The Responsibility Problem in Neural Networks with Unordered Targets
Title: | The Responsibility Problem in Neural Networks with Unordered Targets |
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Authors: | Hayes, Ben, Saitis, Charalampos, Fazekas, György |
Publication Year: | 2023 |
Collection: | Computer Science |
Subject Terms: | Computer Science - Machine Learning, Computer Science - Discrete Mathematics |
More Details: | We discuss the discontinuities that arise when mapping unordered objects to neural network outputs of fixed permutation, referred to as the responsibility problem. Prior work has proved the existence of the issue by identifying a single discontinuity. Here, we show that discontinuities under such models are uncountably infinite, motivating further research into neural networks for unordered data. Comment: Accepted for TinyPaper archival at ICLR 2023: https://openreview.net/forum?id=jd7Hy1jRiv4 |
Document Type: | Working Paper |
Access URL: | http://arxiv.org/abs/2304.09499 |
Accession Number: | edsarx.2304.09499 |
Database: | arXiv |
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Items | – Name: Title Label: Title Group: Ti Data: The Responsibility Problem in Neural Networks with Unordered Targets – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Hayes%2C+Ben%22">Hayes, Ben</searchLink><br /><searchLink fieldCode="AR" term="%22Saitis%2C+Charalampos%22">Saitis, Charalampos</searchLink><br /><searchLink fieldCode="AR" term="%22Fazekas%2C+György%22">Fazekas, György</searchLink> – Name: DatePubCY Label: Publication Year Group: Date Data: 2023 – Name: Subset Label: Collection Group: HoldingsInfo Data: Computer Science – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Computer+Science+-+Machine+Learning%22">Computer Science - Machine Learning</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+Science+-+Discrete+Mathematics%22">Computer Science - Discrete Mathematics</searchLink> – Name: Abstract Label: Description Group: Ab Data: We discuss the discontinuities that arise when mapping unordered objects to neural network outputs of fixed permutation, referred to as the responsibility problem. Prior work has proved the existence of the issue by identifying a single discontinuity. Here, we show that discontinuities under such models are uncountably infinite, motivating further research into neural networks for unordered data.<br />Comment: Accepted for TinyPaper archival at ICLR 2023: https://openreview.net/forum?id=jd7Hy1jRiv4 – Name: TypeDocument Label: Document Type Group: TypDoc Data: Working Paper – Name: URL Label: Access URL Group: URL Data: <link linkTarget="URL" linkTerm="http://arxiv.org/abs/2304.09499" linkWindow="_blank">http://arxiv.org/abs/2304.09499</link> – Name: AN Label: Accession Number Group: ID Data: edsarx.2304.09499 |
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RecordInfo | BibRecord: BibEntity: Subjects: – SubjectFull: Computer Science - Machine Learning Type: general – SubjectFull: Computer Science - Discrete Mathematics Type: general Titles: – TitleFull: The Responsibility Problem in Neural Networks with Unordered Targets Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Hayes, Ben – PersonEntity: Name: NameFull: Saitis, Charalampos – PersonEntity: Name: NameFull: Fazekas, György IsPartOfRelationships: – BibEntity: Dates: – D: 19 M: 04 Type: published Y: 2023 |
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