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Uncertainty estimation with deep learning for rainfall–runoff modeling [PDF]
Hydrology and Earth System Sciences, 2022 Deep learning is becoming an increasingly important way to produce accurate hydrological predictions across a wide range of spatial and temporal scales.
D. Klotz +7 more
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Acknowledgment to the Reviewers of Machine Learning and Knowledge Extraction in 2022
Machine Learning and Knowledge Extraction, 2023 High-quality academic publishing is built on rigorous peer review [...]
Machine Learning and Knowledge Extraction Editorial Office
doaj +1 more source
Acknowledgment to Reviewers of Machine Learning and Knowledge Extraction in 2021
Machine Learning and Knowledge Extraction, 2022 Rigorous peer-reviews are the basis of high-quality academic publishing [...]
Machine Learning and Knowledge Extraction Editorial Office
doaj +1 more source
Machine learning and deep learning [PDF]
Electronic Markets, 2021 AbstractToday, intelligent systems that offer artificial intelligence capabilities often rely on machine learning. Machine learning describes the capacity of systems to learn from problem-specific training data to automate the process of analytical model building and solve associated tasks.
Christian Janiesch +2 more
openaire +7 more sources
Flexible and efficient simulation-based inference for models of decision-making
eLife, 2022 Inferring parameters of computational models that capture experimental data is a central task in cognitive neuroscience. Bayesian statistical inference methods usually require the ability to evaluate the likelihood of the model—however, for many models ...
Jan Boelts +3 more
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SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects
Nature Communications, 2021 Current machine-learned force fields typically ignore electronic degrees of freedom. SpookyNet is a deep neural network that explicitly treats electronic degrees of freedom, closing an important remaining gap for models in quantum chemistry.
Oliver T. Unke +5 more
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Inverse design of 3d molecular structures with conditional generative neural networks
Nature Communications, 2022 The targeted discovery of molecules with specific structural and chemical properties is an open challenge in computational chemistry. Here, the authors propose a conditional generative neural network for the inverse design of 3d molecular structures.
Niklas W. A. Gebauer +4 more
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Faster and more accurate pathogenic combination predictions with VarCoPP2.0
BMC Bioinformatics, 2023 Background The prediction of potentially pathogenic variant combinations in patients remains a key task in the field of medical genetics for the understanding and detection of oligogenic/multilocus diseases.
Nassim Versbraegen +7 more
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Machine Learning in the Air [PDF]
IEEE Journal on Selected Areas in Communications, 2019 Thanks to the recent advances in processing speed and data acquisition and storage, machine learning (ML) is penetrating every facet of our lives, and transforming research in many areas in a fundamental manner. Wireless communications is another success story -- ubiquitous in our lives, from handheld devices to wearables, smart homes, and automobiles.
D. Gunduz +5 more
openaire +4 more sources
A Machine Learning Tutorial for Operational Meteorology, Part I: Traditional Machine Learning [PDF]
Weather and Forecasting 37 (2022) 1509-1529, 2022 Recently, the use of machine learning in meteorology has increased greatly. While many machine learning methods are not new, university classes on machine learning are largely unavailable to meteorology students and are not required to become a meteorologist. The lack of formal instruction has contributed to perception that machine learning methods are
arxiv +1 more source