Results 1 to 10 of about 3,002,307 (310)

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, F. Kratzert, M. Gauch, A. Keefe Sampson, J. Brandstetter, G. Klambauer, S. Hochreiter, G. Nearing   +7 more
doaj   +1 more source

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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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

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, Jan-Matthis Lueckmann, Richard Gao, Jakob H Macke   +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, Stefan Chmiela, Michael Gastegger, Kristof T. Schütt, Huziel E. Sauceda, Klaus-Robert Müller   +5 more
doaj   +1 more source

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, Michael Gastegger, Stefaan S. P. Hessmann, Klaus-Robert Müller, Kristof T. Schütt   +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, Barbara Gravel, Charlotte Nachtegael, Alexandre Renaux, Emma Verkinderen, Ann Nowé, Tom Lenaerts, Sofia Papadimitriou   +7 more
doaj   +1 more source

Polynomial-Time Constrained Message Passing for Exact MAP Inference on Discrete Models with Global Dependencies

Mathematics, 2023
Considering the worst-case scenario, the junction-tree algorithm remains the most general solution for exact MAP inference with polynomial run-time guarantees.
Alexander Bauer, Shinichi Nakajima, Klaus-Robert Müller   +2 more
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LM-GVP: an extensible sequence and structure informed deep learning framework for protein property prediction

Scientific Reports, 2022
Proteins perform many essential functions in biological systems and can be successfully developed as bio-therapeutics. It is invaluable to be able to predict their properties based on a proposed sequence and structure. In this study, we developed a novel
Zichen Wang, Steven A. Combs, Ryan Brand, Miguel Romero Calvo, Panpan Xu, George Price, Nataliya Golovach, Emmanuel O. Salawu, Colby J. Wise, Sri Priya Ponnapalli, Peter M. Clark   +10 more
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Exploring and Exploiting Conditioning of Reinforcement Learning Agents

IEEE Access, 2020
The outcome of Jacobian singular values regularization was studied for supervised learning problems. In supervised learning settings for linear and nonlinear networks, Jacobian regularization allows for faster learning.
Arip Asadulaev, Igor Kuznetsov, Gideon Stein, Andrey Filchenkov   +3 more
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