Off-the-shelf deep learning is not enough, and requires parsimony, Bayesianity, and causality

Abstract Deep neural networks (‘deep learning’) have emerged as a technology of choice to tackle problems in speech recognition, computer vision, finance, etc. However, adoption of deep learning in physical domains brings substantial challenges stemming from the correlative nature of deep learning m...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Rama K. Vasudevan, Maxim Ziatdinov, Lukas Vlcek, Sergei V. Kalinin
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Materials of engineering and construction. Mechanics of materials TA401-492 Computer software QA76.75-76.765
Acceso en línea:	https://doaj.org/article/71fb8b9fb2e94aae97f4e00701a335d9
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:71fb8b9fb2e94aae97f4e00701a335d9
record_format	dspace
spelling	oai:doaj.org-article:71fb8b9fb2e94aae97f4e00701a335d92021-12-02T13:57:35ZOff-the-shelf deep learning is not enough, and requires parsimony, Bayesianity, and causality10.1038/s41524-020-00487-02057-3960https://doaj.org/article/71fb8b9fb2e94aae97f4e00701a335d92021-01-01T00:00:00Zhttps://doi.org/10.1038/s41524-020-00487-0https://doaj.org/toc/2057-3960Abstract Deep neural networks (‘deep learning’) have emerged as a technology of choice to tackle problems in speech recognition, computer vision, finance, etc. However, adoption of deep learning in physical domains brings substantial challenges stemming from the correlative nature of deep learning methods compared to the causal, hypothesis driven nature of modern science. We argue that the broad adoption of Bayesian methods incorporating prior knowledge, development of solutions with incorporated physical constraints and parsimonious structural descriptors and generative models, and ultimately adoption of causal models, offers a path forward for fundamental and applied research.Rama K. VasudevanMaxim ZiatdinovLukas VlcekSergei V. KalininNature PortfolioarticleMaterials of engineering and construction. Mechanics of materialsTA401-492Computer softwareQA76.75-76.765ENnpj Computational Materials, Vol 7, Iss 1, Pp 1-6 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Materials of engineering and construction. Mechanics of materials TA401-492 Computer software QA76.75-76.765
spellingShingle	Materials of engineering and construction. Mechanics of materials TA401-492 Computer software QA76.75-76.765 Rama K. Vasudevan Maxim Ziatdinov Lukas Vlcek Sergei V. Kalinin Off-the-shelf deep learning is not enough, and requires parsimony, Bayesianity, and causality
description	Abstract Deep neural networks (‘deep learning’) have emerged as a technology of choice to tackle problems in speech recognition, computer vision, finance, etc. However, adoption of deep learning in physical domains brings substantial challenges stemming from the correlative nature of deep learning methods compared to the causal, hypothesis driven nature of modern science. We argue that the broad adoption of Bayesian methods incorporating prior knowledge, development of solutions with incorporated physical constraints and parsimonious structural descriptors and generative models, and ultimately adoption of causal models, offers a path forward for fundamental and applied research.
format	article
author	Rama K. Vasudevan Maxim Ziatdinov Lukas Vlcek Sergei V. Kalinin
author_facet	Rama K. Vasudevan Maxim Ziatdinov Lukas Vlcek Sergei V. Kalinin
author_sort	Rama K. Vasudevan
title	Off-the-shelf deep learning is not enough, and requires parsimony, Bayesianity, and causality
title_short	Off-the-shelf deep learning is not enough, and requires parsimony, Bayesianity, and causality
title_full	Off-the-shelf deep learning is not enough, and requires parsimony, Bayesianity, and causality
title_fullStr	Off-the-shelf deep learning is not enough, and requires parsimony, Bayesianity, and causality
title_full_unstemmed	Off-the-shelf deep learning is not enough, and requires parsimony, Bayesianity, and causality
title_sort	off-the-shelf deep learning is not enough, and requires parsimony, bayesianity, and causality
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/71fb8b9fb2e94aae97f4e00701a335d9
work_keys_str_mv	AT ramakvasudevan offtheshelfdeeplearningisnotenoughandrequiresparsimonybayesianityandcausality AT maximziatdinov offtheshelfdeeplearningisnotenoughandrequiresparsimonybayesianityandcausality AT lukasvlcek offtheshelfdeeplearningisnotenoughandrequiresparsimonybayesianityandcausality AT sergeivkalinin offtheshelfdeeplearningisnotenoughandrequiresparsimonybayesianityandcausality
_version_	1718392266735747072

Off-the-shelf deep learning is not enough, and requires parsimony, Bayesianity, and causality

Ejemplares similares