Abstract
Deep neural networks are powerful predictors for a variety of tasks. However, they do not capture uncertainty directly. Using neural network ensembles to quantify uncertainty is competitive with approaches based on Bayesian neural networks while benefiting from better computational scalability. However, building ensembles of neural networks is a challenging task because, in addition to choosing the right neural architecture or hyperparameters for each member of the ensemble, there is an added cost of training each model. To address this issue, we propose AutoDEUQ, an automated approach for generating an ensemble of deep neural networks. Our approach leverages joint neural architecture and hyperparameter search to generate ensembles. We use the law of total variance to decompose the predictive variance of deep ensembles into aleatoric (data) and epistemic (model) uncertainties. We show that AutoDEUQ outperforms probabilistic backpropagation, Monte Carlo dropout, deep ensemble, distribution-free ensembles, and hyper ensemble methods on a number of regression benchmarks.
Original language | English |
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Title of host publication | 2022 26th International Conference on Pattern Recognition, ICPR 2022 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 1908-1914 |
Number of pages | 7 |
ISBN (Electronic) | 9781665490627 |
DOIs | |
State | Published - 2022 |
Externally published | Yes |
Event | 26th International Conference on Pattern Recognition, ICPR 2022 - Montreal, Canada Duration: Aug 21 2022 → Aug 25 2022 |
Publication series
Name | Proceedings - International Conference on Pattern Recognition |
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Volume | 2022-August |
ISSN (Print) | 1051-4651 |
Conference
Conference | 26th International Conference on Pattern Recognition, ICPR 2022 |
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Country/Territory | Canada |
City | Montreal |
Period | 08/21/22 → 08/25/22 |
Funding
V. ACKNOWLEDGEMENT This work was supported by the U.S. Department of Energy, Office of Science, Advanced Scientific Computing Research, under Contract DE-AC02-06CH11357 and DOE Early Career Research Program award. We are grateful for the use of the computing resources in the Joint Laboratory for System Evaluation and Leadership Computing Facility at Argonne.