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Thursday, April 25, 2019 1:00 pm - 1:00 pm EDT (GMT -04:00)

PhD Defence: Dynamical Systems in Spiking Neuromorphic Hardware

Aaron Voelker, PhD candidate
David R. Cheriton School of Computer Science

Dynamical systems are universal computers. They can perceive stimuli, remember, learn from feedback, plan sequences of actions, and coordinate complex behavioural responses. The Neural Engineering Framework (NEF) provides a general recipe to formulate models of such systems as coupled sets of nonlinear differential equations and compile them onto recurrently connected spiking neural networks — akin to a programming language for spiking models of computation. The Nengo software ecosystem supports the NEF and compiles such models onto neuromorphic hardware. 

Wednesday, May 29, 2019 11:30 am - 11:30 am EDT (GMT -04:00)

PhD Defence: Theoretical Foundations for Efficient Clustering

Shrinu Kushagra, PhD candidate
David R. Cheriton School of Computer Science

Clustering aims to group together data instances which are similar while simultaneously separating the dissimilar instances. The task of clustering is challenging due to many factors. The most well-studied is the high computational cost. The clustering task can be viewed as an optimization problem where the goal is to minimize a certain cost function (like k-means cost or k-median cost). Not only are the minimization problems NP-Hard but often also NP-Hard to approximate (within a constant factor). There are two other major issues in clustering, namely under-specificity and noise-robustness. 

Wednesday, November 27, 2019 10:00 am - 10:00 am EST (GMT -05:00)

PhD Defence: Emotion-Aware and Human-Like Autonomous Agents

Nabiha Asghar, PhD candidate
David R. Cheriton School of Computer Science

In human-computer interaction (HCI), one of the technological goals is to build human-like artificial agents that can think, decide and behave like humans during the interaction. A prime example is a dialogue system, where the agent should converse fluently and coherently with a user and connect with them emotionally. Humanness and emotion-awareness of interactive artificial agents have been shown to improve user experience and help attain application-specific goals more quickly. However, achieving human-likeness in HCI systems is contingent on addressing several philosophical and scientific challenges. In this thesis, I address two such challenges: replicating the human ability to 1) correctly perceive and adopt emotions, and 2) communicate effectively through language.

Friday, December 6, 2019 2:00 pm - 2:00 pm EST (GMT -05:00)

PhD Defence: Likelihood-based Density Estimation using Deep Architectures

Priyank Jaini, PhD candidate
David R. Cheriton School of Computer Science

Multivariate density estimation is a central problem in unsupervised machine learning that has been studied immensely in both statistics and machine learning. Several methods have thus been proposed for density estimation including classical techniques like histograms, kernel density estimation methods, mixture models, and more recently neural density estimation that leverages the recent advances in deep learning and neural networks to tractably represent a density function. In today's age when large amounts of data are being generated in almost every field it is of paramount importance to develop density estimation methods that are cheap both computationally and in memory cost. The main contribution of this thesis is in providing a principled study of parametric density estimation methods using mixture models and triangular maps for neural density estimation.