107 citations · 498 across the 13 of their papers we have counts for
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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 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…
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…