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Publications associées (26)

Denoising and Raw-waveform Networks for Weakly-Supervised Gender Identification on Noisy Speech

Mathew Magimai Doss, Jilt Sebastian, Manoj Kumar

This paper presents a raw-waveform neural network and uses it along with a denoising network for clustering in weakly supervised learning scenarios under extreme noise conditions. Specifically, we consider language independent Automatic Gender Recognition ...

ISCA-INT SPEECH COMMUNICATION ASSOC2018

Empirical study of the topology and geometry of deep networks

Pascal Frossard, Seyed Mohsen Moosavi Dezfooli, Alhussein Fawzi

The goal of this paper is to analyze the geometric properties of deep neural network image classifiers in the input space. We specifically study the topology of classification regions created by deep networks, as well as their associated decision boundary. ...

2018

Error Tolerance Analysis of Deep Learning Hardware Using a Restricted Boltzmann Machine Toward Low-Power Memory Implementation

Alexandre Schmid, Takao Marukame, Kodai Ueyoshi

Remarkable hardware robustness of deep learning (DL) is revealed by error injection analyses performed using a custom hardware model implementing parallelized restricted Boltzmann machines (RBMs). RBMs in deep belief networks demonstrate robustness against ...

Ieee-Inst Electrical Electronics Engineers Inc2017

The Robustness of Deep Networks - A geometric perspective

Pascal Frossard, Seyed Mohsen Moosavi Dezfooli, Alhussein Fawzi

Deep neural networks have recently shown impressive classification performance on a diverse set of visual tasks. When deployed in real-world (noise-prone) environments, it is equally important that these classifiers satisfy robustness guarantees: small per ...

Institute of Electrical and Electronics Engineers2017

Tensor train approximation of moment equations for elliptic equations with lognormal coefficient

Fabio Nobile, Daniel Kressner, Francesca Bonizzoni

We study an elliptic equation with stochastic coefficient modeled as a lognormal random field. A perturbation approach is adopted, expanding the solution in Taylor series around the nominal value of the coefficient. The resulting recursive deterministic pr ...

Elsevier2016

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

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