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October 2021

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

October 26, 2021 @ 11:00 am - 12:00 pm
WebEx seminar

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…

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November 2021

AARMS Scientific Machine Learning Seminar: Hamid Usefi (MUN)

November 9, 2021 @ 11:00 am - 12:00 pm
WebEx seminar

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…

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AARMS Scientific Machine Learning Seminar: Ben Adcock (Simon Fraser University)

November 23, 2021 @ 11:00 am - 12:00 pm
WebEx seminar

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…

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December 2021

AARMS Scientific Machine Learning Seminar: Peter Dueben (ECMWF)

December 7, 2021 @ 11:00 am - 12:00 pm
WebEx seminar

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

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