Party Distant Speech Recognition

Model-Based Compressive Sensing for Multi-Party Distant Speech Recognition

Model-based Compressive Sensing for Multi-party Distant Speech Recognition

Improved Similarity Measures for Small Sets of Spike Trains

Reservoir optimization in recurrent neural networks using properties of Kronecker product

Statistical tests for neural population models - The multivariate time rescaling theorem

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Model-Based Compressive Sensing for Multi-Party Distant Speech Recognition

Model-Based Compressive Sensing for Multi-Party Distant Speech Recognition

Model-based Compressive Sensing for Multi-party Distant Speech Recognition

Statistical tests for neural population models - The multivariate time rescaling theorem

Computational Methods For Structured Sparse Component Analysis of Convolutive Speech Mixtures

Coding and Decoding with Adapting Neurons: A Population Approach to the Peri-Stimulus Time Histogram

MULTITASK LEARNING TO IMPROVE ARTICULATORY FEATURE ESTIMATION AND PHONEME RECOGNITION

Improved Similarity Measures for Small Sets of Spike Trains

Improving Articulatory Feature and Phoneme Recognition using Multitask Learning

Reservoir optimization in recurrent neural networks using properties of Kronecker product