Computer Science > Machine Learning
arXiv:2210.13447 (cs)
[Submitted on 24 Oct 2022]
Title:Precision Machine Learning
View a PDF of the paper titled Precision Machine Learning, by Eric J. Michaud and 2 other authors
View PDFAbstract:We explore unique considerations involved in fitting ML models to data with very high precision, as is often required for science applications. We empirically compare various function approximation methods and study how they scale with increasing parameters and data. We find that neural networks can often outperform classical approximation methods on high-dimensional examples, by auto-discovering and exploiting modular structures therein. However, neural networks trained with common optimizers are less powerful for low-dimensional cases, which motivates us to study the unique properties of neural network loss landscapes and the corresponding optimization challenges that arise in the high precision regime. To address the optimization issue in low dimensions, we develop training tricks which enable us to train neural networks to extremely low loss, close to the limits allowed by numerical precision.
| Subjects: | Machine Learning (cs.LG); Computational Physics (physics.comp-ph) |
| Cite as: | arXiv:2210.13447 [cs.LG] |
| (orarXiv:2210.13447v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2210.13447 arXiv-issued DOI via DataCite | |
| Related DOI: | https://doi.org/10.3390/e25010175 DOI(s) linking to related resources |
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View a PDF of the paper titled Precision Machine Learning, by Eric J. Michaud and 2 other authors
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