{"ID":2859553,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.06372","arxiv_id":"2510.06372","title":"A General Constructive Upper Bound on Shallow Neural Nets Complexity","abstract":"We provide an upper bound on the number of neurons required in a shallow neural network to approximate a continuous function on a compact set with a given accuracy. This method, inspired by a specific proof of the Stone-Weierstrass theorem, is constructive and more general than previous bounds of this character, as it applies to any continuous function on any compact set.","short_abstract":"We provide an upper bound on the number of neurons required in a shallow neural network to approximate a continuous function on a compact set with a given accuracy. This method, inspired by a specific proof of the Stone-Weierstrass theorem, is constructive and more general than previous bounds of this character, as it...","url_abs":"https://arxiv.org/abs/2510.06372","url_pdf":"https://arxiv.org/pdf/2510.06372v1","authors":"[\"Frantisek Hakl\",\"Vit Fojtik\"]","published":"2025-10-07T18:40:40Z","proceeding":"stat.ML","tasks":"[\"stat.ML\",\"cs.LG\"]","methods":"[]","has_code":false}