Chengyao Fu, Master’s candidate
David R. Cheriton School of Computer Science
Supervisors: Professors Alan Huang and Yuying Li
Sentiment analysis has been widely used in the domain of finance. There are two most common textual sentiment analysis methods in finance: \textit{dictionary-based approach} and \textit{machine learning approach}.
Neil Budnarain, Master’s candidate
David R. Cheriton School of Computer Science
Supervisor: Professor Jesse Hoey
Yao Lu, Master’s candidate
David R. Cheriton School of Computer Science
Supervisor: Professor Olga Vechtomova
Vikash Balasubramanian, Master’s candidate
David R. Cheriton School of Computer Science
Learning useful representations of data is a crucial task in machine learning with wide ranging applications. In this thesis we explore improving representations of models based on variational inference by improving the posterior.
Gaurav Sahu, Master’s candidate
David R. Cheriton School of Computer Science
Effective fusion of data from multiple modalities, such as video, speech, and text, is a challenging task due to the heterogeneous nature of multimodal data. In this work, we propose fusion techniques that aim to model context from different modalities effectively. Instead of defining a deterministic fusion operation, such as concatenation, for the network, we let the network decide how to combine given multimodal features more effectively.
Daniel Tamming, Master’s candidate
David R. Cheriton School of Computer Science
Thanks to increases in computing power and the growing availability of large datasets, neural networks have achieved state of the art results in many natural language processing (NLP) and computer vision (CV) tasks. These models require a large number of training examples that are balanced between classes, but in many application areas they rely on training sets that are either small or imbalanced, or both. To address this, data augmentation has become standard practice in CV. This research is motivated by the observation that, relative to CV, data augmentation is underused and understudied in NLP.
Wei Sun, Master’s candidate
David R. Cheriton School of Computer Science
Predictive Coding is a hierarchical model of neural computation that approximates backpropagation using only local computations and local learning rules. An important aspect of Predictive Coding is the presence of feedback connections between layers. These feedback connections allow Predictive Coding networks to potentially be generative as well as discriminative. However, Predictive Coding networks trained on supervised classification tasks cannot generate accurate input samples close to the training inputs from the class vectors alone.
Nalin De Zoysa, Master’s candidate
David R. Cheriton School of Computer Science
GitHub is a collaborative platform that is used primarily for the development of software. In order to gain more insight into how teams work on GitHub, we wish to analyze the sentiment content available via communication on the platform.
Gaurav Gupta, Master’s candidate
David R. Cheriton School of Computer Science
We propose a mechanism for achieving cooperation and communication in Multi-Agent Reinforcement Learning (MARL) settings by intrinsically rewarding agents for obeying the commands of other agents. At every timestep, agents exchange commands through a cheap-talk channel. During the following timestep, agents are rewarded both for taking actions that conform to commands received as well as for giving successful commands. We refer to this approach as obedience-based learning.
Alexandre Parmentier, Master’s candidate
David R. Cheriton School of Computer Science
This thesis presents two works with the shared goal of improving the capacity of multiagent trust modeling to be applied to social networks.