3 citations · 3 across the 2 of their papers we have counts for
5 papers
Tailored minimal reservoir computing: on the bidirectional connection between nonlinearities in the reservoir and in data
Davide Prosperino, Haochun Ma, Christoph Räth
We study how the degree of nonlinearity in the input data affects the optimal design of reservoir computers, focusing on how closely the model's nonlinearity should align with that…
Distinguishing indistinguishable attractors: Unsupervised anomaly detection with reservoir computers
Davide Prosperino, Haochun Ma, Christoph Räth
Detecting when a nonlinear dynamical system departs from its normal regime is a recurring problem across the sciences, from cardiology to climate and energy systems. We show that a…
Predicting three-dimensional chaotic systems with four qubit quantum systems
Joel Steinegger, Christoph Räth
Reservoir computing (RC) is among the most promising approaches for AI-based prediction models of complex systems. It combines superior prediction performance with very low CPU-nee…
Predicting two-dimensional spatiotemporal chaotic patterns with optimized high-dimensional hybrid reservoir computing
Tamon Nakano, Sebastian Baur, Christoph Räth
As an alternative approach for predicting complex dynamical systems where physics-based models are no longer reliable, reservoir computing (RC) has gained popularity. The hybrid ap…
Weight fluctuations in (deep) linear neural networks and a derivation of the inverse-variance flatness relation
Markus Gross, Arne P. Raulf, Christoph Räth
We investigate the stationary (late-time) training regime of single- and two-layer underparameterized linear neural networks within the continuum limit of stochastic gradient desce…