PhD Defence • Scientific Computation — Optimization Methods for Semi-Supervised Learning
Edward Cheung, PhD candidate
David R. Cheriton School of Computer Science
Edward Cheung, PhD candidate
David R. Cheriton School of Computer Science
Ivana Kajić, PhD candidate
David R. Cheriton School of Computer Science
Bryan Muscedere, Master’s candidate
David R. Cheriton School of Computer Science
Kareem El Gebaly, PhD candidate
David R. Cheriton School of Computer Science
Mohammad Zokaei Ashtiani, PhD candidate
David R. Cheriton School of Computer Science
Alex C. Williams, PhD candidate
David R. Cheriton School of Computer Science
Babar Naveed Memon, Master’s candidate
David R. Cheriton School of Computer Science
Remote Direct Memory Access (RDMA) can be used to implement a shared storage abstraction or a shared nothing abstraction for distributed applications. We argue that the shared storage abstraction is an overkill for loosely coupled applications and that the shared nothing abstraction does not leverage all the benefits of RDMA.
Junnan Chen, Master’s candidate
David R. Cheriton School of Computer Science
Conversations depend on information from the context. To go beyond one-round conversation, a chatbot must resolve contextual information such as: 1) co-reference resolution, 2) ellipsis resolution, and 3) conjunctive relationship resolution.
There are simply not enough data to avoid these problems by trying to train a sequence-to-sequence model for multi-round conversation similar to that of one-round conversation.
Yifan Zhang, Master’s candidate
David R. Cheriton School of Computer Science
Dallas Fraser, Master’s candidate
David R. Cheriton School of Computer Science
Combining text and mathematics when searching in a corpus with extensive mathematical notation remains an open problem. Recent results for math information retrieval systems on the math and text retrieval task at NTCIR-12, for example, show room for improvement, even though formula retrieval appears to be fairly successful.