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Friday, April 5, 2019 10:00 am - 10:00 am EDT (GMT -04:00)

Master’s Thesis Presentation: End-to-end Neural Information Retrieval

Wei Yang, Master’s candidate
David R. Cheriton School of Computer Science

In recent years, we have witnessed many successes of neural networks in the information retrieval community with lots of labeled data. Yet it remains unknown whether the same techniques can be easily adapted to search social media posts where the text is much shorter. In addition, we find that most neural information retrieval models are compared against weak baselines. 

In this thesis, we build an end-to-end neural information retrieval system using two toolkits: Anserini and MatchZoo. In addition, we also propose a novel neural model to capture the relevance of short and varied tweet text, named MP-HCNN. 

Thursday, April 11, 2019 10:30 am - 10:30 am EDT (GMT -04:00)

AI Seminar: Voting Games: Trembling Hand Equilibria

Svetlana Obraztsova
Nanyang Technological University

Traditionally, computational social choice focuses on evaluating voting rules by their resistance to strategic behaviours, and uses computational complexity as a barrier to them. In contrast, recent works (counting from 2010) take another natural approach and analyse voting scenarios from a game-theoretic perspective, viewing strategic parties as players and examining possible stable outcomes of their interaction (i.e., equilibria). The main problem of this approach is multiple unrealistic Nash equilibria. Fortunately, several refinements have been developed that allow to filter out some undesirable Nash Equilibria. 

Thursday, April 25, 2019 1:00 pm - 1:00 pm EDT (GMT -04:00)

PhD Defence: Dynamical Systems in Spiking Neuromorphic Hardware

Aaron Voelker, PhD candidate
David R. Cheriton School of Computer Science

Dynamical systems are universal computers. They can perceive stimuli, remember, learn from feedback, plan sequences of actions, and coordinate complex behavioural responses. The Neural Engineering Framework (NEF) provides a general recipe to formulate models of such systems as coupled sets of nonlinear differential equations and compile them onto recurrently connected spiking neural networks — akin to a programming language for spiking models of computation. The Nengo software ecosystem supports the NEF and compiles such models onto neuromorphic hardware. 

Thursday, April 25, 2019 1:00 pm - 1:00 pm EDT (GMT -04:00)

PhD Seminar: Automating the Intentional Encoding of Human-Designable Markers

Joshua Jung, PhD candidate
David R. Cheriton School of Computer Science

Recent work established that it is possible for human artists to encode information into hand-drawn markers, but it is difficult to do when simultaneously maintaining aesthetic quality. We present two methods for relieving the mental burden associated with encoding, while allowing an artist to draw as freely as possible. 

Friday, May 17, 2019 11:00 am - 11:00 am EDT (GMT -04:00)

Master’s Essay Presentation: Applications of Deconvolution Network, SPN and ELMo

Joshua Cheng, Master’s candidate
David R. Cheriton School of Computer Science

In this paper, we are going to explore the possibility to apply deconvolution network, sum-product network and contextualized word embeddings (ELMo) on learning encoded sentence representation and sentiment identification.

Yingluo Xun, Master’s candidate
David R. Cheriton School of Computer Science

In reinforcement learning, entropy-regularized value function (in policy space) has attracted a lot of attention recently due to its effect on smoothing the value function, and the effect on encouraging exploration. However, there is a discrepancy between the regularized objective function and the original objective function in existing methods, which would potentially result in a discrepancy between the trained policy and the optimal policy, as the policy directly depends on the value function in the reinforcement learning framework. 

Wednesday, May 29, 2019 11:30 am - 11:30 am EDT (GMT -04:00)

PhD Defence: Theoretical Foundations for Efficient Clustering

Shrinu Kushagra, PhD candidate
David R. Cheriton School of Computer Science

Clustering aims to group together data instances which are similar while simultaneously separating the dissimilar instances. The task of clustering is challenging due to many factors. The most well-studied is the high computational cost. The clustering task can be viewed as an optimization problem where the goal is to minimize a certain cost function (like k-means cost or k-median cost). Not only are the minimization problems NP-Hard but often also NP-Hard to approximate (within a constant factor). There are two other major issues in clustering, namely under-specificity and noise-robustness. 

Wednesday, June 5, 2019 4:00 pm - 4:00 pm EDT (GMT -04:00)

PhD Seminar: Density Estimation using Sum-of-Squares Polynomial Flows

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.

Wednesday, June 26, 2019 10:00 am - 10:00 am EDT (GMT -04:00)

Master’s Thesis Presentation: Applying Fair Reward Divisions to Collaborative Work

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.