Results 1 to 10 of about 9,534,439 (266)
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
Henrique F. de Arruda +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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Generalization in Deep Learning
, 2020 This paper provides theoretical insights into why and how deep learning can generalize well, despite its large capacity, complexity, possible algorithmic instability, nonrobustness, and sharp minima, responding to an open question in the literature.
Bengio, Yoshua +2 more
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Using deep learning algorithms for texture segmentation of ultra-high resolution satellite images [PDF]
E3S Web of Conferences, 2021 This paper presents the results of textural segmentation of satellite images with spatial resolution
Rusin Dmitry +3 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
openaire +7 more sources
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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Review of deep learning: concepts, CNN architectures, challenges, applications, future directions
Journal of Big Data, 2021 In the last few years, the deep learning (DL) computing paradigm has been deemed the Gold Standard in the machine learning (ML) community. Moreover, it has gradually become the most widely used computational approach in the field of ML, thus achieving ...
Laith Alzubaidi +9 more
semanticscholar +1 more source
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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ParaMed: a parallel corpus for English–Chinese translation in the biomedical domain
BMC Medical Informatics and Decision Making, 2021 Background Biomedical language translation requires multi-lingual fluency as well as relevant domain knowledge. Such requirements make it challenging to train qualified translators and costly to generate high-quality translations.
Boxiang Liu, Liang Huang
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