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Finite Neural Networks as Mixtures of Gaussian Processes: From Provable Error Bounds to Prior Selection

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Published in:Journal of Machine Learning Research
Format: Online Article RSS Article
Published: 2026
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container_title Journal of Machine Learning Research
description
discipline_display Engineering & Technology
discipline_facet Engineering & Technology
format Online Article
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genre Journal Article
id rss_article:9405
institution FRELIP
journal_source_facet Journal of Machine Learning Research
publishDate 2026
publishDateSort 2026
record_format rss_article
spellingShingle Finite Neural Networks as Mixtures of Gaussian Processes: From Provable Error Bounds to Prior Selection
Computer Science & Information Science
Computer Science & IT
Engineering & Technology
sub_discipline_display Computer Science & IT
sub_discipline_facet Computer Science & IT
subject_display Computer Science & Information Science
Computer Science & IT
Engineering & Technology
Computer Science & Information Science
Computer Science & IT
Engineering & Technology
subject_facet Computer Science & Information Science
Computer Science & IT
Engineering & Technology
title Finite Neural Networks as Mixtures of Gaussian Processes: From Provable Error Bounds to Prior Selection
title_auth Finite Neural Networks as Mixtures of Gaussian Processes: From Provable Error Bounds to Prior Selection
title_full Finite Neural Networks as Mixtures of Gaussian Processes: From Provable Error Bounds to Prior Selection
title_fullStr Finite Neural Networks as Mixtures of Gaussian Processes: From Provable Error Bounds to Prior Selection
title_full_unstemmed Finite Neural Networks as Mixtures of Gaussian Processes: From Provable Error Bounds to Prior Selection
title_short Finite Neural Networks as Mixtures of Gaussian Processes: From Provable Error Bounds to Prior Selection
title_sort finite neural networks as mixtures of gaussian processes: from provable error bounds to prior selection
topic Computer Science & Information Science
Computer Science & IT
Engineering & Technology
url http://jmlr.org/papers/v27/24-1199.html