Transfer learning aims at reusing the knowledge in some source tasks to improve the learning of a target task. Many transfer learning methods assume that the source tasks and the ...
Bin Cao, Sinno Jialin Pan, Yu Zhang, Dit-Yan Yeung...
Abstract-- This paper presents a novel framework for integrating fundamental tasks in robotic navigation through a statistical inference procedure. A probabilistic model that joint...
The goal of robot learning from demonstration is to have a robot learn from watching a demonstration of the task to be performed. In our approach to learning from demonstration th...
This paper describes the experiments of the State University of New York at Buffalo in TREC 13. We participated in the Genomics track and submitted official runs to the Adhoc retri...
Miguel E. Ruiz, Munirathnam Srikanth, Rohini K. Sr...
FLAVERS is a finite-state verification approach that allows an analyst to incrementally add constraints to improve the precision of the model of the system being analyzed. Except ...