34 citations · 39 across the 8 of their papers we have counts for
3 papers · 1 filter
Applying statistical learning theory to deep learning
Cédric Gerbelot, Avetik Karagulyan, Stefani Karp +3
Although statistical learning theory provides a robust framework to understand supervised learning, many theoretical aspects of deep learning remain unclear, in particular how diff…
Machine Learning Without a Processor: Emergent Learning in a Nonlinear Electronic Metamaterial
Sam Dillavou, Benjamin D Beyer, Menachem Stern +3
Standard deep learning algorithms require differentiating large nonlinear networks, a process that is slow and power-hungry. Electronic learning metamaterials offer potentially fas…
Experimental Demonstration of Coupled Learning in Elastic Networks
Lauren E. Altman, Menachem Stern, Andrea J. Liu +1
Coupled learning is a contrastive scheme for tuning the properties of individual elements within a network in order to achieve desired functionality of the system. It takes advanta…