Articles | Volume 33, issue 4
https://doi.org/10.5194/npg-33-503-2026
https://doi.org/10.5194/npg-33-503-2026
Research article
 | 
02 Oct 2026
Research article |  | 02 Oct 2026

Conditional updates of neural network weights for increased out of training performance

Jan Saynisch-Wagner and Saran Rajendran Sari

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Cited articles

Bereiter, B., Eggleston, S., Schmitt, J., Nehrbass-Ahles, C., Stocker, T. F., Fischer, H., Kipfstuhl, S., and Chappellaz, J.: Revision of the EPICA Dome C CO2 record from 800 to 600 kyr before present, Geophys. Res. Lett., 42, 542–549, https://doi.org/10.1002/2014GL061957, 2015. a
Bergmeir, C., Hyndman, R., and Koo, B.: A note on the validity of cross-validation for evaluating autoregressive time series prediction, Comput. Stat. Data An., 120, 70–83, https://doi.org/10.1016/j.csda.2017.11.003, 2018. a
Beucler, T., Gentine, P., Yuval, J., Gupta, A., Peng, L., Lin, J., Yu, S., Rasp, S., Ahmed, F., O’Gorman, P. A., Neelin, J. D., Lutsko, N. J., and Pritchard, M.: Climate-invariant machine learning, Sci. Adv., 10, eadj7250, https://doi.org/10.1126/sciadv.adj7250, 2024. a, b, c
Chauhan, V. K., Zhou, J., Lu, P., Molaei, S., and Clifton, D. A.: A brief review of hypernetworks in deep learning, Artif. Intell. Rev., 57, 1–29, https://doi.org/10.1007/s10462-024-10862-8, 2024. a
Cortes, C. and Vapnik, V.: Support-vector networks, Mach. Learn., 20, 273–297, https://doi.org/10.1007/BF00994018, 1995. a
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Short summary
Neural networks are limited in situations that differ from the learned conditions. We propose a 3-step solution to this out of distribution problem: (1) Derive the anomalies of a trained neural networks' internal parameters by retraining on subsets of its training data. (2) Relate the ensuing network-parameter anomalies to differences in the training data subsets that caused them. (3) Extrapolate the found relations to generate a network that performs better outside the org. training distribution.
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