The general stochastic optimal control (SOC) problem in robotics scenarios is often too complex to be solved exactly and in near real time. A classical approximate solution is to ...
Sampling is a popular way of scaling up machine learning algorithms to large datasets. The question often is how many samples are needed. Adaptive stopping algorithms monitor the ...
In this work, we present an algorithm for simultaneous template generation and matching. The algorithm profiles the graph and iteratively contracts edges to create the templates. ...
Ryan Kastner, Seda Ogrenci Memik, Elaheh Bozorgzad...
We use a Bayesian approach to optimally solve problems in noisy binary search. We deal with two variants: • Each comparison is erroneous with independent probability 1 − p. â€...
We address the problem of keyword spotting in continuous speech streams when training and testing conditions can be different. We propose a keyword spotting algorithm based on spa...