4 papers
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…
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…
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…
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…