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## October 2021

### AARMS Scientific Machine Learning Seminar: Nicholas Touikan (University of New Brunswick)

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 that can handle inputs that are transformed. For example, a cat detector should be able to detect a rotated cat. Group theory provides the natural formalization of what we mean by transformations and group equivariance is the property we seek…

Find out more »## November 2021

### AARMS Scientific Machine Learning Seminar: Hamid Usefi (MUN)

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, metabolism and immunity are associated with genetic susceptibility to cancer. So, SNPs are potential diagnostic and therapeutic biomarkers in many cancer types. This in part has prompted the rapid advancements in DNA sequencing which makes it possible both in terms…

Find out more »### AARMS Scientific Machine Learning Seminar: Ben Adcock (Simon Fraser University)

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 the high-dimensionality of the function, the expense of generating function samples, the presence of noise in the measurements, and the fact that the target function may take values in a function space. Developing techniques that tackle these challenges without succumbing…

Find out more »## December 2021

### AARMS Scientific Machine Learning Seminar: Peter Dueben (ECMWF)

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 name challenges for the use of machine learning and suggest developments (research/software/hardware) that should enable the community of Earth system modelling to make quick progress. Webex information: Link: https://mun.webex.com/mun/j.php?MTID=m32a22bd61d05707cefe973ca5bf6ad69 Meeting no: 2631 545 3588 Password: t32XHrsMqu6

Find out more »## February 2022

### AARMS Scientific Machine Learning Seminar: Alison Malcom (Memorial University)

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 distribution, like a Gaussian, to a more complicated distribution that may be more difficult to estimate and sample from. In uncertainty quantification for inverse problems, we are trying to estimate one of these more complicated distributions, thus normalizing flows can…

Find out more »## March 2022

### AARMS Scientific Machine Learning Seminar: Stijn De Baerdemacker (UNB)

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 methods also come with a perniciously scaling computational cost, limiting systematic and meaningful calculations mostly to medium-small molecules. This is an undesirable situation as there is a high potential for computationally driven discovery of chemical compounds. For this reason, there…

Find out more »### AARMS Scientific Machine Learning Seminar: Simone Brugiapaglia (Concordia University)

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, which is made intrinsically difficult by the so-called curse of dimensionality. In this talk, we will illustrate how to alleviate the curse thanks to the "blessings" of sparsity and Monte Carlo sampling. First, we will consider the case of sparse…

Find out more »### AARMS Scientific Machine Learning Seminar: Scott MacLachlan (Memorial)

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 typical core of these tools is the numerical approximation of the solution to coupled nonlinear systems of partial differential equations, relying on nonlinear and linear solvers, such as Newton’s method and preconditioned Krylov iterations. Among the most effective preconditioners for…

Find out more »## April 2022

### AARMS Scientific Machine Learning Seminar: Geoffrey McGregor (University of Northern British Columbia)

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 dice, allows us to sample from the corresponding distributions underlying these processes, MCMC methods enable us to sample from more complex distributions. The sample statistics of the sequence generated by MCMC will converge to those of the target distribution, or…

Find out more »### AARMS Scientific Machine Learning Seminar: Michael W. Dunham (Department of Earth Sciences, Memorial University)

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 as how data can be obtained (e.g., real-time data). Manually interpreting data that are exponentially growing in volume has obvious management and analysis challenges. Machine learning is a solution to these challenges. Machine learning algorithms teach computers to recognize patterns…

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