1.6k citations · 7.4k across the 81 of their papers we have counts for
83 papers
Physical learning beyond the quasistatic limit
Menachem Stern, Sam Dillavou, Marc Z. Miskin +2
Physical networks, such as biological neural networks, can learn desired functions without a central processor, using local learning rules in space and time to learn in a fully dis…
Demonstration of Decentralized, Physics-Driven Learning
Sam Dillavou, Menachem Stern, Andrea J. Liu +1
In typical artificial neural networks, neurons adjust according to global calculations of a central processor, but in the brain neurons and synapses self-adjust based on local info…
Elastoplasticity Mediates Dynamical Heterogeneity Below the Mode-Coupling Temperature
Rahul N. Chacko, François P. Landes, Giulio Biroli +3
As liquids approach the glass transition temperature, dynamical heterogeneity emerges as a crucial universal feature of their behavior. Dynamic facilitation, where local motion tri…
Tilting the playing field: Dynamical loss functions for machine learning
Miguel Ruiz-Garcia, Ge Zhang, Samuel S. Schoenholz +1
We show that learning can be improved by using loss functions that evolve cyclically during training to emphasize one class at a time. In underparameterized networks, such dynamica…
Correlation of plastic events with local structure in jammed packings across spatial dimensions
Sean A. Ridout, Jason W. Rocks, Andrea J. Liu
In jammed packings, it is usually thought that local structure only plays a significant role in specific regimes. The standard deviation of the relative excess coordination, $σ_Z/…
Supervised learning in physical networks: From machine learning to learning machines
Menachem Stern, Daniel Hexner, Jason W. Rocks +1
Materials and machines are often designed with particular goals in mind, so that they exhibit desired responses to given forces or constraints. Here we explore an alternative appro…