Sublinearly structured deep neural networks achieve feature learning consistency for compositional functions
- DOI
- 10.1016/j.slads.2026.100007
- Published
- 2026-09
- Container
- Statistical Learning and Data Science
- Publisher
- Elsevier BV
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1016/j.slads.2026.100007,
title = {Sublinearly structured deep neural networks achieve feature learning consistency for compositional functions},
author = {Sehwan Kim and Yan Sun and Faming Liang},
year = {2026},
journal = {Statistical Learning and Data Science},
doi = {10.1016/j.slads.2026.100007},
url = {https://doi.org/10.1016/j.slads.2026.100007}
}RIS
TY - JOUR TI - Sublinearly structured deep neural networks achieve feature learning consistency for compositional functions AU - Sehwan Kim AU - Yan Sun AU - Faming Liang PY - 2026 JO - Statistical Learning and Data Science DO - 10.1016/j.slads.2026.100007 UR - https://doi.org/10.1016/j.slads.2026.100007 ER -
APA
Kim, S., Sun, Y., & Liang, F. (2026). Sublinearly structured deep neural networks achieve feature learning consistency for compositional functions. Statistical Learning and Data Science. https://doi.org/10.1016/j.slads.2026.100007
Source records
- crossref · retrieved 2026-11-10T02:47:49.781Z