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Found 6 papers, 1 papers with code
Dynamical stability and chaos in artificial neural network trajectories along training
The process of training an artificial neural network involves iteratively adapting its parameters so as to minimize the error of the network's prediction, when confronted with a learning task.
Learn one size to infer all: Exploiting translational symmetries in delay-dynamical and spatio-temporal systems using scalable neural networks
We train these networks to predict the dynamics of delay-dynamical and spatio-temporal systems for a single size.
Opportunities in Quantum Reservoir Computing and Extreme Learning Machines
Quantum reservoir computing (QRC) and quantum extreme learning machines (QELM) are two emerging approaches that have demonstrated their potential both in classical and quantum machine learning tasks.
Quantum Physics
Unveiling the role of plasticity rules in reservoir computing
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 method.
A Fast Machine Learning Model for ECG-Based Heartbeat Classification and Arrhythmia Detection
In the MIT-BIH AR database, our classification approach provides a sensitivity of 92. 7% and positive predictive value of 86. 1% for the ventricular ectopic beats, using the single lead II, and a sensitivity of 95. 7% and positive predictive value of 75. 1% when using the lead V1'.
Ranked #1 on
Heartbeat Classification
on AHA
Machine learning applied to quantum synchronization-assisted probing
A probing scheme is considered with an accessible and controllable qubit, used to probe an out-of equilibrium system consisting of a second qubit interacting with an environment.
