A prediction rigidity formalism for low-cost uncertainties in trained neural networks
Quantifying the uncertainty of regression models is essential to ensure their reliability, particularly since their application often extends beyond their training domain. Based on the solution of a constrained optimization problem, this work proposes ‘prediction rigidities’ as a formalism to obtain...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IOP Publishing
2024-01-01
|
| Series: | Machine Learning: Science and Technology |
| Subjects: | |
| Online Access: | https://doi.org/10.1088/2632-2153/ad805f |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850210725225562112 |
|---|---|
| author | Filippo Bigi Sanggyu Chong Michele Ceriotti Federico Grasselli |
| author_facet | Filippo Bigi Sanggyu Chong Michele Ceriotti Federico Grasselli |
| author_sort | Filippo Bigi |
| collection | DOAJ |
| description | Quantifying the uncertainty of regression models is essential to ensure their reliability, particularly since their application often extends beyond their training domain. Based on the solution of a constrained optimization problem, this work proposes ‘prediction rigidities’ as a formalism to obtain uncertainties of arbitrary pre-trained regressors. A clear connection between the suggested framework and Bayesian inference is established, and a last-layer approximation is developed and rigorously justified to enable the application of the method to neural networks. This extension affords cheap uncertainties without any modification to the neural network itself or its training procedure. The effectiveness of this approach is shown for a wide range of regression tasks, ranging from simple toy models to applications in chemistry and meteorology. |
| format | Article |
| id | doaj-art-761be39486c44b488e70c67939201fda |
| institution | OA Journals |
| issn | 2632-2153 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | Machine Learning: Science and Technology |
| spelling | doaj-art-761be39486c44b488e70c67939201fda2025-08-20T02:09:43ZengIOP PublishingMachine Learning: Science and Technology2632-21532024-01-015404501810.1088/2632-2153/ad805fA prediction rigidity formalism for low-cost uncertainties in trained neural networksFilippo Bigi0https://orcid.org/0000-0002-9338-7317Sanggyu Chong1https://orcid.org/0000-0002-6948-1602Michele Ceriotti2https://orcid.org/0000-0003-2571-2832Federico Grasselli3https://orcid.org/0000-0003-4284-0094Laboratory of Computational Science and Modeling, Institut des Matériaux , École Polytechnique Fédérale de Lausanne, 1015 Lausanne, SwitzerlandLaboratory of Computational Science and Modeling, Institut des Matériaux , École Polytechnique Fédérale de Lausanne, 1015 Lausanne, SwitzerlandLaboratory of Computational Science and Modeling, Institut des Matériaux , École Polytechnique Fédérale de Lausanne, 1015 Lausanne, SwitzerlandLaboratory of Computational Science and Modeling, Institut des Matériaux , École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland; Department of Physics, Informatics and Mathematics, University of Modena and Reggio Emilia , via Giuseppe Campi 213/a, Modena 41125, ItalyQuantifying the uncertainty of regression models is essential to ensure their reliability, particularly since their application often extends beyond their training domain. Based on the solution of a constrained optimization problem, this work proposes ‘prediction rigidities’ as a formalism to obtain uncertainties of arbitrary pre-trained regressors. A clear connection between the suggested framework and Bayesian inference is established, and a last-layer approximation is developed and rigorously justified to enable the application of the method to neural networks. This extension affords cheap uncertainties without any modification to the neural network itself or its training procedure. The effectiveness of this approach is shown for a wide range of regression tasks, ranging from simple toy models to applications in chemistry and meteorology.https://doi.org/10.1088/2632-2153/ad805fpredictionsrigiditylow-cost uncertaintiesneural networkregressionpre-trained |
| spellingShingle | Filippo Bigi Sanggyu Chong Michele Ceriotti Federico Grasselli A prediction rigidity formalism for low-cost uncertainties in trained neural networks Machine Learning: Science and Technology predictions rigidity low-cost uncertainties neural network regression pre-trained |
| title | A prediction rigidity formalism for low-cost uncertainties in trained neural networks |
| title_full | A prediction rigidity formalism for low-cost uncertainties in trained neural networks |
| title_fullStr | A prediction rigidity formalism for low-cost uncertainties in trained neural networks |
| title_full_unstemmed | A prediction rigidity formalism for low-cost uncertainties in trained neural networks |
| title_short | A prediction rigidity formalism for low-cost uncertainties in trained neural networks |
| title_sort | prediction rigidity formalism for low cost uncertainties in trained neural networks |
| topic | predictions rigidity low-cost uncertainties neural network regression pre-trained |
| url | https://doi.org/10.1088/2632-2153/ad805f |
| work_keys_str_mv | AT filippobigi apredictionrigidityformalismforlowcostuncertaintiesintrainedneuralnetworks AT sanggyuchong apredictionrigidityformalismforlowcostuncertaintiesintrainedneuralnetworks AT micheleceriotti apredictionrigidityformalismforlowcostuncertaintiesintrainedneuralnetworks AT federicograsselli apredictionrigidityformalismforlowcostuncertaintiesintrainedneuralnetworks AT filippobigi predictionrigidityformalismforlowcostuncertaintiesintrainedneuralnetworks AT sanggyuchong predictionrigidityformalismforlowcostuncertaintiesintrainedneuralnetworks AT micheleceriotti predictionrigidityformalismforlowcostuncertaintiesintrainedneuralnetworks AT federicograsselli predictionrigidityformalismforlowcostuncertaintiesintrainedneuralnetworks |