Faculty

Speaker: Bowen Hui, University of British Columbia, Okanagan Campus

There has been much discussion on transforming the learning environment for higher education institutions from the perspective of educators. In this talk, I will outline what students report as their priorities in a personalized learning environment.

Speaker: Hassan Ashtiani, PhD Candidate

We consider PAC learning of probability distributions (a.k.a. density estimation), where we are given an i.i.d. sample generated from an unknown target distribution, and want to output a distribution that is close to the target in total variation distance.

Speaker: Nabiha Asghar, PhD Candidate

We propose an online, end-to-end, neural generative conversational model for open-domain dialogue. It is trained using a unique combination of offline two-phase supervised learning and online human-in-the-loop active learning.

Speaker: Ali Wytsma, Master's Candidate

The naive Bayes model is a simple model that has been used for many decades, often as a baseline, for both supervised and unsupervised learning. With a latent class variable it is one of the simplest latent variable models, and is often used for clustering. The estimation of its parameters by maximum likelihood (e.g., gradient ascent, expectation maximization) is subject to local optima since the objective is non-concave. However, the conditions under which global optimality can be guaranteed are currently unknown.

Speaker: Michael Cormier, PhD Candidate

In this talk I will present recent advances in our vision-based web page segmentation method. This method is an edge-based, Bayesian image segmentation algorithm, capable of estimating the semantic structure of a web page from an image of the page.

Speaker: Areej Alhothali, PhD Candidate

Natural language texts are often meant to express or impact individuals emotions. Sentiment analysis researchers are increasingly interested in investigating natural language processing techniques as well as emotions theories to classify sentiments expressed in natural language text. Most sentiment analysis research effort focuses on classifying highly opinionated documents from the writer's perspectives and uses either count-based word representations that ignore sentence structure or a small set of lexicon resources that do not cover the wide range of words used on the Internet.

Speaker: Mohammed Alliheedi, PhD Candidate

Over the past decade, the focus on argumentation mining has been growing significantly in different areas of Artificial Intelligence (AI) research. The incentive to build Natural Language Processing (NLP) systems to automatically identify and analyze argumentative components in various genres of texts has increased because knowledge of argumentative structure facilitates various tasks such as text summarization and opinion mining for commercial purposes.

Speaker: Carolyn Lamb, PhD Candidate

Computer software has frequently been used to perform creative tasks, such as generating artwork or discovering new mathematical proofs. But if a computer is intended to be creative, how do we measure its performance or decide if it is doing a good job?

Speaker: Mariah Shein, Master's Candidate

The development of the field of reinforcement learning was based on psychological studies of the instrumental conditioning of humans and other animals. Recently, reinforcement learning algorithms have been applied to neuroscience to help characterize neural activity and animal behaviour in instrumental conditioning tasks. A specific example is the hybrid learner developed to match human behaviour on a two-stage decision task. This hybrid learner is composed of a model-free and a model-based system.

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?