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Ferianc, M., Maji, P., Mattina, M. and Rodrigues, M. (2021) On the Effects of Quantisation on Model Uncertainty in Bayesian Neural Networks. Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence (UAI 2021), Proceedings of Machine Learning Research, 161, 929-938.
https://proceedings.mlr.press/v161/ferianc21a/ferianc21a.pdf

has been cited by the following article:

• TITLE: Objective Model Selection in Physics: Exploring the Finite Information Quantity Approach

  AUTHORS: Boris Menin

  KEYWORDS: Comparative Uncertainty, Finite Information Quantity, Formulating a Model, Measurement Accuracy Limit, Objective Model Selection

  JOURNAL NAME: Journal of Applied Mathematics and Physics, Vol.12 No.5, May 30, 2024

  ABSTRACT: Traditional methods for selecting models in experimental data analysis are susceptible to researcher bias, hindering exploration of alternative explanations and potentially leading to overfitting. The Finite Information Quantity (FIQ) approach offers a novel solution by acknowledging the inherent limitations in information processing capacity of physical systems. This framework facilitates the development of objective criteria for model selection (comparative uncertainty) and paves the way for a more comprehensive understanding of phenomena through exploring diverse explanations. This work presents a detailed comparison of the FIQ approach with ten established model selection methods, highlighting the advantages and limitations of each. We demonstrate the potential of FIQ to enhance the objectivity and robustness of scientific inquiry through three practical examples: selecting appropriate models for measuring fundamental constants, sound velocity, and underwater electrical discharges. Further research is warranted to explore the full applicability of FIQ across various scientific disciplines.