Results 71 to 80 of about 9,750,100 (244)
A Euclidean transformer for fast and stable machine learned force fields
Nature CommunicationsRecent years have seen vast progress in the development of machine learned force fields (MLFFs) based on ab-initio reference calculations. Despite achieving low test errors, the reliability of MLFFs in molecular dynamics (MD) simulations is facing ...
J. Thorben Frank +3 more
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Scientific Reports, 2023 In this study, an automated 2D machine learning approach for fast and precise segmentation of MS lesions from multi-modal magnetic resonance images (mmMRI) is presented.
Florian Raab +4 more
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Algorithms & Fiduciaries: Existing and Proposed Regulatory Approaches to Artificially Intelligent Financial Planners [PDF]
, 2017 Artificial intelligence is no longer solely in the realm of science fiction. Today, basic forms of machine learning algorithms are commonly used by a variety of companies.
Lightbourne, John
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Experimental demonstration of quantum learning speed-up with classical input data
, 2018 We consider quantum-classical hybrid machine learning in which large-scale input channels remain classical and small-scale working channels process quantum operations conditioned on classical input data. This does not require the conversion of classical (
Bang, Jeongho +6 more
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Introduction to Machine Learning [PDF]
, 2013 The machine learning field, which can be briefly defined as enabling computers make successful predictions using past experiences, has exhibited an impressive development recently with the help of the rapid increase in the storage capacity and processing power of computers. Together with many other disciplines, machine learning methods have been widely
Baştanlar, Yalın, Özuysal, Mustafa
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Maximum Entropy Approach to Massive Graph Spectrum Learning with Applications
Algorithms, 2022 We propose an alternative maximum entropy approach to learning the spectra of massive graphs. In contrast to state-of-the-art Lanczos algorithm for spectral density estimation and applications thereof, our approach does not require kernel smoothing.
Diego Granziol +5 more
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The Journal of Physical Chemistry Letters, 2018 William F. Schneider, Hua Guo
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, 2020 Talk at the 3rd NOAA Workshop on leveraging AI in Environmental Sciences;
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Using machine learning techniques for sentiment analysis [PDF]
, 2017 The Natural language processing is the discipline that studies how to make the machines read and interpret the language that the people use, the natural language.
Romero Llombart, Òscar +1 more
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Machine learning for neuroscience [PDF]
Neural Systems & Circuits, 2011 What is machine learning? Machine learning is a type of statistics that places particular emphasis on the use of advanced computational algorithms. As computers become more powerful, and modern experimental methods in areas such as imaging generate vast bodies of data, machine learning is becoming ever more important for extracting reliable and ...
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