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Looking at the posterior: accuracy and uncertainty of neural-network predictions
Looking at the posterior: accuracy and uncertainty of neural-network predictions
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Looking at the posterior: accuracy and uncertainty of neural-network predictions

Machine learning: science and technology, 2023-12, Vol.4 (4), p.45032 [Peer Reviewed Journal]

2023 The Author(s). Published by IOP Publishing Ltd. This work is published under http://creativecommons.org/licenses/by/4.0 (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 2632-2153 ;EISSN: 2632-2153 ;DOI: 10.1088/2632-2153/ad0ab4

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2
Review of natural language processing techniques for characterizing positive energy districts
Review of natural language processing techniques for characterizing positive energy districts
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Review of natural language processing techniques for characterizing positive energy districts

Journal of physics. Conference series, 2023-11, Vol.2600 (8), p.82024 [Peer Reviewed Journal]

Published under licence by IOP Publishing Ltd. This work is published under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 1742-6588 ;EISSN: 1742-6596 ;DOI: 10.1088/1742-6596/2600/8/082024

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3
An attempt to predict planing hull motions using machine learning methods
An attempt to predict planing hull motions using machine learning methods
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An attempt to predict planing hull motions using machine learning methods

12th INTERNATIONAL WORKSHOP ON SHIP AND MARINE HYDRODYNAMICS (IWSH-2023), 2023-08, Vol.1288 (1), p.12026 [Peer Reviewed Journal]

Published under licence by IOP Publishing Ltd ;Published under licence by IOP Publishing Ltd. This work is published under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 1757-8981 ;EISSN: 1757-899X ;DOI: 10.1088/1757-899X/1288/1/012026

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4
A Data Science Platform to Enable Time-domain Astronomy
A Data Science Platform to Enable Time-domain Astronomy
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A Data Science Platform to Enable Time-domain Astronomy

Astrophys.J.Suppl, 2023-08, Vol.267 (2), p.31 [Peer Reviewed Journal]

2023. The Author(s). Published by the American Astronomical Society. ;Attribution ;ISSN: 0067-0049 ;ISSN: 1538-4365 ;EISSN: 1538-4365 ;DOI: 10.3847/1538-4365/acdee1

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5
Deep multi-task mining Calabi–Yau four-folds
Deep multi-task mining Calabi–Yau four-folds
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Deep multi-task mining Calabi–Yau four-folds

Mach.Learn.Sci.Tech, 2022-03, Vol.3 (1), p.15006 [Peer Reviewed Journal]

2021 The Author(s). Published by IOP Publishing Ltd ;2022. This work is published under http://creativecommons.org/licenses/by/4.0 (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;Distributed under a Creative Commons Attribution 4.0 International License ;ISSN: 2632-2153 ;EISSN: 2632-2153 ;DOI: 10.1088/2632-2153/ac37f7 ;CODEN: MLSTCK

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6
News selection and framing: the media as a stakeholder in human–carnivore coexistence
News selection and framing: the media as a stakeholder in human–carnivore coexistence
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News selection and framing: the media as a stakeholder in human–carnivore coexistence

Environmental research letters, 2021-06, Vol.16 (6), p.64075 [Peer Reviewed Journal]

2021 The Author(s). Published by IOP Publishing Ltd ;2021. This work is published under http://creativecommons.org/licenses/by/4.0 (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 1748-9326 ;EISSN: 1748-9326 ;DOI: 10.1088/1748-9326/ac05ef ;CODEN: ERLNAL

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7
Graph networks for molecular design
Graph networks for molecular design
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Graph networks for molecular design

Machine learning: science and technology, 2021-06, Vol.2 (2), p.25023 [Peer Reviewed Journal]

2021. This work is published under http://creativecommons.org/licenses/by/4.0 (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 2632-2153 ;EISSN: 2632-2153 ;DOI: 10.1088/2632-2153/abcf91

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8
Towards spatial integration of qualitative data for urban transformation - challenges with automated geovisualization of perception of urban places
Towards spatial integration of qualitative data for urban transformation - challenges with automated geovisualization of perception of urban places
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Towards spatial integration of qualitative data for urban transformation - challenges with automated geovisualization of perception of urban places

Digital Twin Cities Centre, 2020-11, Vol.588 (5), p.52041 [Peer Reviewed Journal]

Published under licence by IOP Publishing Ltd ;ISSN: 1755-1307 ;ISSN: 1755-1315 ;EISSN: 1755-1315 ;DOI: 10.1088/1755-1315/588/5/052041

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9
Prediction of wall-bounded turbulence from wall quantities using convolutional neural networks
Prediction of wall-bounded turbulence from wall quantities using convolutional neural networks
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Prediction of wall-bounded turbulence from wall quantities using convolutional neural networks

Journal of Physics, 2020-04, Vol.1522 (1), p.12022 [Peer Reviewed Journal]

Published under licence by IOP Publishing Ltd ;2020. This work is published under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 1742-6588 ;EISSN: 1742-6596 ;DOI: 10.1088/1742-6596/1522/1/012022

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