How neural networks learn to classify chaotic time series.

We tackle the outstanding issue of analyzing the inner workings of neural networks trained to classify regular-vs-chaotic time series. This setting, well-studied in dynamical systems, enables thorough formal analyses. We focus specifically on a family of networks dubbed large Kernel convolutional neural networks (LKCNNs), recently introduced by Boullé et al. [403, 132261 (2021)]. These non-recursive networks have been shown to outperform other established architectures (e.g., residual networks, shallow neural networks, and fully convolutional networks) at this classification task. Furthermore, they outperform "manual" classification approaches based on direct reconstruction of the Lyapunov exponent. We find that LKCNNs use qualitative properties of the input sequence. We show that LKCNN models trained from random weight initialization, end in two most common performance groups: one with relatively low performance (0.72 average classification accuracy) and one with high classification performance (0.94 average classification accuracy). Notably, the models in the low performance class display periodic activations that are qualitatively similar to those exhibited by LKCNNs with random weights. This could give very general criteria for identifying, a priori, trained weights that yield poor accuracy.