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DTSTART;TZID=UTC:20211109T110000
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SUMMARY:AARMS Scientific Machine Learning Seminar: Hamid Usefi (MUN)
DESCRIPTION:Multicollinearity\, singular vectors\, and dimensionality reduction for high-dimensional datasets\nSingle 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 of cost and time to genetically sequence a single suspect tissue. The number of SNPs in a disease dataset varies from tens of thousands to several million. So\, one of the bottlenecks of working with these genome datasets is their large-scale size that makes it difficult to render the data for meaningful analysis.  Furthermore\, in most diseases\, there are at most a couple of hundred SNPs associated with the disease. So\, simply put we would be looking for a needle in a haystack. \nMachine learning algorithms are gaining increasing attention and believe to have great potential in answering many questions in this respect. A common problem in machine learning and pattern recognition is the process of identifying the most relevant features\, specifically in dealing with high-dimensional datasets in bioinformatics. In this talk\, I will discuss some of our recent work on a new feature selection method\, called Singular-Vectors Feature Selection (SVFS). Part of this work is joint with my recently graduated PhD student Majid Afshar. It is stemmed from identifying linearly dependent columns of a matrix A. This problem can also be viewed as multicollinearity and subset selection in statistical modelling and arises in many contexts\, including regression\, ecology\, and machine learning. \nLet D = [A | b] be a labeled dataset\, where b is the class label and features (attributes or SNPs) are columns of matrix A; rows of A can be viewed as samples. We show with examples as well as a sketch of proof that the projector matrix P_A onto the null space of A can be used to partition the columns of A into clusters so that columns in a cluster correlate only with the columns in the same cluster. In the first step\, SVFS uses the projector P_D to find the cluster that contains b. We reduce the size of A by discarding features in the other clusters as irrelevant features. In the next step\, SVFS uses the P_A of reduced A to partition the remaining features into clusters and choose the most important features from each cluster. I will discuss the  performance of SVFS on genomic datasets compared to the state-of-the-art feature selection methods. \n[ recording ] \n\nMeeting link:  https://mun.webex.com/mun/j.php?MTID=m855b1e73549cf668f5b57190a7ef3eae\nMeeting number:   2633 483 6250\nMeeting password:  W3FisMnJa86\n\n\n 
URL:https://aarms.math.ca/event/aarms-scientific-machine-learning-seminar-hamid-usefi-mun/
LOCATION:WebEx seminar
CATEGORIES:AARMS Scientific Machine Learning Seminar
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DTSTART;TZID=UTC:20211123T110000
DTEND;TZID=UTC:20211123T120000
DTSTAMP:20211117T110237Z
CREATED:20211112T192613Z
LAST-MODIFIED:20211117T110237Z
UID:6497-1637665200-1637668800@aarms.math.ca
SUMMARY:AARMS Scientific Machine Learning Seminar: Ben Adcock (Simon Fraser University)
DESCRIPTION:Title: Tackling the curse: polynomial and deep neural network methods for function approximation in high dimensions \nAbstract: 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 to the famous “curse of dimensionality” has been a long-standing problem. In the first part of this talk I will give a brief survey of a decade’s worth of progress on high-dimensional function approximation via sparse polynomial expansions. I will show how the proper use of nonlinear approximation theory and compressed sensing techniques leads to algorithms for high-dimensional approximation which\, unlike other approaches\, possess provably near-optimal error bounds and moderate sample complexities. In particular\, these techniques mitigate the curse of dimensionality to a substantial degree. The second part of the talk will be devoted to emerging approaches based on deep neural networks and deep learning. Such tools are beginning to garner substantial attention in the scientific computing community. Nonetheless\, I will present evidence of a key gap between current theory and practice. I will then discuss recent results showing that there exist deep neural networks that match the performance of best-in-class schemes\, and furthermore\, these can indeed be trained through realizable procedures. This highlights the potential of deep neural networks\, and sheds light on achieving robust\, reliable and overall improved practical performance. \nThis talk is based on joint work with Anyi Bao\, Simone Brugiapaglia\, Juan M. Cardenas\, Nick Dexter\, Sebastian Moraga\, Yi Sui and Clayton G. Webster. \nMeeting link:\nhttps://mun.webex.com/mun/j.php?MTID=m38cc6ce370de2b1c234e33feac5bb93a \nMeeting number: 2630 760 7617\nPassword: cJwPcrHM425
URL:https://aarms.math.ca/event/aarms-scientific-machine-learning-seminar-ben-adcock-sfu/
LOCATION:WebEx seminar
CATEGORIES:AARMS Scientific Machine Learning Seminar
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