ABOUT MY WORK
My research deals with developing and analyzing novel efficient algorithms for learning and inference, and applying these algorithms in challenging real world domains.
My research interests are mainly related to statistical machine learning and more specifically to the fields of graphical models and deep learning. As often pointed out, the same machine learning models and algorithms can be applied in many different research areas. In my research I concentrate on developing and analyzing those algorithms in the context of classical machine learning tasks (classification, clustering, dimensionality reduction etc.) and applying them to a large variety of real world applications.
Information-theory interpretation of the skip-gram negative-sampling objective function. | ACL, 2017.
A simple language model based on PMI matrix approximations. | EMNLP, 2017
Training deep neural-networks using a noise adaptation layer. | ICLR, 2017
Deep recurrent mixture of experts for speech enhancement. | (WASPAA), 2017.