Wednesday, August 21, 2019 — 3:00 PM EDT

Hamidreza Shahidi, Master’s candidate
David R. Cheriton School of Computer Science

A number of researchers have recently questioned the necessity of increasingly complex neural network (NN) architectures. In particular, several recent papers have shown that simpler, properly tuned models are at least competitive across several natural language processing (NLP) tasks. 

Friday, August 16, 2019 — 2:30 PM EDT

Aman Jhunjhunwala, Master’s candidate
David R. Cheriton School of Computer Science

Recently, deep neural networks have been capable of solving complex control tasks in certain challenging environments. However, these deep learning policies continue to be hard to interpret, explain and verify, which limits their practical applicability. Decision Trees lend themselves well to explanation and verification tools but are not easy to train especially in an online fashion. The aim of this thesis is to explore online tree construction algorithms and demonstrate the technique and effectiveness of distilling reinforcement learning policies into a Bayesian tree structure.

Friday, August 16, 2019 — 1:30 PM EDT

Alexander Sachs, Master’s candidate
David R. Cheriton School of Computer Science

GitHub is an excellent democratic source of software. Unlike traditional work groups however, GitHub repositories are primarily anonymous and virtual. Traditional strategies for improving the productivity of a work group often include external consultation agencies that do in-person interviews. The resulting data from these interviews are then reviewed and their recommendations provided. In the online world however, where colleagues are often anonymous and geographically dispersed, it is often impossible to apply such approaches.

Friday, August 16, 2019 — 10:30 AM EDT

Jonathan Vi Perrie, Master’s candidate
David R. Cheriton School of Computer Science

Over the years, hit song science has been a controversial topic within music information retrieval (MIR). Researchers have debated whether an unbiased dataset can be constructed and what it means to successfully model song performance. Often classes for modelling are derived from one component of song performance, like for example, a song's peak position on some chart. 

Tuesday, August 6, 2019 — 10:00 AM EDT

Rahul Iyer, Master’s candidate
David R. Cheriton School of Computer Science

Social interactions in the form of discussion are an indispensable part of collaborative software development. The discussions are essential for developers to share their views and to form a strong relationship with other teammates. These discussions invoke both positive and negative emotions such as joy, love, aggression, and disgust. Additionally, developers also exhibit hidden behaviors that dictate their personality. Some developers can be supportive and open to new ideas, whereas others can be conservative. Past research has shown that the personality of the developers has a significant role in determining the success of the task they collaboratively perform.

Tuesday, July 23, 2019 — 11:30 AM EDT

Matthew Angus, Master’s candidate
David R. Cheriton School of Computer Science

There exists wide research surrounding the detection of out of distribution sample for image classification. Safety critical applications, such as autonomous driving, would benefit from the ability to localise the unusual objects causing an image to be out of distribution. 

Thursday, July 18, 2019 — 1:30 PM EDT

Paulo Pacheco, PhD candidate
David R. Cheriton School of Computer Science

In this seminar I will discuss some of the current state-of-the-art methods for generating attention maps in weakly supervised (image level annotations) for classification tasks, aiming image segmentation inference and object localization.

Wednesday, July 17, 2019 — 2:00 PM EDT

Guojun Zhang, PhD candidate
David R. Cheriton School of Computer Science

The expectation-maximization (EM) algorithm has been widely used in minimizing the negative log likelihood (also known as cross entropy) of mixture models. However, little is understood about the goodness of the fixed points it converges to. 

Wednesday, June 26, 2019 — 10:00 AM EDT

Greg d’Eon, Master’s candidate
David R. Cheriton School of Computer Science

Collaborative crowdsourcing tasks allow workers to solve more difficult problems than they could alone, but motivating workers in these tasks is complex. In this thesis, we study how to use payments to motivate groups of crowd workers. We leverage concepts from equity theory and cooperative game theory to understand the connection between fair payments and motivation. 

Wednesday, June 5, 2019 — 4:00 PM EDT

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

Triangular map is a recent construct in probability theory that allows one to transform any source probability density function to any target density function. Based on triangular maps, we propose a general framework for high-dimensional density estimation, by specifying one-dimensional transformations (equivalently conditional densities) and appropriate conditioner networks.

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