May 21, 2024, 4:44 a.m. | Raffaele Marino, Lorenzo Giambagli, Lorenzo Chicchi, Lorenzo Buffoni, Duccio Fanelli

cs.LG updates on

arXiv:2311.10387v2 Announce Type: replace-cross
Abstract: A novel approach for supervised classification is presented which sits at the intersection of machine learning and dynamical systems theory. At variance with other methodologies that employ ordinary differential equations for classification purposes, the untrained model is a priori constructed to accommodate for a set of pre-assigned stationary stable attractors. Classifying amounts to steer the dynamics towards one of the planted attractors, depending on the specificity of the processed item supplied as an input. Asymptotically …

abstract arxiv classification cond-mat.dis-nn cond-mat.stat-mech cs.lg differential intersection machine machine learning networks neural networks node novel ordinary replace systems theory type variance via

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