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Learning from machine learning [PDF]
Journal of Vascular Surgery, 2022 A discussion of the rapidly evolving realm of machine learning.
Ted G. Lewis, Peter J. Denning
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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 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
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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
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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Quantum machine learning [PDF]
Nature, 2017 Fuelled by increasing computer power and algorithmic advances, machine learning techniques have become powerful tools for finding patterns in data. Since quantum systems produce counter-intuitive patterns believed not to be efficiently produced by classical systems, it is reasonable to postulate that quantum computers may outperform classical computers
Jacob Biamonte +5 more
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Machine learning-based prediction of a BOS reactor performance from operating parameters [PDF]
, 2020 A machine learning-based analysis was applied to process data obtained from a Basic Oxygen Steelmaking (BOS) pilot plant. The first purpose was to identify correlations between operating parameters and reactor performance, defined as rate of ...
Li, Zushu +2 more
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