107 citations · 498 across the 13 of their papers we have counts for
13 papers
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…
The Physical Effects of Learning
Menachem Stern, Andrea J. Liu, Vijay Balasubramanian
Interacting many-body physical systems ranging from neural networks in the brain to folding proteins to self-modifying electrical circuits can learn to perform diverse tasks. This…
Learning to self-fold at a bifurcation
Chukwunonso Arinze, Menachem Stern, Sidney R. Nagel +1
Disordered mechanical systems can deform along a network of pathways that branch and recombine at special configurations called bifurcation points. Multiple pathways are accessible…
Learning without neurons in physical systems
Menachem Stern, Arvind Murugan
Learning is traditionally studied in biological or computational systems. The power of learning frameworks in solving hard inverse-problems provides an appealing case for the devel…
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)…
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…