3 papers
cond-mat.dis-nn2026
Understanding and Embracing Imperfection in Physical Learning Networks
Sam Dillavou, Marcelo Guzman, Andrea J. Liu +1
Performing machine learning with analog signals offers advantages in speed and energy efficiency, but sensitivity to component and measurement imperfections often foils training wi…
cond-mat.dis-nn2025
Analog Physical Systems Can Exhibit Double Descent
Sam Dillavou, Jason W Rocks, Jacob F Wycoff +2
An important component of the success of large AI models is double descent, in which networks avoid overfitting as they grow relative to the amount of training data, instead improv…
cond-mat.soft2025
Stochastic dynamics of granular hopper flows: a configurational mode controls the stability of clogs
David Hathcock, Sam Dillavou, Jesse M. Hanlan +2
Granular flows in small-outlet hoppers exhibit several characteristic but poorly understood behaviors: temporary clogs (pauses) where flow stops before later spontaneously restarti…