Bridging the Gap Between Practice and Theory in Deep Learning

Wei Chen, Christa Cuchiero, Hadi Daneshmand, Stefanie Jegelka, Zelda E Mariet, Andre Niyongabo Rubungo, Jiaye Teng, Bohan Wang, Bohang Zhang, Jingzhao Zhang

Workshop Type: Hybrid

Keywords: gap, learning theory, empirical observation

Abstract:

The success of deep learning practices has driven the rapid development of learning theory. However, recent studies have pointed out that contrasting scenarios and conclusions exist between many existing theories and their corresponding real-world applications, leading to a significant gap. This workshop aims to bridge this gap by (i) troubleshooting unnoticed gaps between learning theory and practice and (ii) narrowing the existing ones by developing new analyses. We hope that this workshop will not only raise awareness of the challenges in bridging the gap between theory and practice in deep learning but also inspire new solutions and insights that contribute to the advancement of deep learning.
The workshop website: https://sites.google.com/view/bgpt-iclr24.

Submission Number: 28