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Kolmogorov-Arnold Networks for Personal Context Recognition on ExtraSensory — arXiv:2610.05250

Source: arXiv
Original: https://arxiv.org/abs/2610.05250
License (as archived): CC0-1.0
Archived: 2026-10-06T05:30:58.219Z
SHA-256 of the archived text: 530b8693a0b602cbe7f892c692e4363c8a6ca7b84f5d1bc8d17d5c63b2fb79c1
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How to cite

Kolmogorov-Arnold Networks for Personal Context Recognition on ExtraSensory — arXiv:2610.05250 — arXiv, https://arxiv.org/abs/2610.05250 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_dff8fa45991d (Metraipolis archive dc_dff8fa45991d, sha256 530b8693a0b602cbe7f892c692e4363c8a6ca7b84f5d1bc8d17d5c63b2fb79c1)

BibTeX

@misc{dcdff8fa45991d,
  title = {Kolmogorov-Arnold Networks for Personal Context Recognition on ExtraSensory — arXiv:2610.05250},
  howpublished = {arXiv},
  url = {https://arxiv.org/abs/2610.05250},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_dff8fa45991d; sha256 530b8693a0b602cbe7f892c692e4363c8a6ca7b84f5d1bc8d17d5c63b2fb79c1},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv, https://arxiv.org/abs/2610.05250. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Kolmogorov--Arnold Networks (KANs) have attracted increasing attention in recent years, with applications across a wide range of AI tasks. In this paper, we evaluate several KAN variants on the ExtraSensory dataset for personal context recognition and compare them with a multilayer perceptron (MLP) and TabM. We conduct the main experiments using five user folds and three random seeds per fold and report the average Macro-F1, Micro-F1, and training time. We also perform shallow ablation studies on grid size, the number of grids, and data normalization to examine their effects on KAN performance. The results show that all evaluated KAN variants significantly outperform MLP in terms of Macro-F1 and Micro-F1 and achieve performance comparable to TabM. However, KAN variants generally require more training time, while TabM provides a more favorable balance between predictive performance and training efficiency. These results suggest that KANs are promising for personal context recognition, while their computational efficiency remains an important challenge. Our source code is publicly available at: this https URL .

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.LG, nuovi lavori del giorno).
Categorie arXiv: Machine Learning (cs.LG).
Pagina del lavoro: https://arxiv.org/abs/2610.05250 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-06T05:27:05.774Z dalla pagina https://arxiv.org/list/cs.LG/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).