[Lisa_teatalk] Tea Talk by Sarath Chandar on Friday (30th, 2:30pm)

Jörg Bornschein bornj at iro.umontreal.ca
Tue Oct 27 11:01:07 EDT 2015


Hi everyone,

this Friday at 2:30pm we will have a talk by Sarath Chandar about Correlational
Neural Networks.

Looking forward to the talk and hope to see you there,

    Jorg


== Details & Abstract ==
Title: Correlational Neural Networks
Speaker: Sarath Chandar
Time: Friday, October 30th, 2:30pm
Location: AA3195

Abstract:

Common Representation Learning (CRL), wherein different descriptions (or
views) of the data are embedded in a common subspace, is receiving a lot of
attention recently. Two popular paradigms here are Canonical Correlation
Analysis (CCA) based approaches and Autoencoder (AE) based approaches. CCA
based approaches learn a joint representation by maximizing correlation of
the views when projected to the common subspace. AE based methods learn a
common representation by minimizing the error of reconstructing the two
views. Each of these approaches has its own advantages and disadvantages.
For example, while CCA based approaches outperform AE based approaches for
the task of transfer learning, they are not as scalable as the latter. In
this work we propose an AE based approach called Correlational Neural
Network (CorrNet), that explicitly maximizes correlation among the views
when projected to the common subspace. Through a series of experiments, we
demonstrate that the proposed CorrNet is better than the above mentioned
approaches with respect to its ability to learn correlated common
representations. Further, we employ CorrNet for several cross language
tasks and show that the representations learned using CorrNet perform
better than the ones learned using other state of the art approaches.
CorrNet can be easily extended to the case where you have more than 2
views. We demonstrate this by applying CorrNet for two specific downstream
applications: cross language document classification across 12 different
languages and multilingual multimodal retrieval.

Links:
1. Correlational Neural Networks ( http://arxiv.org/abs/1504.07225 )
2. Bridge Correlational Neural Networks for Multilingual Multimodal
Representation Learning ( http://arxiv.org/abs/1510.03519 )
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