Enhanced Computational Complexity in Continuous-Depth Models: Neural Ordinary Differential Equations With Trainable Numerical Schemes
- DOI
- 10.1109/tpami.2025.3599629
- Published
- 2026-01
- Container
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Publisher
- Institute of Electrical and Electronics Engineers (IEEE)
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1109/tpami.2025.3599629,
title = {Enhanced Computational Complexity in Continuous-Depth Models: Neural Ordinary Differential Equations With Trainable Numerical Schemes},
author = {Said Ouala and Laurent Debreu and Bertrand Chapron and Fabrice Collard and Lucile Gaultier and Ronan Fablet},
year = {2026},
journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
doi = {10.1109/tpami.2025.3599629},
url = {https://doi.org/10.1109/tpami.2025.3599629}
}RIS
TY - JOUR TI - Enhanced Computational Complexity in Continuous-Depth Models: Neural Ordinary Differential Equations With Trainable Numerical Schemes AU - Said Ouala AU - Laurent Debreu AU - Bertrand Chapron AU - Fabrice Collard AU - Lucile Gaultier AU - Ronan Fablet PY - 2026 JO - IEEE Transactions on Pattern Analysis and Machine Intelligence DO - 10.1109/tpami.2025.3599629 UR - https://doi.org/10.1109/tpami.2025.3599629 ER -
APA
Ouala, S., Debreu, L., Chapron, B., Collard, F., Gaultier, L., & Fablet, R. (2026). Enhanced Computational Complexity in Continuous-Depth Models: Neural Ordinary Differential Equations With Trainable Numerical Schemes. IEEE Transactions on Pattern Analysis and Machine Intelligence. https://doi.org/10.1109/tpami.2025.3599629
Source records
- crossref · retrieved 2026-09-25T07:23:19.316Z