About this Event
150 Western Avenue, Allston, MA 02134
https://iacs.seas.harvard.edu/event/quarks-attentionRSVP HERE: https://forms.gle/smSKw4ARugNSZMwM7
Abstract: Attention plays a fundamental role in both natural and artificial intelligence systems. In deep learning, several attention-based neural network architectures have been proposed to tackle problems in natural language processing (NLP) and beyond, including transformer architectures which currently achieve state-of-the-art performance in NLP tasks. In this presentation we will: 1) identify and classify the most fundamental building blocks (quarks) of attention, both within and beyond the standard model of deep learning; 2) identify how these building blocks are used in all current attention-based architectures, including transformers; 3) demonstrate how transformers can effectively be applied to new problems in physics, from particle physics to astronomy; and 4) present a mathematical theory of attention capacity where, paradoxically, one of the main tools in the proofs is itself an attention mechanism.