34 citations · 69 across the 9 of their papers we have counts for
9 papers
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…
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…
Microscopic imprints of learned solutions in adaptive resistor networks
Marcel Guzman, Felipe Martins, Menachem Stern +1
In physical networks trained using supervised learning, physical parameters are adjusted to produce desired responses to inputs. An example is electrical contrastive local learning…
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…
Physical networks become what they learn
Menachem Stern, Marcelo Guzman, Felipe Martins +2
Physical networks can develop diverse responses, or functions, by design, evolution or learning. We focus on electrical networks of nodes connected by resistive edges. Such network…
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…
Physical learning of power-efficient solutions
Menachem Stern, Sam Dillavou, Dinesh Jayaraman +2
As the size and ubiquity of artificial intelligence and computational machine learning (ML) models grow, their energy consumption for training and use is rapidly becoming economica…
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)…