Reliable Prediction Errors for Deep Neural Networks Using Test-Time Dropout

Author:

Cortés-Ciriano Isidro1ORCID,Bender Andreas1ORCID

Affiliation:

1. Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, United Kingdom

Funder

H2020 Marie Sklodowska-Curie Actions

Publisher

American Chemical Society (ACS)

Subject

Library and Information Sciences,Computer Science Applications,General Chemical Engineering,General Chemistry

Reference67 articles.

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3. Unterthiner, T.; Mayr, A.; Unter Klambauer, G.; Steijaert, M.; Wegner, J. K.; Johnson, J.; Ceulemans, H.; Hochreiter, S. Deep Learning as an Opportunity in Virtual Screening; Workshop on Deep Learning and Representation Learning, 2014

4. Deep learning

5. Goh, G. B.; Siegel, C.; Vishnu, A.; Hodas, N. O.; Baker, N. Chemception: A Deep Neural Network with Minimal Chemistry Knowledge Matches the Performance of Expert-Developed QSAR/QSPR Models. arXiv1706.06689, 2017, https//arxiv.org/abs/1706.06689 (accessed Jul 8, 2018).

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