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DTSTART;TZID=UTC:20220405T113000
DTEND;TZID=UTC:20220405T123000
DTSTAMP:20260613T060312
CREATED:20220330T113152Z
LAST-MODIFIED:20220330T113152Z
UID:6634-1649158200-1649161800@aarms.math.ca
SUMMARY:AARMS Scientific Machine Learning Seminar: Geoffrey McGregor (University of Northern British Columbia)
DESCRIPTION:Conservative Hamiltonian Monte Carlo\n\nMarkov 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 “stationary distribution” provided certain acceptance and rejection criteria are satisfied. However\, as the dimensionality of the stationary distribution increases\, the acceptance rate of traditional MCMC methods inevitably diminishes and their convergence slows down substantially. This has led to recent developments in computational techniques\, such as Hamiltonian Monte Carlo (HMC) to improve the performance in convergence and acceptance rate. Specifically\, HMC proposes samples for acceptance or rejection by solving a Hamiltonian system of differential equations using volume preserving numerical methods.\n\nIn this talk\, we introduce the Conservative Hamiltonian Monte Carlo (CHMC) method\, which instead utilizes an energy preserving numerical method\, known as the Discrete Multiplier Method. We show that CHMC converges to the correct stationary distribution under appropriate conditions and provide numerical examples showcasing improvements on acceptance rates.\n\nThis is joint work with Andy Wan from the University of Northern British Columbia.\n\nWebex link:\nhttps://mun.webex.com/mun/j.php?MTID=mdff68bb6ee4a7d34ae94a2b77b2c4888
URL:https://aarms.math.ca/event/aarms-scientific-machine-learning-seminar-geoffrey-mcgregor-university-of-northern-british-columbia/
LOCATION:WebEx seminar
CATEGORIES:AARMS Scientific Machine Learning Seminar
ORGANIZER;CN="Alexander Bihlo":MAILTO:abihlo@mun.ca
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BEGIN:VEVENT
DTSTART;TZID=UTC:20220406T153000
DTEND;TZID=UTC:20220406T163000
DTSTAMP:20260613T060312
CREATED:20220404T142531Z
LAST-MODIFIED:20220404T142531Z
UID:6637-1649259000-1649262600@aarms.math.ca
SUMMARY:Atlantic Graph Theory Seminar: John Engbers (Marquette University)
DESCRIPTION:Extremal questions for vertex colorings of graphs\n\nFor graphs $G$ and $H$\, an $H$-coloring of $G$ is a map from the vertices of $G$ to the vertices of $H$ so that an edge in $G$ is mapped to an edge in $H$.  The graph $H$ can be thought of as the allowable coloring scheme: its vertices are the colors used and its edges indicating colors that can appear on the endpoints of an edge in $G$. When the graph $H$ is the complete graph $K_q$\, an $H$-coloring corresponds to a proper vertex coloring of $G$ with $q$ colors; when $H$ is an edge with one looped endvertex\, an $H$-coloring corresponds to an independent set in $G$.After familiarizing ourselves with the notion of an $H$-coloring\, we will consider the following extremal graph theory question: given a family of graphs and an $H$\, which graph in the family has the most number of $H$-colorings\, and which has the least number of $H$-colorings?  We will discuss some things that are known (and not known!) in a variety of families\, including trees and graphs with a fixed minimum degree.\n\n\nJoin Zoom Meeting: link\n\n\n 
URL:https://aarms.math.ca/event/atlantic-graph-theory-seminar-john-engbers-marquette-university/
LOCATION:Zoom seminar
CATEGORIES:AARMS Atlantic Graph Theory Seminar
ORGANIZER;CN="Jason Brown":MAILTO:jason.brown@dal.ca
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20220412T110000
DTEND;TZID=UTC:20220412T120000
DTSTAMP:20260613T060312
CREATED:20220411T164445Z
LAST-MODIFIED:20220411T164445Z
UID:6644-1649761200-1649764800@aarms.math.ca
SUMMARY:AARMS Scientific Machine Learning Seminar: Michael W. Dunham (Department of Earth Sciences\, Memorial University)
DESCRIPTION:Semisupervised machine learning algorithms and their application to geoscience classification problems\n\nIn 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 in data and assign repetitive patterns to similar categories. This process automates pattern recognition of data and allows meaningful information to be extracted in an efficient manner.\n\n\nFor many machine learning problems\, there are sufficient data to train a wide range of algorithms. Some applications\, such as image classification and speech recognition\, have large training datasets readily available. However\, in several geoscience-related problems\, labeled data are generally obtained by sampling the earth in some manner (e.g.\, drilling wells\, field sampling\, etc.)\, which is not trivial due to cost and logistical factors. As such\, many earth science-related machine learning problems have limited training data. Supervised machine learning algorithms are prone to overfitting in scarce training data situations\, but semisupervised approaches are designed for these problems because the unlabelled data are also used to inform the learning process.\n\nThree geoscience applications inherently challenged with limited training data are well log classification\, seismic classification\, and bedrock lithology mapping. I apply various semisupervised algorithms to these three geoscience problems and determine if semisupervised algorithms can perform better than supervised methods and under what conditions\, if applicable. The semisupervised methods I consider are self-training\, label propagation\, and semisupervised Gaussian mixture models. I consider several supervised methods in my work\, but the most prevalent are gradient boosting decision tree methods (e.g.\, XGBoost\, LightGBM). The results show that semisupervised methods can outperform their supervised counterparts for each of the geoscience applications\, but there are situations where this is not always the case. Nonetheless\, semisupervised methods are rarely considered for many geoscience disciplines\, which is supported by the lack of published examples in the literature. The outcomes of this work help fill this gap\, but they also help raise the awareness of semisupervised methods.\n\n\nWebex link:\n\nhttps://mun.webex.com/mun/j.php?MTID=mf0e24b554219c531763a22ffce2e82c9
URL:https://aarms.math.ca/event/aarms-scientific-machine-learning-seminar-michael-w-dunham-department-of-earth-sciences-memorial-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:20220413T153000
DTEND;TZID=UTC:20220413T163000
DTSTAMP:20260613T060312
CREATED:20220411T113741Z
LAST-MODIFIED:20220411T113741Z
UID:6641-1649863800-1649867400@aarms.math.ca
SUMMARY:Atlantic Graph Theory Seminar: Aysel Erey (Gebze Technical University\, Turkey)
DESCRIPTION:Graph polynomials\n\nIn this talk\, I will discuss various aspects of several graph polynomials such as the location of their roots\, their combinatorial properties and extremal questions.\n\nJoin Zoom Meeting: link
URL:https://aarms.math.ca/event/atlantic-graph-theory-seminar-aysel-erey-gebze-technical-university-turkey/
LOCATION:Zoom seminar
CATEGORIES:AARMS Atlantic Graph Theory Seminar
ORGANIZER;CN="Jason Brown":MAILTO:jason.brown@dal.ca
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