<ul><li>Researchers have explored the dynamics of artificial neural network trajectory during training with unconventional large learning rates.</li><li>For certain learning rate values, the optimization shifts from exploitation-like to exploration-exploitation balance, leading to sensitive dependence on initial conditions.</li><li>Training time to achieve acceptable accuracy in the test set reduces to a minimum in this regime, indicating accelerated training of neural networks near the onset of chaos.</li><li>The study, initially demonstrated on the MNIST classification task, shows the constructive role of transient chaotic dynamics in training artificial neural networks across various learning tasks and architectures.</li></ul>

Leveraging chaos in the training of artificial neural networks

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