Events

Speaker: Amir-Hossein Karimi, Master’s candidate

The story of this work is dimensionality reduction. Dimensionality reduction is a method that takes as input a point-set P of n points in \(R^d\) where d is typically large and attempts to find a lower-dimensional representation of that dataset, in order to ease the burden of processing for down-stream algorithms. In today’s landscape of machine learning, researchers and practitioners work with datasets that either have a very large number of samples and/or include high-dimensional samples. Therefore, dimensionality reduction is applied as a pre-processing technique primarily to overcome the curse of dimensionality.

Speaker: Junnan Chen, Master’s candidate

Conversations depend on information from the context. To go beyond one-round conversation, a chatbot must resolve contextual information such as: 1) co-reference resolution, 2) ellipsis resolution, and 3) conjunctive relationship resolution.

Michael Cormier, PhD candidate

This thesis is focused on the development of computer vision techniques for parsing web pages using an image of the rendered page as evidence, and on understanding this under-explored class of images from the perspective of computer vision. This project is divided into two tracks — applied and theoretical — which complement each other. Our practical motivation is the application of improved web page parsing to assistive technology, such as screenreaders for visually impaired users or the ability to declutter the presentation of a web page for those with cognitive deficit. From a more theoretical standpoint, images of rendered web pages have interesting properties from a computer vision perspective; in particular, low-level assumptions can be made in this domain, but the most important cues are often subtle and can be highly non-local. The parsing system developed in this thesis is a principled Bayesian segmentation-classification pipeline, using innovative techniques to produce valuable results in this challenging domain. The thesis includes both implementation and evaluation solutions.

Royal Sequiera, Master’s candidate
David R. Cheriton School of Computer Science

With the advent of deep learning methods, researchers are abandoning decades-old work in Natural Language Processing (NLP). The research community has been increasingly moving away from otherwise dominant feature engineering approaches; rather, it is gravitating towards more complicated neural architectures. Highly competitive tools like Parts-of-Speech taggers that exhibit human-like accuracy are traded for complex networks, with the hope that the neural network will learn the features needed. In fact, there have been efforts to do NLP "from scratch" with neural networks that altogether eschew featuring engineering based tools (Collobert et al., 2011).

Vineet John, Master’s candidate
David R. Cheriton School of Computer Science

This thesis tackles the problem of disentangling the latent style and content variables in a language modelling context. This involves splitting the latent representations of documents by learning which features of a document are discriminative of its style and content, and encoding these features separately using neural network models.

Ricardo Salmon, PhD candidate
David R. Cheriton School of Computer Science

Stochastic satisfiability (SSAT), Quantified Boolean Satisfiability (QBF) and decision theoretic planning in infinite horizon partially observable Markov decision processes (POMDPs) are all PSPACE-Complete problems. Since they are all complete for the same complexity class, I show how to convert them into one another in polynomial time and space.

Irish Medina, Master’s candidate
David R. Cheriton School of Computer Science

Smart water meters have been installed across Abbotsford, British Columbia, Canada, to measure the water consumption of households in the area. Using this water consumption data, we develop machine learning and deep learning models to predict daily water consumption for existing multi-family residences. We also present a new methodology for predicting the water consumption of new housing developments. 

Nicole McNabb, Master’s candidate
David R. Cheriton School of Computer Science

Clustering is the task of partitioning data so that “similar” points are grouped together and “dissimilar” ones are separated. In general, this is an ill-defined task. One way to make clustering well-defined is to introduce a clustering objective to optimize. While many common objectives such as k-means are known to be NP-hard, heuristics output “nice” clustering solutions efficiently in practice. This work analyzes two avenues of theoretical research that attempt to explain this discrepancy.

Carolyn Lamb, PhD candidate
David R. Cheriton School of Computer Science

This thesis is driven by the question of how computers can generate poetry, and how that poetry can be evaluated. We survey existing work on computer-generated poetry and interdisciplinary work on how to evaluate this type of computer-generated creative product.