Abstract Models are among the most essential tools in robotics, such as kinematics and
dynamics models of the robot's own body and controllable external objects. It is widely …

The increasing wealth of biological data coming from a large variety of platforms and the
continued development of new high-throughput methods for probing biological systems …

Text documents can be described by a number of abstract concepts such as semantic
category, writing style, or sentiment. Machine learning (ML) models have been trained to …

Most of the currently used techniques for linear system identification are based on classical
estimation paradigms coming from mathematical statistics. In particular, maximum likelihood …

The accurate and reliable prediction of properties of molecules typically requires
computationally intensive quantum-chemical calculations. Recently, machine learning …

Abstract The Support Vector Machine (SVM) is a widely used classifier in bioinformatics.
Obtaining the best results with SVMs requires an understanding of their workings and the …

An important goal in visual recognition is to devise image representations that are invariant
to particular transformations. In this paper, we address this goal with a new type of …

An easy-to-follow introduction to support vector machines This book provides an in-depth,
easy-to-follow introduction to support vector machines drawing only from minimal, carefully …

We introduce a class of nonstationary covariance functions for Gaussian process (GP)
regression. Nonstationary covariance functions allow the model to adapt to functions whose …

In this paper we present the implementation of a framework for accelerating training and
classification of arbitrary Convolutional Neural Networks (CNNs) on the GPU. CNNs are a …