Concept

Boltzmann machine

Publications associées (109)

An Investigation of Deep Neural Networks for Multilingual Speech Recognition Training and Adaptation

Hervé Bourlard, Philip Neil Garner, Sibo Tong

Different training and adaptation techniques for multilingual Automatic Speech Recognition (ASR) are explored in the context of hybrid systems, exploiting Deep Neural Networks (DNN) and Hidden Markov Models (HMM). In multilingual DNN training, the hidden l ...

2017

Machine Vision Algorithms on Cadaster Plans

Isabella Di Lenardo, Sofia Ares Oliveira

Cadaster plans are cornerstones for reconstructing dense representations of the history of the city. They provide information about the city urban shape, enabling to reconstruct footprints of most important urban components as well as information about the ...

2017

Machine Vision Algorithms on Cadaster Plans

Frédéric Kaplan, Isabella Di Lenardo, Sofia Ares Oliveira

2017

Memory-error tolerance of Scalable and Highly Parallel Architecture for Restricted Boltzmann Machines in Deep Belief Network

Alexandre Schmid, Takao Marukame, Kodai Ueyoshi

A key aspect of constructing highly scalable Deep-learning microelectronic systems is to implement fault tolerance in the learning sequence. Error-injection analyses for memory is performed using a custom hardware model implementing parallelized restricted ...

Ieee2016

DEEP NEURAL NETWORK BASED POSTERIORS FOR TEXT-DEPENDENT SPEAKER VERIFICATION

Petr Motlicek, Subhadeep Dey

The i-vector and Joint Factor Analysis (JFA) systems for text- dependent speaker verification use sufficient statistics computed from a speech utterance to estimate speaker models. These statis- tics average the acoustic information over the utterance ther ...

IEEE2016

DEEP NEURAL NETWORK BASED POSTERIORS FOR TEXT-DEPENDENT SPEAKER VERIFICATION

Petr Motlicek, Subhadeep Dey

Idiap2016

Stochastic Spectral Descent for Discrete Graphical Models

Volkan Cevher, Ya-Ping Hsieh, Edo Collins

Interest in deep probabilistic graphical models has increased in recent years, due to their state-of-the-art perfor- mance on many machine learning applications. Such models are typically trained with the stochastic gradient method, which can take a signif ...

Ieee-Inst Electrical Electronics Engineers Inc2016

Enhanced Sensitivity to Rapid Input Fluctuations by Nonlinear Threshold Dynamics in Neocortical Pyramidal Neurons

Wulfram Gerstner, Skander Mensi, Christian Antonio Pozzorini, Olivier Richard Hagens

The way in which single neurons transform input into output spike trains has fundamental consequences for network coding. Theories and modeling studies based on standard Integrate-and-Fire models implicitly assume that, in response to increasingly strong i ...

Public Library of Science2016

Stochastic Spectral Descent for Restricted Boltzmann Machines.

Volkan Cevher

Restricted Boltzmann Machines (RBMs) are widely used as building blocks for deep learning models. Learning typically proceeds by using stochastic gradient descent, and the gradients are estimated with sampling methods. However, the gradient estimation is a ...

2015

Preconditioned Spectral Descent for Deep Learning

Volkan Cevher, Ya-Ping Hsieh, Edo Collins

Deep learning presents notorious computational challenges. These challenges in- clude, but are not limited to, the non-convexity of learning objectives and estimat- ing the quantities needed for optimization algorithms, such as gradients. While we do not a ...

2015

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