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Found 4 papers, 1 papers with code
Learn to Synchronize, Synchronize to Learn
In recent years, the machine learning community has seen a continuous growing interest in research aimed at investigating dynamical aspects of both training procedures and machine learning models.
Input-to-State Representation in linear reservoirs dynamics
Reservoir computing is a popular approach to design recurrent neural networks, due to its training simplicity and approximation performance.
Echo State Networks with Self-Normalizing Activations on the Hyper-Sphere
Finding such a region requires searching in hyper-parameter space in a sensible way: hyper-parameter configurations marginally outside such a region might yield networks exhibiting fully developed chaos, hence producing unreliable computations.
A characterization of the Edge of Criticality in Binary Echo State Networks
Echo State Networks (ESNs) are simplified recurrent neural network models composed of a reservoir and a linear, trainable readout layer.
