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
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
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
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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Machine Learning: Algorithms, Real-World Applications and Research Directions
SN Computer Science, 2021 In the current age of the Fourth Industrial Revolution (4IR or Industry 4.0), the digital world has a wealth of data, such as Internet of Things (IoT) data, cybersecurity data, mobile data, business data, social media data, health data, etc.
Iqbal H. Sarker
semanticscholar +1 more source
A Survey on Bias and Fairness in Machine Learning [PDF]
ACM Computing Surveys, 2019 With the widespread use of artificial intelligence (AI) systems and applications in our everyday lives, accounting for fairness has gained significant importance in designing and engineering of such systems.
Ninareh Mehrabi +4 more
semanticscholar +1 more source
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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Membership Inference Attacks Against Machine Learning Models [PDF]
IEEE Symposium on Security and Privacy, 2016 We quantitatively investigate how machine learning models leak information about the individual data records on which they were trained. We focus on the basic membership inference attack: given a data record and black-box access to a model, determine if ...
R. Shokri +3 more
semanticscholar +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 +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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