In many practical reinforcement learning problems, the state space is too large to permit an exact representation of the value function, much less the time required to compute it. ...
Graphical models are usually learned without regard to the cost of doing inference with them. As a result, even if a good model is learned, it may perform poorly at prediction, be...
In this work we focus on efficient heuristics for solving a class of stochastic planning problems that arise in a variety of business, investment, and industrial applications. The...
Forecasting of wave height is necessary in a large number of ocean coastal activities. Recently, neural networks are used for prediction and approximation of wave heights in sea a...
Abstract-- We study the problem of total throughput maximization in arbitrary multi-hop wireless networks, with constraints on the total power usage (denoted by PETM), when nodes h...
Deepti Chafekar, V. S. Anil Kumar, Madhav V. Marat...