In this paper, we extend the classical result by Huang, Kintala, Kolettis and Fulton (1995), and in addition propose a modified stochastic model to determine the software rejuvena...
Hierarchical reinforcement learning (RL) is a general framework which studies how to exploit the structure of actions and tasks to accelerate policy learning in large domains. Pri...
Scene text images feature an abundance of font style variety but a dearth of data in any given query. Recognition methods must be robust to this variety or adapt to the query data...
The goal of online failure prediction is to forecast imminent failures while the system is running. This paper compares Similar Events Prediction (SEP) with two other well-known t...
The policy optimization problem for dynamic power management has received considerable attention in the recent past. We formulate policy optimization as a constrained optimization...