June 6, 2024, 4:43 a.m. | Darshil Doshi, Tianyu He, Aritra Das, Andrey Gromov

cs.LG updates on arXiv.org arxiv.org

arXiv:2406.03495v1 Announce Type: new
Abstract: Neural networks readily learn a subset of the modular arithmetic tasks, while failing to generalize on the rest. This limitation remains unmoved by the choice of architecture and training strategies. On the other hand, an analytical solution for the weights of Multi-layer Perceptron (MLP) networks that generalize on the modular addition task is known in the literature. In this work, we (i) extend the class of analytical solutions to include modular multiplication as well as …

abstract architecture arxiv cond-mat.dis-nn cs.lg hep-th layer learn math.nt mlp modular networks neural networks perceptron rest solution stat.ml strategies tasks training type while

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