Results 11 to 20 of about 57,852 (256)
Techniques for interpretable machine learning [PDF]
Communications of the ACM, 2019 Uncovering the mysterious ways machine learning models make decisions.
Mengnan Du +2 more
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Diagnostics, 2022 Heart disease is one of the leading causes of mortality throughout the world. Among the different heart diagnosis techniques, an electrocardiogram (ECG) is the least expensive non-invasive procedure. However, the following are challenges: the scarcity of
Yehualashet Megersa Ayano +3 more
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Interpretable machine learning methods for predictions in systems biology from omics data
Frontiers in Molecular Biosciences, 2022 Machine learning has become a powerful tool for systems biologists, from diagnosing cancer to optimizing kinetic models and predicting the state, growth dynamics, or type of a cell.
David Sidak +5 more
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Interpretable machine learning in Physics
CoRR, 2022 Adding interpretability to multivariate methods creates a powerful synergy for exploring complex physical systems with higher order correlations while bringing about a degree of clarity in the underlying dynamics of the system.
Grojean, Christophe +3 more
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Computer Methods and Programs in Biomedicine Update, 2023 Background: Machine learning (ML) has demonstrated success in classifying patients’ diagnostic outcomes in free-text clinical notes. However, due to the machine learning model's complexity, interpreting the mechanism behind classification results remains
Tong Ling +5 more
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Interpretable machine learning with an ensemble of gradient boosting machines [PDF]
Knowledge-Based Systems, 2021 A method for the local and global interpretation of a black-box model on the basis of the well-known generalized additive models is proposed. It can be viewed as an extension or a modification of the algorithm using the neural additive model. The method is based on using an ensemble of gradient boosting machines (GBMs) such that each GBM is learned on ...
Andrei V. Konstantinov, Lev V. Utkin
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Interpretable Differencing of Machine Learning Models
CoRR, 2023 UAI ...
Swagatam Haldar +4 more
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Interpretable machine learning for materials design
Journal of Materials Research, 2023 Fueled by the widespread adoption of Machine Learning (ML) and the high-throughput screening of materials, the data-centric approach to materials design has asserted itself as a robust and powerful tool for the in-silico prediction of materials properties.
James Dean +5 more
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Ultra-fast interpretable machine-learning potentials
npj Computational Materials, 2023 All-atom dynamics simulations are an indispensable quantitative tool in physics, chemistry, and materials science, but large systems and long simulation times remain challenging due to the trade-off between computational efficiency and predictive ...
Stephen R. Xie +2 more
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MethodsX, 2023 Wastewater-based epidemiology has emerged as a viable tool for monitoring disease prevalence in a population. This paper details a time series machine learning (TSML) method for predicting COVID-19 cases from wastewater and environmental variables.
Mallory Lai +4 more
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