165 citations · 1k across the 16 of their papers we have counts for
5 papers · 1 filter
Learn one size to infer all: Exploiting translational symmetries in delay-dynamical and spatio-temporal systems using scalable neural networks
Mirko Goldmann, Claudio R. Mirasso, Ingo Fischer +1
We design scalable neural networks adapted to translational symmetries in dynamical systems, capable of inferring untrained high-dimensional dynamics for different system sizes. We…
Nonlinear photonic dynamical systems for unconventional computing
Daniel Brunner, Laurent Larger, Miguel C. Soriano
Driven by the remarkable breakthroughs during the past decade, photonics neural networks have experienced a revival. Here, we provide a general overview of progress over the past d…
Dynamical phase transitions in quantum reservoir computing
Rodrigo Martínez-Peña, Gian Luca Giorgi, Johannes Nokkala +2
Closed quantum systems exhibit different dynamical regimes, like Many-Body Localization or thermalization, which determine the mechanisms of spread and processing of information. H…
Opportunities in Quantum Reservoir Computing and Extreme Learning Machines
Pere Mujal, Rodrigo Martínez-Peña, Johannes Nokkala +4
Quantum reservoir computing (QRC) and quantum extreme learning machines (QELM) are two emerging approaches that have demonstrated their potential both in classical and quantum mach…
Unveiling the role of plasticity rules in reservoir computing
Guillermo B. Morales, Claudio R. Mirasso, Miguel C. Soriano
Reservoir Computing (RC) is an appealing approach in Machine Learning that combines the high computational capabilities of Recurrent Neural Networks with a fast and easy training m…