Results 21 to 30 of about 66,762 (195)
Interpretable Models in Probabilistic Machine Learning
, 2019 This thesis describes contributions to the field of interpretable models in probabilistic machine learning, by first outlining the desiderata and properties associated with the term interpretability. We claim that probabilistic models are suitable candidates for interpretable machine learning, and this claim is supported by examples of such models that
Hyunjik Kim, Kim, Hyunjik
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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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Mixture of Decision Trees for Interpretable Machine Learning
, 2023 11751182This work introduces a novel interpretable machine learning method called Mixture of Decision Trees (MoDT). It constitutes a special case of the Mixture of Experts ensemble architecture, which utilizes a linear model as gating function and ...
Huber, Marco +3 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
doaj +1 more source
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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ENHANCING LOAN APPROVAL DECISION-MAKING: AN INTERPRETABLE MACHINE LEARNING APPROACH USING LIGHTGBM FOR DIGITAL ECONOMY DEVELOPMENT [PDF]
Malaysian Journal of ComputingThis study aims to enhance loan approval decision-making in the digital economy using an interpretable machine learning approach. The primary research question investigates how integrating an interpretable machine learning approach can improve the ...
Teuku Rizky Noviandy +2 more
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Informed, Interactive, and Interpretable Machine Learning for Forward Kinematics of Robot Arms [PDF]
, 2022 Machine learning (ML) is becoming increasingly sought after in diverse domains. Unfortunately for this objective, most ML research has focused too much on improving performance on evaluation metrics such as accuracy to the exclusion of other qualities ...
Kanneganti, Sai Teja
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Interpretable machine learning on metabolomics data reveals biomarkers for Parkinson’s disease
, 2022 The use of machine learning (ML) with metabolomics provides opportunities for the early diagnosis of disease. However, the accuracy and extent of information obtained from ML and metabolomics can be limited owing to challenges associated with ...
J. Diana, Zhang +3 more
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ImmunoInformatics, 2023 The recognition of an epitope by a T-cell receptor (TCR) is crucial for eliminating pathogens and establishing immunological memory. Prediction of the binding of any TCR–epitope pair is still a challenging task, especially for novel epitopes, because the
Ceder Dens +4 more
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Interpretable Neural-Symbolic Concept Reasoning [PDF]
, 2023 Deep learning methods are highly accurate, yet their opaque decision process prevents them from earning full human trust. Concept-based models aim to address this issue by learning tasks based on a set of human-understandable concepts.
Pietro Lio +9 more
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