|Title||:||Correlational Neural Networks|
|Speaker||:||Mitesh Khapra (IBM Research)|
|Details||:||Mon, 5 Oct, 2015 2:00 PM @ BSB 361|
|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.
Speaker Bio: Mitesh M Khapra is currently working as a Researcher at IBM Research India. During the past three years at IBM he has worked on several interesting problems in the areas of Statistical Machine Translation, Cross Language Learning, Multimodal Learning, Argument Mining and Deep Learning. This work has led to publications in top conferences in the areas of Computational Linguistics and Machine Learning. Prior to joining IBM, he completed his PhD and M.Tech degrees from IIT Bombay in the year 2012 and 2008 respectively. His Ph.D. thesis dealt with the important problem of reusing resources for multilingual computation.