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231225s2022 xx |||||o 00| ||eng c |
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|a 10.1109/TPAMI.2021.3065601
|2 doi
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|a eng
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| 100 |
1 |
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|a Tank, Alex
|e verfasserin
|4 aut
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| 245 |
1 |
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|a Neural Granger Causality
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|c 2022
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|a Text
|b txt
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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 02.08.2023
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|a published: Print-Electronic
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|a Citation Status MEDLINE
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|a While most classical approaches to Granger causality detection assume linear dynamics, many interactions in real-world applications, like neuroscience and genomics, are inherently nonlinear. In these cases, using linear models may lead to inconsistent estimation of Granger causal interactions. We propose a class of nonlinear methods by applying structured multilayer perceptrons (MLPs) or recurrent neural networks (RNNs) combined with sparsity-inducing penalties on the weights. By encouraging specific sets of weights to be zero-in particular, through the use of convex group-lasso penalties-we can extract the Granger causal structure. To further contrast with traditional approaches, our framework naturally enables us to efficiently capture long-range dependencies between series either via our RNNs or through an automatic lag selection in the MLP. We show that our neural Granger causality methods outperform state-of-the-art nonlinear Granger causality methods on the DREAM3 challenge data. This data consists of nonlinear gene expression and regulation time courses with only a limited number of time points. The successes we show in this challenging dataset provide a powerful example of how deep learning can be useful in cases that go beyond prediction on large datasets. We likewise illustrate our methods in detecting nonlinear interactions in a human motion capture dataset
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| 650 |
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|a Journal Article
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| 650 |
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|a Research Support, Non-U.S. Gov't
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| 650 |
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|a Research Support, U.S. Gov't, Non-P.H.S.
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| 700 |
1 |
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|a Covert, Ian
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Foti, Nicholas
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Shojaie, Ali
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Fox, Emily B
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on pattern analysis and machine intelligence
|d 1979
|g 44(2022), 8 vom: 11. Aug., Seite 4267-4279
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|x 1939-3539
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|g volume:44
|g year:2022
|g number:8
|g day:11
|g month:08
|g pages:4267-4279
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| 856 |
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|u http://dx.doi.org/10.1109/TPAMI.2021.3065601
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