Machine-Learning Methods for Computational Science and Engineering [PDF]
Computation, 2020 The re-kindled fascination in machine learning (ML), observed over the last few decades, has also percolated into natural sciences and engineering. ML algorithms are now used in scientific computing, as well as in data-mining and processing. In this paper, we provide a review of the state-of-the-art in ML for computational science and engineering.
Michael Frank +2 more
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Machine learning in glaucoma: a bibliometric analysis comparing computer science and medical fields’ research [PDF]
Expert Review of Ophthalmology, 2021 Saif Aldeen AlRyalat +2 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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Computer Science and Machine Learning Trends 2023 [PDF]
, 2023 Bob Zigon, Fengguang Song
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Extending the reach of quantum computing for materials science with machine learning potentials
AIP Advances, 2022 Solving electronic structure problems represents a promising field of applications for quantum computers. Currently, much effort is spent in devising and optimizing quantum algorithms for near-term quantum processors, with the aim of outperforming classical counterparts on selected problem instances using limited quantum resources.
Julian Schuhmacher +6 more
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Provenance Data in the Machine Learning Lifecycle in Computational Science and Engineering [PDF]
2019 IEEE/ACM Workflows in Support of Large-Scale Science (WORKS), 2019 Machine Learning (ML) has become essential in several industries. In Computational Science and Engineering (CSE), the complexity of the ML lifecycle comes from the large variety of data, scientists' expertise, tools, and workflows. If data are not tracked properly during the lifecycle, it becomes unfeasible to recreate a ML model from scratch or to ...
Souza, Renan +12 more
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Researcher reasoning meets computational capacity: Machine learning for social science
Social Science Research, 2022 Computational power and big data have created new opportunities to explore and understand the social world. A special synergy is possible when social scientists combine human attention to certain aspects of the problem with the power of algorithms to automate other aspects of the problem. We review selected exemplary applications where machine learning
Ian Lundberg +2 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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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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