Seminar

Speaker: Areej Alhothali, PhD Candidate

We propose an extension to graph-based sentiment lexicon induction methods by incorporating distributed and semantic word representations in building the similarity graph to expand a three-dimensional sentiment lexicon. We also present a comprehensive evaluation of four graph-based propagation approaches using different word representations and similarity metrics.

Speaker: Priyank Jaini, PhD

We consider the problem of inferring a sequence of hidden states associated with a sequence of observations produced by an individual within a population. Instead of learning a single sequence model for the population (which does not account for variations within the population), we learn a set of basis sequence models based on different individuals.

Speaker: Aaron R. Voelker, PhD Candidate

One of the central challenges in neuroscience involves understanding how dynamic stimuli can be processed by neural mechanisms to drive behavior. By synthesizing models from neuroscience with tools from signal processing and control theory, we derive the feedback weights required to represent a low-frequency input signal by delaying it along a low-dimensional manifold, using a recurrently connected network of biologically plausible spiking neurons. This is shown to nonlinearly encode the continuous-time history of an input signal by optimally compressing it into a low-dimensional subspace.

Speaker: Milad Khaki, PhD Candidate

The project involves the analysis of procurement auctions for water infrastructure maintenance projects for Cities of Niagara Falls and London. The primary concern of this proposal is to generate a reliable mechanism for importing different formats of the tender summaries, provided as input data, of the projects and unify them in a database for further exploration and processing.

Speaker: Ivana Kajic, PhD Candidate

The semantic fluency task has been used to understand the effects of semantic relationships on human memory search. A variety of computational models have been proposed that explain human behavioral data, yet it remains unclear how millions of spiking neurons work in unison to realize the cognitive processes involved in memory search.

Speaker: Areej Alhothali, PhD Candidate

We propose an affect control theory-based (ACT) model to predict the temporal progression of the interactants' emotions and their optimal behavior over a sequence of interactions. ACT has a solid foundation in sociology to interpret, understand, and predict human social interactions. In this study, we extend the sociological mathematical model in ACT by learning and incorporating parameters from real data.

Speaker: Bowen Hui, University of British Columbia, Okanagan Campus

There has been much discussion on transforming the learning environment for higher education institutions from the perspective of educators. In this talk, I will outline what students report as their priorities in a personalized learning environment.

Speaker: Hassan Ashtiani, PhD Candidate

We consider PAC learning of probability distributions (a.k.a. density estimation), where we are given an i.i.d. sample generated from an unknown target distribution, and want to output a distribution that is close to the target in total variation distance.

Speaker: Nabiha Asghar, PhD Candidate

We propose an online, end-to-end, neural generative conversational model for open-domain dialogue. It is trained using a unique combination of offline two-phase supervised learning and online human-in-the-loop active learning.

Speaker: Michael Cormier, PhD Candidate

In this talk I will present recent advances in our vision-based web page segmentation method. This method is an edge-based, Bayesian image segmentation algorithm, capable of estimating the semantic structure of a web page from an image of the page.