RAPID MRI analysis may speed up selection for tPA therapy

Exploiting Example Selection and Ordering to Speed-Up Learning

In this paper the results of a preliminary study on the effects of selection and ordering of examples in incremental machine learning are presented. We consider learning as a two agents communication process, in which the teacher supplies information to the learner according to a pre--defined protocol, in order to transfer to him the knowledge sufficient to solve a given problem. The aim of thi...

Fast Forward Selection to Speed Up Sparse Gaussian Process Regression

We present a method for the sparse greedy approximation of Bayesian Gaussian process regression, featuring a novel heuristic for very fast forward selection. Our method is essentially as fast as an equivalent one which selects the “support” patterns at random, yet it can outperform random selection on hard curve fitting tasks. More importantly, it leads to a sufficiently stable approximation of...

Subtree Parsing to Speed up Deep Analysis

Monotonicity Analysis Can Speed Up Verification

We introduce a strategy for the verification of relational specifications based on the analysis of monotonicity of variables within formulas. By comparing with the Alloy Analyzer, we show that for a relevant class of problems this technique outperforms analysis of the same problems using SAT-solvers, while consuming a fraction of the memory SAT-solvers require.

Transformations for variational factor analysis to speed up learning

We propose simple transformation of the hidden states in variational Bayesian factor analysis models to speed up the learning procedure. The speed-up is achieved by using proper parameterization of the posterior approximation which allows joint optimization of its individual factors, thus the transformation is theoretically justified. We derive the transformation formulae for variational Bayesi...