Results 11 to 20 of about 1,212,418 (284)
Aerospace, 2022 The rapid growth of global aviation operations has made its negative environmental impact an international concern. Accurate modeling of aircraft fuel burn, emissions, and noise is the prerequisite for informing new operational procedures, technologies ...
Zhenyu Gao, D. Mavris
semanticscholar +1 more source
NMR in Metabolomics: From Conventional Statistics to Machine Learning and Neural Network Approaches
Applied Sciences, 2022 NMR measurements combined with chemometrics allow achieving a great amount of information for the identification of potential biomarkers responsible for a precise metabolic pathway. These kinds of data are useful in different fields, ranging from food to
C. Corsaro +5 more
semanticscholar +1 more source
Remote Sensing, 2021 Flash floods are considered to be one of the most destructive natural hazards, and they are difficult to accurately model and predict. In this study, three hybrid models were proposed, evaluated, and used for flood susceptibility prediction in the Dadu ...
Jun Liu +6 more
semanticscholar +1 more source
When and How to Apply Statistics, Machine Learning and Deep Learning Techniques
International Conference on Transparent Optical Networks, 2018 Machine Learning has become 'commodity' in engineering and experimental sciences, as calculus and statistics did before. After the hype produced during the 00's, machine learning (statistical learning, neural networks, etc.) has become a solid and ...
Josep Lluis Berral-Garcia
semanticscholar +1 more source
Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges [PDF]
Statistics Survey, 2021 Interpretability in machine learning (ML) is crucial for high stakes decisions and troubleshooting. In this work, we provide fundamental principles for interpretable ML, and dispel common misunderstandings that dilute the importance of this crucial topic.
C. Rudin +5 more
semanticscholar +1 more source
Machine Learning for Statistical Modeling [PDF]
ACM Transactions on Design Automation of Electronic Systems, 2021 We propose a methodology to perform process variation-aware device and circuit design using fully physics-based simulations within limited computational resources, without developing a compact model. Machine learning (ML), specifically a support vector regression (SVR) model, has been used. The SVR model has been trained using a dataset of
Urmimala Roy +5 more
openaire +1 more source
Debiased Machine Learning U-statistics
, 2022 We propose a method to debias estimators based on U-statistics with Machine Learning (ML) first-steps. Standard plug-in estimators often suffer from regularization and model-selection biases, producing invalid inferences. We show that Debiased Machine Learning (DML) estimators can be constructed within a U-statistics framework to correct these biases ...
Escanciano, Juan Carlos +1 more
openaire +2 more sources
bioRxiv, 2023 Personalized interventions are deemed vital given the intricate characteristics, advancement, inherent genetic composition, and diversity of cardiovascular diseases (CVDs). The appropriate utilization of artificial intelligence (AI) and machine learning (
William DeGroat +5 more
semanticscholar +1 more source
Machine Learning: Deepest Learning as Statistical Data Assimilation Problems
Neural Computation, 2018 We formulate an equivalence between machine learning and the formulation of statistical data assimilation as used widely in physical and biological sciences. The correspondence is that layer number in a feedforward artificial network setting is the analog of time in the data assimilation setting.
Abarbanel, Henry D. I. +2 more
openaire +3 more sources