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231225s2022 xx |||||o 00| ||eng c |
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|a 10.1109/TPAMI.2021.3059462
|2 doi
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|a eng
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| 100 |
1 |
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|a Gould, Stephen
|e verfasserin
|4 aut
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| 245 |
1 |
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|a Deep Declarative Networks
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| 264 |
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|c 2022
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|a Text
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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|a Date Completed 07.07.2022
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|a Date Revised 09.07.2022
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|a published: Print-Electronic
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|a Citation Status MEDLINE
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|a We explore a class of end-to-end learnable models wherein data processing nodes (or network layers) are defined in terms of desired behavior rather than an explicit forward function. Specifically, the forward function is implicitly defined as the solution to a mathematical optimization problem. Consistent with nomenclature in the programming languages community, we name these models deep declarative networks. Importantly, it can be shown that the class of deep declarative networks subsumes current deep learning models. Moreover, invoking the implicit function theorem, we show how gradients can be back-propagated through many declaratively defined data processing nodes thereby enabling end-to-end learning. We discuss how these declarative processing nodes can be implemented in the popular PyTorch deep learning software library allowing declarative and imperative nodes to co-exist within the same network. We also provide numerous insights and illustrative examples of declarative nodes and demonstrate their application for image and point cloud classification tasks
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|a Journal Article
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|a Research Support, Non-U.S. Gov't
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| 700 |
1 |
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|a Hartley, Richard
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Campbell, Dylan
|e verfasserin
|4 aut
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| 773 |
0 |
8 |
|i Enthalten in
|t IEEE transactions on pattern analysis and machine intelligence
|d 1979
|g 44(2022), 8 vom: 04. Aug., Seite 3988-4004
|w (DE-627)NLM098212257
|x 1939-3539
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|g volume:44
|g year:2022
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|g day:04
|g month:08
|g pages:3988-4004
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|u http://dx.doi.org/10.1109/TPAMI.2021.3059462
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