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Automatic acoustic mosquito tagging with Bayesian neural networks

Abstract:

Deep learning models are now widely used in decision-making applications. These models must be robust to noise and carefully map to the underlying uncertainty in the data. Standard deterministic neural networks are well known to be poor at providing reliable estimates of uncertainty and often lack the robustness that is required for real-world deployment. In this paper, we work with an application that requires accurate uncertainty estimates in addition to good predictive performance. In part...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1007/978-3-030-86514-6_22

Authors


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Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
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Institution:
University of Oxford
Division:
MPLS
Department:
Biology
Role:
Author
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Institution:
University of Oxford
Division:
MPLS
Department:
Biology
Role:
Author
ORCID:
0000-0002-6763-2489
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
ORCID:
0000-0002-9305-9268
Publisher:
Springer Publisher's website
Host title:
Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track
Series:
Lecture Notes in Computer Science
Series number:
12978
Pages:
351-366
Place of publication:
Cham, Switzerland
Publication date:
2021-09-10
Acceptance date:
2021-06-18
Event title:
European Conference on Machine Learning and Knowledge Discovery in Databases (ECML PDKK 2021)
Event location:
Virtual event
Event website:
https://2021.ecmlpkdd.org/index.html
Event start date:
2021-09-13
Event end date:
2021-09-17
DOI:
EISSN:
1611-3349
ISSN:
0302-9743
EISBN:
9783030865146
ISBN:
9783030865139
Language:
English
Keywords:
Pubs id:
1198305
Local pid:
pubs:1198305
Deposit date:
2023-01-20

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