3 papers
cond-mat.dis-nn2026
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…
cond-mat.dis-nn2025
Remembrance of Tasks Past in Tunable Physical Networks
Purba Chatterjee, Marcelo Guzman, Andrea J. Liu
Sequential learning in physical networks is hindered by catastrophic forgetting, where training a new task erases solutions to earlier ones. We show that we can significantly enhan…
cond-mat.dis-nn2025
Unsupervised and probabilistic learning with Contrastive Local Learning Networks: The Restricted Kirchhoff Machine
Marcelo Guzman, Simone Ciarella, Andrea J. Liu
Autonomous physical learning systems modify their internal parameters and solve computational tasks without relying on external computation. Compared to traditional computers, they…