Abstract. Bayesian reinforcement learning (RL) is aimed at making more efficient use of data samples, but typically uses significantly more computation. For discrete Markov Decis...
In this paper, we consider the link prediction problem, where we are given a partial snapshot of a network at some time and the goal is to predict the additional links formed at a ...
Janardhan Rao Doppa, Jun Yu, Prasad Tadepalli, Lis...
Finding relational expressions which exist frequently in one class of data while not in the other class of data is an interesting work. In this paper, a relational expression of th...
Lei Duan, Jie Zuo, Tianqing Zhang, Jing Peng, Jie ...
In this paper, we analyze the impact of different automatic annotation methods on the performance of supervised approaches to the complex question answering problem (defined in th...
In this paper, we propose a new technique, referred to as virtual probe (VP), to efficiently measure, characterize and monitor both inter-die and spatially-correlated intra-die va...