Group equivariant neural networks seen by a mathematician Artificial neural networks (ANNs) are incredibly successful at performing certain machine learning tasks, such as classification. In applications such as computer vision or quantum chemistry, we will often seek machine learning algorithms

Multicollinearity, singular vectors, and dimensionality reduction for high-dimensional datasets Single nucleotide polymorphisms (SNPs) as building blocks of our DNA, can determine the variations between people. It is believed that SNPs in genes that regulate DNA mismatch repair, cell cycle regulation,

Title: Tackling the curse: polynomial and deep neural network methods for function approximation in high dimensions Abstract: Many problems in computational science and engineering require the accurate approximation of a target function from data. This problem is rendered challenging by

This talk provides an overview on the machine learning efforts at the European Centre for Medium-Range Weather Forecasts (ECMWF), and outlines how machine learning, and in particular deep learning, could help to improve weather predictions in the coming years. The talk will

Using Normalizing Flows for Seismic Data Interpolation Normalizing Flows are a type of neural network that allow us to map one probability distribution into another.  The advantage of such a technique is that they allow us to relate a simple

Deep Learning Chemistry: extracting chemical features from Graph Convolutional Neural Networks The task of quantum chemistry is to compute chemical properties of molecular compounds from the fundamental laws of quantum mechanics.  This poses a tremendous challenge as the most accurate

The curse of dimensionality and the blessings of sparsity and Monte Carlo sampling: From polynomial approximation to deep learning in high dimensions In data science and scientific computing, the approximation of high-dimensional functions from pointwise samples is a ubiquitous task,

Optimization and Learning in the Design of Preconditioners Computer simulation algorithms are a major tool in many areas of science and industry, particularly in areas where the behaviour of fluids or complex materials governs the physical processes of interest.  A

Conservative Hamiltonian Monte Carlo Markov Chain Monte Carlo (MCMC) methods enable us to extract meaningful statistics from complex distributions which frequently appear in parameter estimation, Bayesian statistics, statistical mechanics and machine learning. Similar to how flipping a coin, or rolling a

Semisupervised machine learning algorithms and their application to geoscience classification problems In recent years, many disciplines have been challenged with trying to efficiently extract meaning, or value, out of large datasets. Technological advances have improved data storage capabilities as well