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DTSTART:20190310T070000
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DTSTART;TZID=America/Toronto:20191206T140000
SEQUENCE:0
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URL:https://uwaterloo.ca/artificial-intelligence-group/events/phd-defence-l
 ikelihood-based-density-estimation-using-deep
LOCATION:DC - William G. Davis Computer Research Centre 200 University Aven
 ue West 2310 Waterloo ON N2L 3G1 Canada
SUMMARY:PhD Defence: Likelihood-based Density Estimation using Deep\nArchit
 ectures
CLASS:PUBLIC
DESCRIPTION:PRIYANK JAINI\, PHD CANDIDATE\n_David R. Cheriton School of Com
 puter Science_\n\nMultivariate density estimation is a central problem in 
 unsupervised\nmachine learning that has been studied immensely in both sta
 tistics\nand machine learning. Several methods have thus been proposed for
 \ndensity estimation including classical techniques like histograms\,\nker
 nel density estimation methods\, mixture models\, and more recently\nneura
 l density estimation that leverages the recent advances in deep\nlearning 
 and neural networks to tractably represent a density\nfunction. In today'
 s age when large amounts of data are being\ngenerated in almost every fiel
 d it is of paramount importance to\ndevelop density estimation methods tha
 t are cheap both computationally\nand in memory cost. The main contributio
 n of this thesis is in\nproviding a principled study of parametric density
  estimation methods\nusing mixture models and triangular maps for neural d
 ensity\nestimation. 
DTSTAMP:20260315T094950Z
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