In recent years, the use of machine learning (ML) in computational chemistry has enabled
numerous advances previously out of reach due to the computational complexity of …

Deep learning approaches to anomaly detection (AD) have recently improved the state of
the art in detection performance on complex data sets, such as large collections of images or …

Machine learning models are poised to make a transformative impact on chemical sciences
by dramatically accelerating computational algorithms and amplifying insights available from …

Using conservation of energy—a fundamental property of closed classical and quantum
mechanical systems—we develop an efficient gradient-domain machine learning (GDML) …

Theoretical results suggest that in order to learn the kind of complicated functions that can
represent high-level abstractions (eg, in vision, language, and other AI-level tasks), one may …

In this tutorial we give an overview of the basic ideas underlying Support Vector (SV)
machines for function estimation. Furthermore, we include a summary of currently used …

A comprehensive introduction to Support Vector Machines and related kernel methods. In
the 1990s, a new type of learning algorithm was developed, based on results from statistical …

This paper introduces a general Bayesian framework for obtaining sparse solutions to
regression and classification tasks utilising models linear in the parameters. Although this …

Drug discovery aims at finding new compounds with specific chemical properties for the
treatment of diseases. In the last years, the approach used in this search presents an …

In this paper we apply a recently proposed technique of optimal control by support vector
machines (SVMs) to chaos control. Vapnik's support vector method, which is based on the …