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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.

Monday, August 13, 2018 10:00 am - 10:00 am EDT (GMT -04:00)

Master’s Thesis Presentation • Data Systems — Serverless Data Analytics with Flint

Youngbin Kim, Master’s candidate
David R. Cheriton School of Computer Science

AbstractServerless architectures organized around loosely-coupled function invocations represent an emerging design for many applications. Recent work mostly focuses on user-facing products and event-driven processing pipelines. 

Pak Hay Chan, Master’s candidate
David R. Cheriton School of Computer Science

We consider a new problem of designing a network with small $s$-$t$ effective resistance. In this problem, we are given an undirected graph $G=(V,E)$ where each edge $e$ has a cost $c_e$ and a resistance $r_e$, two designated vertices $s,t \in V$, and a cost budget $k$.

Taylor Hornby, Master’s candidate
David R. Cheriton School of Computer Science

This thesis contributes to two areas. The first is the study of parallel repetition theorems and concentration bounds for nonlocal games and quantum interactive proofs. 

We make the following contributions: