Events

Tuesday, August 14, 2018 — 4:00 PM EDT

Abdullah Rashwan, PhD candidate
David R. Cheriton School of Computer Science

We present a discriminative learning algorithm for Sum-Product Networks (SPNs) based on the Extended Baum-Welch (EBW) algorithm.

Thursday, August 9, 2018 — 10:00 AM EDT

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.

Friday, August 3, 2018 — 11:00 AM EDT

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).

Tuesday, July 24, 2018 — 2:00 PM EDT

Daniel Recoskie, PhD candidate
David R. Cheriton School of Computer Science

The wavelet transform has seen success when incorporated into neural network architectures, such as in wavelet scattering networks. More recently, it has been shown that the dual-tree complex wavelet transform can provide better representations than the standard transform.

Friday, June 29, 2018 — 10:00 AM EDT

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.

Monday, June 25, 2018 — 4:00 PM EDT

Abdullah Rashwan, PhD candidate

Sum-product networks have recently emerged as an attractive representation due to their dual view as a special type of deep neural network with clear semantics and a special type of probabilistic graphical model for which inference is always tractable. Those properties follow from some conditions (i.e., completeness and decomposability) that must be respected by the structure of the network. 

Thursday, June 21, 2018 — 9:00 AM EDT

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.

Tuesday, June 12, 2018 — 3:00 PM EDT

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

At their core, many unsupervised learning models provide a compact representation of homogeneous density mixtures, but their similarities and differences are not always clearly understood. In this work, we formally establish the relationships among latent tree graphical models (including special cases such as hidden Markov models and tensorial mixture models), hierarchical tensor formats and sum-product networks.

Tuesday, June 5, 2018 — 12:00 PM EDT

Nisarg Shah, Department of Computer Science
University of Toronto

Algorithms are increasingly making decisions that affect humans. The field of computational social choice deals with algorithms for eliciting individual preferences and making collective decisions. Everyday examples of such decisions include citizens electing their representatives, roommates dividing collectively purchased items, or residents voting over allocation of city's budget. Making reasonable collective decisions requires viewing the problem through the lenses of elicitation, fairness, efficiency, incentives, and ethics.

Friday, May 11, 2018 — 2:00 PM EDT

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.

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