Stochastic relational models (SRMs) [15] provide a rich family of choices for learning and predicting dyadic data between two sets of entities. The models generalize matrix factor...
Abstract— Our main goal in this paper is to set the foundations of a general continuous-domain framework for designing steerable, reversible signal transformations (a.k.a. frames...
We introduce a general-purpose learning machine that we call the Guaranteed Error Machine, or GEM, and two learning algorithms, a real GEM algorithm and an ideal GEM algorithm. Th...
Abstract-- Reinforcement Programming (RP) is a new approach to automatically generating algorithms, that uses reinforcement learning techniques. This paper describes the RP approac...
Spencer K. White, Tony R. Martinez, George L. Rudo...
We present a technique to approximate the worst-case execution time that combines structural analysis with a loop-bounding algorithm based on local induction variable analysis. St...