34 citations · 69 across the 9 of their papers we have counts for
4 papers · 1 filter
Cornerstones are the Key Stones: Using Interpretable Machine Learning to Probe the Clogging Process in 2D Granular Hoppers
Jesse M. Hanlan, Sam Dillavou, Andrea J. Liu +1
The sudden arrest of flow by formation of a stable arch over an outlet is a unique and characteristic feature of granular materials. Previous work suggests that grains near the out…
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…
Desynchronous Learning in a Physics-Driven Learning Network
Jacob F Wycoff, Sam Dillavou, Menachem Stern +2
In a neuron network, synapses update individually using local information, allowing for entirely decentralized learning. In contrast, elements in an artificial neural network (ANN)…