Constraint guided gradient descent: Training with inequality constraints with applications in regression and semantic segmentation

Author:

Van Baelen QuintenORCID,Karsmakers PeterORCID

Funder

KU Leuven

Vlaamse regering

Publisher

Elsevier BV

Subject

Artificial Intelligence,Cognitive Neuroscience,Computer Science Applications

Reference30 articles.

1. Constraint guided gradient descent: Guided training with inequality constraints;Van Baelen,2022

2. A proof of convergence for gradient descent in the training of artificial neural networks for constant target functions;Cheridito;J. Complexity,2022

3. Optimization methods for large-scale machine learning;Bottou;SIAM Rev.,2018

4. Comparative assessment of various machine learning-based bias correction methods for numerical weather prediction model forecasts of extreme air temperatures in urban areas;Cho;Earth Space Sci.,2020

5. M. Fischer, M. Balunovic, D. Drachsler-Cohen, T. Gehr, C. Zhang, M. Vechev, DL2: Training and querying neural networks with logic, in: 36th International Conference on Machine Learning, ICML 2019, ISBN: 9781510886988, 2019, pp. 3411–3427.

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