all AI news
On Measuring Excess Capacity in Neural Networks. (arXiv:2202.08070v2 [cs.LG] UPDATED)
July 1, 2022, 1:11 a.m. | Florian Graf, Sebastian Zeng, Bastian Rieck, Marc Niethammer, Roland Kwitt
cs.LG updates on arXiv.org arxiv.org
We study the excess capacity of deep networks in the context of supervised
classification. That is, given a capacity measure of the underlying hypothesis
class -- in our case, empirical Rademacher complexity -- by how much can we (a
priori) constrain this class while retaining an empirical error on a par with
the unconstrained regime? To assess excess capacity in modern architectures
(such as residual networks), we extend and unify prior Rademacher complexity
bounds to accommodate function composition and addition, …
More from arxiv.org / cs.LG updates on arXiv.org
Jobs in AI, ML, Big Data
Senior ML Researcher - 3D Geometry Processing | 3D Shape Generation | 3D Mesh Data
@ Promaton | Europe
Senior AI Engineer, EdTech (Remote)
@ Lightci | Toronto, Ontario
Data Scientist for Salesforce Applications
@ ManTech | 781G - Customer Site,San Antonio,TX
AI Research Scientist
@ Gridmatic | Cupertino, CA
Data Engineer
@ Global Atlantic Financial Group | Boston, Massachusetts, United States
Machine Learning Engineer - Conversation AI
@ DoorDash | Sunnyvale, CA; San Francisco, CA; Seattle, WA; Los Angeles, CA