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Learning from machine learning - how to deduce a mechanism-based pharmacometrics model for serum creatinine in preterm neonates from neural ordinary differential equations
Learning from machine learning - how to deduce a mechanism-based pharmacometrics model for serum creatinine in preterm neonates from neural ordinary differential equations
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Learning from machine learning - how to deduce a mechanism-based pharmacometrics model for serum creatinine in preterm neonates from neural ordinary differential equations

Koch, Gilbert ; Dominic, Braem ; Allegaert, Karel ; van den Anker, John ; Pfister, Marc

2024

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Scientific Discussions from CH4 Conversion to Machine Learning Applications CINE-CMSC
Scientific Discussions from CH4 Conversion to Machine Learning Applications CINE-CMSC
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Scientific Discussions from CH4 Conversion to Machine Learning Applications CINE-CMSC

Juarez Lopes Ferreira da Silva

São Carlos Grupo QTNano, Instituto de Química de São Carlos, IQSC, Universidade de São Paulo, SP 2020

Localização: IQSC - Inst. Química de São Carlos    (P19032 )(Acessar)

3
Machine Learning for Advanced Solar Cell Production. Adversarial Denoising, Sub-pixel Alignment and the Digital Twin: Paper presented at Climate Change with Machine Learning Workshop at 34th Conference on Neural Information Processing Systems, NeurIPS 2020, December 6, 2020, Online, Vancouver, Canada
Machine Learning for Advanced Solar Cell Production. Adversarial Denoising, Sub-pixel Alignment and the Digital Twin: Paper presented at Climate Change with Machine Learning Workshop at 34th Conference on Neural Information Processing Systems, NeurIPS 2020, December 6, 2020, Online, Vancouver, Canada
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Machine Learning for Advanced Solar Cell Production. Adversarial Denoising, Sub-pixel Alignment and the Digital Twin: Paper presented at Climate Change with Machine Learning Workshop at 34th Conference on Neural Information Processing Systems, NeurIPS 2020, December 6, 2020, Online, Vancouver, Canada

Demant, M ; Kurumundayil, L ; Kunze, P ; Woernhoer, A ; Kovvali, A ; Rein, S

2020

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Machine learning with tensor decompositions
Machine learning with tensor decompositions
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Machine learning with tensor decompositions

Vannieuwenhoven, Nick

2022

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Improving Spaceflight Imagery with Machine Learning
Improving Spaceflight Imagery with Machine Learning
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Improving Spaceflight Imagery with Machine Learning

Fisher, Kenton ; Stoken, Alex

Johnson Space Center 2023

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Learning closure of fluid equations from kinetic simulations of reconnection using machine learning
Learning closure of fluid equations from kinetic simulations of reconnection using machine learning
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Learning closure of fluid equations from kinetic simulations of reconnection using machine learning

Lapenta, G ; Laperre, B ; Amaya, J ; Miloshevich, G

AGU23, 2023

AGU

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Aiding forensic investigations using machine learning
Aiding forensic investigations using machine learning
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Aiding forensic investigations using machine learning

Sharifzadeh, Hamid ; Varastehpour, Soheil ; Francis, X ; Keivanmarz, A ; Fleming, R ; Ardekani, Iman ; Newton, A

2022

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The role of Machine Learning in space science
The role of Machine Learning in space science
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The role of Machine Learning in space science

Lapenta, Giovanni

2024

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Open Source Machine Learning: Licensing and Business Models
Open Source Machine Learning: Licensing and Business Models
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Open Source Machine Learning: Licensing and Business Models

Emanuilov, Ivo

2023

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Analysis techniques for turbulent reconnecting plasmas using machine learning methods
Analysis techniques for turbulent reconnecting plasmas using machine learning methods
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Analysis techniques for turbulent reconnecting plasmas using machine learning methods

Lapenta, Giovanni

2023

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