Location: CS 402

Date: Oct 2, 2019

Time: 3 - 4pm

Title: Diverse Decoding from Neural Sequence Models

Abstract: Consider assistive technology like Google’s auto response that suggests replies with multiple intents. The ability to produce multiple diverse outputs is an integral component of such assistive systems and further, the fundamental problem of producing diverse outputs shows up across domains. In this talk, I will first introduce Diverse Beam Search (DBS) — a modification to the popular Beam Search algorithm that encourages diversity in the sequences decoded from neural sequence models. Then, I will present 𝛁BS, a fully-differentiable variant that directly models sets of sequences while optimizing more general set-level metrics including diversity. While both algorithms are task-agnostic, I will discuss their effectiveness in the context of image captioning.

Bio: Ashwin Kalyan is a PhD student at Georgia Tech advised by Prof. Dhruv Batra. He is interested in developing machine learning solutions for producing diverse outputs, developing fast and accurate reasoning systems and modeling human preferences — all essential components of effective assistive technology. During his PhD, he has interned at Microsoft Research and IBM. He is also a professional violinist specializing in Karnatic Classical Music.