34 citations · 39 across the 8 of their papers we have counts for
8 papers
Contrastive learning in tunable dynamical systems
Menachem Stern, Adam G. Frim, Raúl Candás +2
We generalize the theory of supervised contrastive learning, previously applied to physical systems at equilibrium or steady state, to systems following any dynamics described by c…
Learning Associations in Reconfigurable Particle Packings via Local Cyclic Driving
Wenjing Guo, Vidyesh Rao Anisetti, Kairui Zhang +7
We investigate associative-memory behavior in a reconfigurable particle packing programmed by purely local cyclic driving. The system is a two-dimensional bidisperse Lennard--Jones…
Harnessing intuitive local evolution rules for physical learning
Roie Ezraty, Menachem Stern, Shmuel M. Rubinstein
Machine Learning, however popular and accessible, is computationally intensive and highly power-consuming, prompting interest in alternative physical implementations of learning ta…
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…
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…
Applying statistical learning theory to deep learning
Cédric Gerbelot, Avetik Karagulyan, Stefani Karp +3
Although statistical learning theory provides a robust framework to understand supervised learning, many theoretical aspects of deep learning remain unclear, in particular how diff…
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…