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Machine Learning for Testing Machine-Learning Hardware
Proceedings of the 41st IEEE/ACM International Conference on Computer-Aided Design, 2022Arjun Chaudhuri +2 more
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Unsupervised Machine Learning and Beyond Machine Learning
2023Keisuke Takahashi, Lauren Takahashi
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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
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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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