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Towards Model Understanding

Series: Department Seminar

Speaker: Danish Pruthi, Carnegie Mellon University, Pittsburgh, USA

Date/Time: Nov 10 08:00:00

Location: Online Meeting MS Teams

Faculty Advisor:

Abstract:
While deep learning models have become increasingly accurate over the last decade, concerns about their (lack of) interpretability have taken a center stage. In response, a growing subfield on interpretability and analysis of these models has emerged. Interpretability is an umbrella term encompassing efforts to understand the learned models and communicate that understanding to the stakeholders. In this talk, I will share our research towards these goals and first highlight methods that aid user understanding, and then, focus on protocols to evaluate model explanations—a fundamental issue facing much of interpretability research.
Online Teams Meeting Link:
https://teams.microsoft.com/l/meetup-join/19%3ameeting_NDljNWVlMTYtYjIzYy00MGM0LWE2YzgtMjBmMDNiYjhhMzJm%40thread.v2/0?context=%7b%22Tid%22%3a%226f15cd97-f6a7-41e3-b2c5-ad4193976476%22%2c%22Oid%22%3a%224bcd3d56-e405-4b06-99fb-27742262f261%22%7d

Speaker Bio:
Danish Pruthi is a Ph.D. student in the School of Computer Science at Carnegie Mellon University, advised by Graham Neubig and Zachary Lipton. He is broadly interested in the areas of natural language processing and deep learning, with a focus on model interpretability. He completed his bachelors degree in computer science from BITS Pilani, Pilani in 2015. He has spent time doing research at Google AI, Facebook AI Research, IISc Bangalore and Microsoft Research. He is also a recipient of the Siebel Scholarship and the CMU Presidential Fellowship. His legal name is only Danish—a cause of airport quagmires and, in equal parts, funny anecdotes.

Host Faculty: R. Govindarajan