We show that for every α > 0, there exist n-point metric spaces (X, d) where every “scale” admits a Euclidean embedding with distortion at most α, but the whole space req...
Abstract. Most of the work in Machine Learning assume that examples are generated at random according to some stationary probability distribution. In this work we study the problem...
—In the context of multiple constant multiplications (MCM) design, we propose a novel common-subexpression-elimination (CSE) algorithm that models synthesis of coefficients into ...
Reinforcement learning is an effective machine learning paradigm in domains represented by compact and discrete state-action spaces. In high-dimensional and continuous domains, ti...
Historically, rendering system development has been mainly focused on improving the numerical accuracy of the rendering algorithms and their runtime efficiency. In this paper, we ...