@article{torres2024,
  title={Model-Free Local Recalibration of Neural Networks},
  author={Torres, R. and Nott, D. J. and Sisson, S. A. and Rodrigues, T. and Reis, J. G. and Rodrigues, G. S.},
  journal={arXiv preprint arXiv:2403.05756},
  year={2024},
  institution={University of Bras{\'i}lia; National University of Singapore; University of New South Wales, Sydney},
  subjects={Methodology (stat.ME); Machine Learning (cs.LG)},
  abstract={Artificial neural networks (ANNs) are highly flexible predictive models. However, reliably quantifying uncertainty for their predictions is a continuing challenge. This work proposes a localized recalibration of ANN predictive distributions using the dimension-reduced representation of the input provided by the ANN hidden layers, demonstrating improved prediction and uncertainty quantification in real-world applications.},
  comments={25 pages, 5 figures},
  msc_classes={62G07 (Primary), 68T07, 68T37 (Secondary), 68Q10},
  acm_classes={G.3; I.5.1; I.6.4},
  doi={10.48550/arXiv.2403.05756}
}
