Master’s Thesis Presentation • Artificial Intelligence — Disentangled Representation Learning for Stylistic Variation in Neural Language Models
Vineet John, Master’s candidate
David R. Cheriton School of Computer Science
This thesis tackles the problem of disentangling the latent style and content variables in a language modelling context. This involves splitting the latent representations of documents by learning which features of a document are discriminative of its style and content, and encoding these features separately using neural network models.
CrySP Speaker Series on Privacy — When A Small Leak Sinks A Great Ship: Deanonymizing Tor Hidden Service Users Through Bitcoin Transactions Analysis
Aiman Erbad, Computer Science and Engineering Department