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Training methods for climate and neural network models

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dc.contributor Aalto-yliopisto fi
dc.contributor Aalto University en
dc.contributor.advisor Kivinen, Jyri, Dr., Aalto University, Department of Computer Science, Finland
dc.contributor.author Abbas, Mudassar
dc.date.accessioned 2018-10-25T09:03:19Z
dc.date.available 2018-10-25T09:03:19Z
dc.date.issued 2018
dc.identifier.isbn 978-952-60-8259-2 (electronic)
dc.identifier.isbn 978-952-60-8258-5 (printed)
dc.identifier.issn 1799-4942 (electronic)
dc.identifier.issn 1799-4934 (printed)
dc.identifier.issn 1799-4934 (ISSN-L)
dc.identifier.uri https://aaltodoc.aalto.fi/handle/123456789/34496
dc.description.abstract When modeling complex phenomena in nature and in technological systems, one is often faced with  the task of tuning/calibrating the models. In such cases, there typically exists a need for model  parameter (and/or meta-parameter) value tuning for more effective modeling performance. Often such cannot be done manually, and in the machine learning approach, the tuning is done in an algorithmic and data-driven manner, and is called model training. The thesis presents studies in which such methods are adopted, in the contexts of climate and artificial neural networks, and proposes novel techniques. One of the studies is on the suitability of a well-known machine learning method called Bayesian optimization (BO), for parametric tuning of chaotic systems such as climate and numerical weatherprediction (NWP) models. The obtained results show that BO is a suitable method for such tuning tasks. A major desiderata for a trained machine learning model is the ability to generalize well to unseen data, and thus the phenomena such as (so-called) under- and overfitting are to be avoided. In this context, adopting (so-called) regularization methods as part of the model training process has become a standard procedure. In this thesis, we introduce a regularization framework that is shown to have close connections with many existing state-of-the-art regularization approaches. An adversarial variant, derived from the proposed regularization framework, is used for solving a classification task, and the obtained results are compared to those of other regularization methods. en
dc.format.extent 56 + app. 76
dc.format.mimetype application/pdf en
dc.language.iso en en
dc.publisher Aalto University en
dc.publisher Aalto-yliopisto fi
dc.relation.ispartofseries Aalto University publication series DOCTORAL DISSERTATIONS en
dc.relation.ispartofseries 209/2018
dc.relation.haspart [Publication 1]: Solonen, A., Hakkarainen, J., Ilin, A., Abbas, M., Bibov, A. Estimating model error covariance matrix parameters in extended Kalman filtering. Nonlinear Processes in Geophysics, 21, 5, 919–927, 2014. DOI: 10.5194/npg-21-919-2014
dc.relation.haspart [Publication 2]: Abbas, M., Ilin, A., Solonen, M., Hakkarainen, J., Oja, E., Järvinen, H. Bayesian optimization for tuning chaotic systems. Nonlin. Processes Geophys. Discuss., 1, 1283–1312, 2014. DOI: 10.5194/npgd-1-1283-2014
dc.relation.haspart [Publication 3]: Abbas, M., Ilin, A., Solonen, M., Hakkarainen, J., Oja, E., Järvinen, H. Empirical evaluation of Bayesian optimization in parametric tuning of chaotic systems. Int. J. Uncertainty Quantification, 6, 6, 467–485, 2016. DOI: 10.1615/Int.J.UncertaintyQuantification.2016016645
dc.relation.haspart [Publication 4]: Abbas, M., Kivinen, J., Raiko, T. Understanding regularization by virtual adversarial training, ladder networks and others. In International Conference on Learning Representations (ICLR) Workshop track, Puerto Rico, May 2016
dc.subject.other Computer science en
dc.title Training methods for climate and neural network models en
dc.type G5 Artikkeliväitöskirja fi
dc.contributor.school Perustieteiden korkeakoulu fi
dc.contributor.school School of Science en
dc.contributor.department Tietotekniikan laitos fi
dc.contributor.department Department of Computer Science en
dc.subject.keyword chaotic systems en
dc.subject.keyword filtering en
dc.subject.keyword bayesian optimization en
dc.identifier.urn URN:ISBN:978-952-60-8259-2
dc.type.dcmitype text en
dc.type.ontasot Doctoral dissertation (article-based) en
dc.type.ontasot Väitöskirja (artikkeli) fi
dc.contributor.supervisor Kaski, Kimmo, Prof., Aalto University, Department of Computer Science, Finland
dc.opn Heikkonen, Jukka, Dr., University of Turku, Finland
dc.contributor.lab Complex Systems en
dc.rev Roos, Teemu, Prof., University of Helsinki, Finland
dc.rev Pahikkala, Tapio, Prof., University of Turku, Finland
dc.date.defence 2018-11-15
local.aalto.acrisexportstatus checked
local.aalto.formfolder 2018_10_25_klo_11_54
local.aalto.archive yes

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