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 ) ====