Results 31 to 40 of about 7,921,142 (232)
De Computis, 2023 In recent years, deep learning (DL) has been the most popular computational approach in the field of machine learning (ML), achieving exceptional results on a variety of complex cognitive tasks, matching or even surpassing human performance.
Mohammad Mustafa Taye
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Interpretable Machine Learning
Hands-On Machine Learning with R, 2019 Interpretable machine learning has become a popular research direction as deep neural networks (DNNs) have become more powerful and their applications more mainstream, yet DNNs remain difficult to understand. Testing with Concept Activation Vectors, TCAV,
Bradley C. Boehmke, Brandon M. Greenwell
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Machine Learning for Fluid Mechanics [PDF]
Annual Review of Fluid Mechanics, 2019 The field of fluid mechanics is rapidly advancing, driven by unprecedented volumes of data from experiments, field measurements, and large-scale simulations at multiple spatiotemporal scales. Machine learning (ML) offers a wealth of techniques to extract
S. Brunton, B. R. Noack, P. Koumoutsakos
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Small data machine learning in materials science
npj Computational Materials, 2023 This review discussed the dilemma of small data faced by materials machine learning. First, we analyzed the limitations brought by small data. Then, the workflow of materials machine learning has been introduced.
Pengcheng Xu +3 more
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Quantum-chemical insights from deep tensor neural networks
Nature Communications, 2017 Machine learning is an increasingly popular approach to analyse data and make predictions. Here the authors develop a ‘deep learning’ framework for quantitative predictions and qualitative understanding of quantum-mechanical observables of chemical ...
Kristof T. Schütt +4 more
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SoilGrids250m: Global gridded soil information based on machine learning
PLoS ONE, 2017 This paper describes the technical development and accuracy assessment of the most recent and improved version of the SoilGrids system at 250m resolution (June 2016 update).
T. Hengl +18 more
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Ideal Learning Machines* [PDF]
Cognitive Science, 1982 We examine the prospects for finding “best possible” or “ideal” computing machines for various learning tasks. For this purpose, several precise senses of “ideal machine” are considered within the context of formal learning theory. Generally negative results are provided concerning the existence of ideal learning‐machines in the senses considered.
D OSHERSON, M STOB, S WEINSTEIN
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Machine learning in automated text categorization [PDF]
CSUR, 2001 The automated categorization (or classification) of texts into predefined categories has witnessed a booming interest in the last 10 years, due to the increased availability of documents in digital form and the ensuing need to organize them.
F. Sebastiani
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Machine learning and radiology [PDF]
Medical Image Analysis, 2012 In this paper, we give a short introduction to machine learning and survey its applications in radiology. We focused on six categories of applications in radiology: medical image segmentation, registration, computer aided detection and diagnosis, brain function or activity analysis and neurological disease diagnosis from fMR images, content-based image
Shijun, Wang, Ronald M, Summers
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Teleconnection Patterns of Different El Niño Types Revealed by Climate Network Curvature
Geophysical Research Letters, 2022 The diversity of El Niño events is commonly described by two distinct flavors, the Eastern Pacific (EP) and Central Pacific (CP) type. While the remote impacts, that is, teleconnections, of EP and CP events have been studied for different regions ...
Felix M. Strnad +3 more
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