Year: 2020
Type: dissertation
Abstract: There has been a shift of attention in the AI research where people gradually abandon traditional statistical models in favor of deep neural architectures. While effective in learning input-output mappings from two arbitrary distributions, the complex nature of neural models makes them hard to interpret. In this thesis, we introduce a more interpretable hierarchical bigram (HiBi) model, which is extended based on the simple bigram language model. It contains a few components inspired by theories of human cognition, and has been shown through experiments to be effective in learning meaningful representation from sequential inputs without any labeling. We hope that HiBi could be a starting point to develop more complex cognitive models that are both interpretable and effective for representation learning.
Author: Wang, Xiaoyan
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7000747272