In this paper, we present a prototype that helps visualizing the relative importance of sentences extracted from medical texts using Embodied Conversational Agents (ECA). We propo...
Learning a sequence classifier means learning to predict a sequence of output tags based on a set of input data items. For example, recognizing that a handwritten word is "ca...
Given a collection of complex, time-stamped events, how do we find patterns and anomalies? Events could be meetings with one or more persons with one or more agenda items at zero ...
Hanghang Tong, Yasushi Sakurai, Tina Eliassi-Rad, ...
In this paper, we propose a fast, memory-efficient, and scalable clustering algorithm for analyzing transactional data. Our approach has three unique features. First, we use the c...
External regret compares the performance of an online algorithm, selecting among N actions, to the performance of the best of those actions in hindsight. Internal regret compares ...