Interpretable and fair generalized additive neural networks via multi-objective learning.
- DOI
- 10.1016/j.neunet.2026.109520
- Published
- 2026 Aug 19
- Container
- Neural networks : the official journal of the International Neural Network Society
- Publisher
- Not recorded
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1016/j.neunet.2026.109520,
title = {Interpretable and fair generalized additive neural networks via multi-objective learning.},
author = {Wang Z and Huang C and Tang K and Ong YS and Yao X},
year = {2026},
journal = {Neural networks : the official journal of the International Neural Network Society},
doi = {10.1016/j.neunet.2026.109520},
url = {https://doi.org/10.1016/j.neunet.2026.109520}
}RIS
TY - JOUR TI - Interpretable and fair generalized additive neural networks via multi-objective learning. AU - Wang Z AU - Huang C AU - Tang K AU - Ong YS AU - Yao X PY - 2026 JO - Neural networks : the official journal of the International Neural Network Society DO - 10.1016/j.neunet.2026.109520 UR - https://doi.org/10.1016/j.neunet.2026.109520 ER -
APA
Z, W., C, H., K, T., YS, O., & X, Y. (2026). Interpretable and fair generalized additive neural networks via multi-objective learning.. Neural networks : the official journal of the International Neural Network Society. https://doi.org/10.1016/j.neunet.2026.109520
Source records
- pubmed · retrieved 2026-09-26T06:32:36.046Z