This paper investigates a new learning formulation called dynamic group sparsity. It is a natural extension of the standard sparsity concept in compressive sensing, and is motivat...
Modern machine learning techniques provide robust approaches for data-driven modeling and critical information extraction, while human experts hold the advantage of possessing hig...
This is the first part of a two-part paper on information-theoretically secure secret key agreement. In this part, we study the secrecy problem under the widely studied source mod...
In emotion recognition, a widely-used method to reconciliate disagreement between multiple human evaluators is to perform majority-voting on their assigned class labels. Instead, ...
In this work we present two extensions to the well-known dynamic programming beam search in phrase-based statistical machine translation (SMT), aiming at increased efficiency of ...