2024
DOI: 10.3389/fcpxs.2024.1367957
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Dynamical stability and chaos in artificial neural network trajectories along training

Kaloyan Danovski,
Miguel C. Soriano,
Lucas Lacasa

Abstract: 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. This iterative change can be naturally interpreted as a trajectory in network space–a time series of networks–and thus the training algorithm (e.g., gradient descent optimization of a suitable loss function) can be interpreted as a dynamical system in graph space. In order to illustrate this interpretation, here we study… Show more

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