Events

Speaker: Carolyn Lamb, PhD Candidate

Artists, hobbyists, cognitive scientists, and computer scientists are all trying their hands at writing computer programs that generate poetry. Why? What do these poems look like, and how are they made? What might meta-poetry writers on both sides of the art-science spectrum learn from each other?

Speaker: Deepak Rishi, Master's Candidate

Sentiment and emotional analysis on online collaborative software development forums can be very useful to gain important insights into the behaviours and personalities of the developers. Such information can later on be used to increase productivity of developers by making recommendations on how to behave best in order to get a task accomplished. However, due to the highly technical nature of the data present in online collaborative software development forums, mining sentiments and emotions becomes a very challenging task.

Speaker: Valerie Platsko, Master's Candidate

Smart meter technology allows frequent measurements of water consumption at a household level. This greater availability of data allows improved analysis of patterns of residential water consumption, which is important for demand management and targeting conservation efforts. The dataset in this thesis includes 8,000 single family residences in Abbotsford, British Columbia from 2012 to 2013, and contains hourly measurements of water consumption recorded by smart meters installed in 2010. This work focuses on identifying outdoor consumption due to its contribution to peak demand during the summer, which is important because of concerns about strain on infrastructure in Abbotsford.

Speaker: Marta Kryven, PhD Candidate

Human beings quickly and confidently attribute more or less intelligence to one another. What is meant by intelligence when they do so? And what are the surface features of human behavior that determine their judgments? Because the judges of success or failure in the quest for 'artificial intelligence' will be human, the answers to such questions are an essential part of cognitive science. This thesis studies such questions in the context of a maze world, complex enough to require non-trivial answers, and simple enough to analyze the answers in term of decision-making algorithms.

Speaker: Ricardo Salmon, PhD Candidate

Stochastic satisfiability (SSAT) problems are an extension of SAT problems that is PSPACE-complete and in the same complexity class as quantified Boolean formula (QBF) and partially observable Markov decision processes (POMDPs).

Speaker: James Wright, Microsoft Research

In order to do a good job of interacting with people, a system must have an adequate model of how people will react to its actions. This is particularly true in strategic settings: settings that contain multiple agents, each with their own goals and priorities, in which each agent's ability to accomplish their goals depends partly on the actions of the other agents. Standard models of strategic behavior assume that the participants are perfectly rational. However, a wealth of experimental evidence shows that not only do human agents fail to behave according to these models, but that they frequently deviate from these models' predictions in a predictable, systematic way.

Speaker: Haifeng Xu, University of Southern California

Strategic interactions among self-interested agents (a.k.a., games) are ubiquitous, ranging from economic activity in daily life and the Internet to defender-adversary interactions in national security.  A key variable influencing agents' strategic decision making is the information they have available about their environment as well as the preferences and actions of others. In this talk, I will describe my work on computational questions pertaining to the role of information in games.

Speaker: Daniel Recoskie, PhD candidate

We propose a method for learning wavelet filters directly from data. We accomplish this by framing the discrete wavelet transform as a modified convolutional neural network. We introduce an autoencoder wavelet transform network that is trained using gradient descent.

Speaker: Feng-Xuan Choo, PhD candidate

Building large-scale brain models is one method used by theoretical neuroscientists to understand the way the human brain functions. Researchers typically use either a bottom-up approach, which focuses on the detailed modelling of various biological properties of the brain and places less importance on reproducing functional behaviour, or a top-down approach, which generally aim to reproduce the behaviour observed in real cognitive agents, but typically sacrifices adherence to constraints imposed by the neuro-biology. 

The focus of this thesis is Spaun, a large-scale brain model constructed using a combination of the bottom-up and top-down approaches to brain modelling. Spaun is currently the world's largest functional brain model, capable of performing 8 distinct cognitive tasks ranging from digit recognition to inductive reasoning. The thesis is organized to discuss three aspects of the Spaun model.

Speaker: Zhucheng Tu, Master's Candidate

Modelling the similarity of two sentences is an important problem in natural language processing and information retrieval, with applications in tasks such as paraphrase identification and answer selection in question answering. The Multi-Perspective Convolutional Neural Network (MP-CNN) is a model that improved previous state-of-the-art models in 2015 and has remained a popular model for sentence similarity tasks. However, until now, there has not been a rigorous study of how the model actually achieves competitive accuracy.