AI RESEARCH
Leveraging chaotic transients in the training of artificial neural networks
arXiv CS.LG
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ArXi:2506.08523v2 Announce Type: replace Traditional algorithms to optimize artificial neural networks when confronted with a supervised learning task are usually exploitation-type relaxational dynamics such as gradient descent (GD). Here, we explore the dynamics of the neural network trajectory along