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Revista Brasileira de Ensino de Física, 2022 As a consequence of its capability of creating high level abstractions from data, deep learning has been effectively employed in a wide range of applications, including physics. Though deep learning can be, at first and simplistically understood in terms of very large neural networks, it also encompasses new concepts and methods. In order to understand
Arruda, Henrique F. de +3 more
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, 2018 We present a novel regularization approach to train neural networks that enjoys better generalization and test error than standard stochastic gradient descent. Our approach is based on the principles of cross-validation, where a validation set is used to
A Hadjidimos +8 more
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Machine learning and deep learning [PDF]
Electronic Markets, 2021 AbstractToday, intelligent systems that offer artificial intelligence capabilities often rely on machine learning. Machine learning describes the capacity of systems to learn from problem-specific training data to automate the process of analytical model building and solve associated tasks.
Christian Janiesch +2 more
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Deep attentive video summarization with distribution consistency learning [PDF]
, 2021 This article studies supervised video summarization by formulating it into a sequence-to-sequence learning framework, in which the input and output are sequences of original video frames and their predicted importance scores, respectively.
Han, Jungong +4 more
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Magnetic Resonance in Medicine, 2022 PurposeTo develop a deep‐learning‐based image reconstruction framework for reproducible research in MRI.MethodsThe BART toolbox offers a rich set of implementations of calibration and reconstruction algorithms for parallel imaging and compressed sensing.
Blumenthal, Moritz +7 more
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Nature, 2015 Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-art in speech recognition, visual object recognition, object detection and many other domains such as drug discovery and genomics.
Yoshua Bengio +4 more
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Deep learning microscopy [PDF]
Optica, 2017 We demonstrate that a deep neural network can significantly improve optical microscopy, enhancing its spatial resolution over a large field-of-view and depth-of-field. After its training, the only input to this network is an image acquired using a regular optical microscope, without any changes to its design.
Yair Rivenson +5 more
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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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Deep learning exotic hadrons [PDF]
Physical Review D, 2022 We perform the first model independent analysis of experimental data using Deep Neural Networks to determine the nature of an exotic hadron. Specifically, we study the line shape of the $P_c(4312)$ signal reported by the LHCb collaboration and we find that its most likely interpretation is that of a virtual state.
JPAC Collaboration +8 more
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Deep learning in bioinformatics [PDF]
Briefings in Bioinformatics, 2016 In the era of big data, transformation of biomedical big data into valuable knowledge has been one of the most important challenges in bioinformatics. Deep learning has advanced rapidly since the early 2000s and now demonstrates state-of-the-art performance in various fields.
Seonwoo Min, Byunghan Lee, Sungroh Yoon
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