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Thursday, September 13, 2018 1:30 pm - 1:30 pm EDT (GMT -04:00)

Master’s Thesis Presentation • Artificial Intelligence — Predicting Short-Term Water Consumption for Multi-Family Residences

Irish Medina, Master’s candidate
David R. Cheriton School of Computer Science

Smart water meters have been installed across Abbotsford, British Columbia, Canada, to measure the water consumption of households in the area. Using this water consumption data, we develop machine learning and deep learning models to predict daily water consumption for existing multi-family residences. We also present a new methodology for predicting the water consumption of new housing developments. 

Friday, September 14, 2018 1:00 pm - 1:00 pm EDT (GMT -04:00)

Master’s Thesis Presentation • Scientific Computation — Fast Algorithms for Finding the Characteristic Polynomial of a Rank 2 Drinfeld Module

Yossef Musleh, Master’s candidate
David R. Cheriton School of Computer Science

We introduce a Monte Carlo randomized algorithm for computing the characteristic polynomial of a rank 2 Drinfeld module than runs in $O(n^2 \log n \log \log n \log q)$ field operations. We also introduce a deterministic algorithm that runs in $O(n^{2.6258} \log n + n^2 \log n \log log n \log q)$ field operations. Both approaches are a significant improvement over the current literature.

Friday, September 21, 2018 1:30 pm - 1:30 pm EDT (GMT -04:00)

Master’s Thesis Presentation • Scientific Computation — Bidirectional Learning in Recurrent Neural Networks Using Equilibrium Propagation

Ahmed Khan, Master’s candidate
David R. Cheriton School of Computer Science

Neurobiologically-plausible learning algorithms for recurrent neural networks that can perform supervised learning are a neglected area of study. Equilibrium propagation is a recent synthesis of several ideas in biological and artificial neural network research that uses a continuous-time, energy-based neural model with a local learning rule. However, despite dealing with recurrent networks, equilibrium propagation has only been applied to discriminative categorization tasks.