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DTSTART;TZID=UTC:20220301T110000
DTEND;TZID=UTC:20220301T120000
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SUMMARY:AARMS Scientific Machine Learning Seminar: Stijn De Baerdemacker (UNB)
DESCRIPTION:Deep Learning Chemistry: extracting chemical features from Graph Convolutional Neural Networks\n\nThe 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 have been significant advances in the design of Machine Learning (ML) algorithms to interpolate chemical observables from precomputed data\, rather than engaging into costly computations.  At present\, the ML methods of choice in (quantum) chemistry are based upon Graph Convolutional Neural Networks (GCNN)\, as molecular compounds are easily reinterpreted into graph-based structures.\n\nIn this presentation\, I will discuss recent results from the QuNB group on the analysis of the feature space of SchNet\, a popular GCNN for quantum chemistry with high predictive power.  I will show that chemical information from chemical functional groups is learned and encoded in the feature space of the GCNN\, giving a strong quality hallmark of interpretability to the GCNN ML algorithm.\n\nJoint work from the QuNB group (UNB) with Guillaume Acke (Ghent University)\n\nWebex link: https://mun.webex.com/mun/j.php?MTID=m8b04b29275dfeb0c3617be2958e577ff
URL:https://aarms.math.ca/event/aarms-scientific-machine-learning-seminar-stijn-de-baerdemacker-unb/
CATEGORIES:AARMS Scientific Machine Learning Seminar
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BEGIN:VEVENT
DTSTART;TZID=UTC:20220308T110000
DTEND;TZID=UTC:20220308T120000
DTSTAMP:20220228T160748Z
CREATED:20220228T160654Z
LAST-MODIFIED:20220228T160748Z
UID:6615-1646737200-1646740800@aarms.math.ca
SUMMARY:AARMS Scientific Machine Learning Seminar: Simone Brugiapaglia (Concordia University)
DESCRIPTION:The curse of dimensionality and the blessings of sparsity and Monte Carlo sampling: From polynomial approximation to deep learning in high dimensions \nIn 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.\n\nFirst\, we will consider the case of sparse polynomial approximation via compressed sensing. Focusing on the case where the target function is smooth\, but possibly highly anisotropic\, we will show how to obtain sample complexity bounds only mildly affected by the curse of dimensionality\, near-optimal accuracy guarantees\, stability to unknown errors corrupting the data\, and rigorous convergence rates of algebraic and exponential type.\n\nThen\, we will illustrate how the mathematical toolkit of sparse polynomial approximation can be employed to obtain a “practical existence theorem” for deep learning in the context of high-dimensional Hilbert-valued function approximation. This result shows not only the existence of neural networks with desirable approximation properties\, but also how to compute them via a suitable training procedure in order to achieve best-in-class performance guarantees.\n\nWe will conclude by discussing ongoing and future research directions.\n\n\nWebex link:\n\nhttps://mun.webex.com/mun/j.php?MTID=m3908ed63dfe6896d1e2421f4a3356bc9
URL:https://aarms.math.ca/event/aarms-scientific-machine-learning-seminar-simone-brugiapaglia-concordia-university/
LOCATION:WebEx seminar
CATEGORIES:AARMS Scientific Machine Learning Seminar
ORGANIZER;CN="Alexander Bihlo":MAILTO:abihlo@mun.ca
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20220329T110000
DTEND;TZID=UTC:20220329T120000
DTSTAMP:20220322T143839Z
CREATED:20220322T143839Z
LAST-MODIFIED:20220322T143839Z
UID:6631-1648551600-1648555200@aarms.math.ca
SUMMARY:AARMS Scientific Machine Learning Seminar: Scott MacLachlan (Memorial)
DESCRIPTION:Optimization and Learning in the Design of Preconditioners\n\nComputer 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 these systems are multigrid and domain decomposition methods\, which use multiscale representations of the systems to be solved to achieve linear-scaling complexity for the solution of these linear systems.  These preconditioners typically rely on heuristics in their construction\, to approximate solutions to underlying combinatorial (and other) optimization problems that specify parameters and other components of the preconditioners\, based on the discrete problem to which they are being applied.  In this talk\, I will discuss the use of advanced optimization and machine learning techniques to approximately solve these optimization problems and the impact these techniques can have on advanced preconditioner design.\n\n\nWebex link:\nhttps://mun.webex.com/mun/j.php?MTID=m8e73e9072ce63a9072b87874c1ee7cae
URL:https://aarms.math.ca/event/aarms-scientific-machine-learning-seminar-scott-maclachlan-memorial/
LOCATION:WebEx seminar
CATEGORIES:AARMS Scientific Machine Learning Seminar
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