Thermodynamic limit in learning period three
<|reference_start|>Thermodynamic limit in learning period three: A continuous one-dimensional map with period three includes all periods. This raises the following question: Can we obtain any types of periodic orbits solely by learning three data points? In this letter, we report the answer to be yes. Considering a random neural network in its thermodynamic limit, we show that under certain conditions, learning period three can embed attractors with all periods into the network as a bifurcation after learning. The associated universality is explained by a topological conjugacy between the trained network and the classical logistic map.<|reference_end|>
@article{terasaki2024thermodynamic,
    title={Thermodynamic limit in learning period three},
    author={Yuichiro Terasaki and Kohei Nakajima},
    journal={arXiv preprint arXiv:2405.08825},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.08825},
    primaryClass={stat.ML cs.LG nlin.AO nlin.CD}
}