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Deep Learning for Causal Inference
, 2023 This primer systematizes the emerging literature on causal inference using deep neural networks under the potential outcomes framework. It provides an intuitive introduction on building and optimizing custom deep learning models and shows how to adapt ...
Tim Sainburg +5 more
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Deep Representation Learning for Speech Emotion Recognition [PDF]
, 2022 The success of machine learning (ML) algorithms generally depends on the quality of data representation or features. Good representations of the data make it easier to develop machine learning predictors or even deep learning (DL) classifiers.
Latif, Siddique
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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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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
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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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Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering, 2016 Developers often wonder how to implement a certain functionality (e.g., how to parse XML files) using APIs. Obtaining an API usage sequence based on an API-related natural language query is very helpful in this regard. Given a query, existing approaches utilize information retrieval models to search for matching API sequences.
Xiaodong Gu 0002 +3 more
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Quantum Information and Computation, 2016 In recent years, deep learning has had a profound impact on machine learning and artificial intelligence. At the same time, algorithms for quantum computers have been shown to efficiently solve some problems that are intractable on conventional, classical computers.
Nathan Wiebe +2 more
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IEEE Access, 2020 A Steel Plate Rolling Mill (SPM) is a milling machine that uses rollers to press hot slab inputs to produce ferrous or non-ferrous metal plates. To produce high-quality steel plates, it is important to precisely detect and sense values of manufacturing ...
Cheol Young Park +3 more
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