Results 31 to 40 of about 1,147,666 (317)
, 2019 In the ML fairness literature, there have been few investigations through the viewpoint of philosophy, a lens that encourages the critical evaluation of basic assumptions. The purpose of this paper is to use three ideas from the philosophy of science and computer science to tease out blind spots in the assumptions that underlie ML fairness: abstraction,
Samuel Deng, Achille C. Varzi
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Overview: Computer Vision and Machine Learning for Microstructural Characterization and Analysis [PDF]
Metallurgical and Materials Transactions. A, 2020 Microstructural characterization and analysis is the foundation of microstructural science, connecting materials structure to composition, process history, and properties.
E. Holm +6 more
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Bridging learning sciences, machine learning and affective computing for understanding cognition and affect in collaborative learning [PDF]
British Journal of Educational Technology, 2020 AbstractCollaborative learning (CL) can be a powerful method for sharing understanding between learners. To this end, strategic regulation of processes, such as cognition and affect (including metacognition, emotion and motivation) is key. Decades of research on self‐regulated learning has advanced our understanding about the need for and complexity of
Järvelä, S. (Sanna) +4 more
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Trend Prediction for Computer Science Research Topics Using Extreme Learning Machine
Procedia Engineering, 2012 Novita Sari +2 more
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Philosophy of science at sea: Clarifying the interpretability of machine learning
Philosophy Compass, 2022 In computer science, there are efforts to make machine learning more interpretable or explainable, and thus to better understand the underlying models ...
C. Beisbart, Tim Räz
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Iraqi Journal for Computer Science and Mathematics, 2023 People in the modern era spend most of their lives in virtual environments that offer a range of public and private services and social platforms. Therefore, these environments need to be protected from cyber attackers that can steal data or disrupt ...
Maad M. Mijwil
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Journal of Science Education and Technology, 2022 Science education researchers typically face a trade-off between more quantitatively oriented confirmatory testing of hypotheses, or more qualitatively oriented exploration of novel hypotheses.
P. Wulff +5 more
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Neuron, 2023 Neuroscience, cognitive science, and computer science are increasingly benefiting through their interactions. This could be accelerated by direct sharing of computational models across disparate modeling software used in each. We describe a Model Description Format designed to meet this challenge.
Gleeson, Padraig +6 more
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A general guide to applying machine learning to computer architecture [PDF]
, 2018 The resurgence of machine learning since the late 1990s has been enabled by significant advances in computing performance and the growth of big data.
Arkose, Tugberk +6 more
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Discriminative Cooperative Networks for Detecting Phase Transitions [PDF]
, 2018 The classification of states of matter and their corresponding phase transitions is a special kind of machine-learning task, where physical data allow for the analysis of new algorithms, which have not been considered in the general computer-science ...
Liu, Ye-Hua +1 more
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