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20062013
most citedBetter Mixing via Deep Representations

209 citations

Showing cs.LGShow all

6 papers · 1 filter

cs.LG2013

Functional Regularized Least Squares Classi cation with Operator-valued Kernels

Hachem Kadri, Asma Rabaoui, Philippe Preux +2

Although operator-valued kernels have recently received increasing interest in various machine learning and functional data analysis problems such as multi-task learning or functio…

cs.LG2012209 cited

Better Mixing via Deep Representations

Yoshua Bengio, Grégoire Mesnil, Yann Dauphin +1

It has previously been hypothesized, and supported with some experimental evidence, that deeper representations, when well trained, tend to do a better job at disentangling the und…

cs.LG201214 cited

Adaptive Canonical Correlation Analysis Based On Matrix Manifolds

Florian Yger, Maxime Berar, Gilles Gasso +1

In this paper, we formulate the Canonical Correlation Analysis (CCA) problem on matrix manifolds. This framework provides a natural way for dealing with matrix constraints and tool…

cs.LG201240 cited

Craniofacial reconstruction as a prediction problem using a Latent Root Regression model

Maxime Berar, Françoise Tilotta, Joan Alexis Glaunès +1

In this paper, we present a computer-assisted method for facial reconstruction. This method provides an estimation of the facial shape associated with unidentified skeletal remains…

cs.LG2011

Handling uncertainties in SVM classification

Emilie Niaf, Rémi Flamary, Carole Lartizien +1

This paper addresses the pattern classification problem arising when available target data include some uncertainty information. Target data considered here is either qualitative (…

cs.LG20101 cited

Filtrage vaste marge pour l'étiquetage séquentiel à noyaux de signaux

Rémi Flamary, Benjamin Labbé, Alain Rakotomamonjy

We address in this paper the problem of multi-channel signal sequence labeling. In particular, we consider the problem where the signals are contaminated by noise or may present so…