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Convolutional neural networks for heat conduction
Case Studies in Thermal Engineering, 2022 This paper presents a data-driven approach to solve heat conduction problems, in particular 2D heat conduction problems. The physical laws which govern such problems are modeled by partial differential equations.
Sidharth Tadeparti +1 more
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, 2016 The goal of these course notes is to describe the main mathematical ideas behind geometric deep learning and to provide implementation details for several applications in shape analysis and synthesis, computer vision and computer graphics.
Andreux M. +31 more
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Building Program Vector Representations for Deep Learning [PDF]
, 2014 Deep learning has made significant breakthroughs in various fields of artificial intelligence. Advantages of deep learning include the ability to capture highly complicated features, weak involvement of human engineering, etc.
Jin, Zhi +6 more
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Applied Sciences, 2021 To properly restore masonry cultural heritage sites, the materials used for retrofitting can have a critical effect, and this requires standards for traditional Korean brick and lime mortar to be examined.
Gayoon Lee +4 more
doaj +1 more source
Deep learning in deep time [PDF]
Proceedings of the National Academy of Sciences, 2020 Digitized natural history records, now numbering in the billions (1), span widely across the tree of life and provide the foundation for numerous recent advances in biodiversity research (2, 3). Mechanistic insights are emerging for old questions, including how diversity has expanded and contracted through Earth’s history (4), how species have come to ...
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IEEE Transactions on Neural Networks and Learning Systems, 2018 In this paper, we propose a novel approach for efficient training of deep neural networks in a bottom-up fashion using a layered structure. Our algorithm, which we refer to as deep cascade learning, is motivated by the cascade correlation approach of Fahlman and Lebiere, who introduced it in the context of perceptrons.
Enrique S. Marquez +2 more
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Opening the Black Box of Financial AI with CLEAR-Trade: A CLass-Enhanced Attentive Response Approach for Explaining and Visualizing Deep Learning-Driven Stock Market Prediction [PDF]
, 2017 Deep learning has been shown to outperform traditional machine learning algorithms across a wide range of problem domains. However, current deep learning algorithms have been criticized as uninterpretable "black-boxes" which cannot explain their decision
Kumar, Devinder +2 more
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, 2023 Deep Learning (DL) has made a major impact on data science in the last decade. This chapter introduces the basic concepts of this field. It includes both the basic structures used to design deep neural networks and a brief survey of some of its popular use cases.
Lihi Shiloh-Perl, Raja Giryes
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Deep learning and geometric deep learning: An introduction for mathematicians and physicists
International Journal of Geometric Methods in Modern Physics, 2023 In this expository paper, we want to give a brief introduction, with few key references for further reading, to the inner functioning of the new and successful algorithms of Deep Learning and Geometric Deep Learning with a focus on Graph Neural Networks.
R. Fioresi, F. Zanchetta
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Deep learning in remote sensing: a review [PDF]
, 2017 Standing at the paradigm shift towards data-intensive science, machine learning techniques are becoming increasingly important. In particular, as a major breakthrough in the field, deep learning has proven as an extremely powerful tool in many fields ...
Fraundorfer, Friedrich +6 more
core +4 more sources