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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
Henrique F. de Arruda +3 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
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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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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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IEEE Access, 2022 Reinforcement Learning (RL) has shown promising performance in environments for both robotic control and strategic decision making. However, they are usually treated as separate problems with different objectives.
Bruno Brandao +4 more
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Image Segmentation Using Deep Learning: A Survey [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020 Image segmentation is a key task in computer vision and image processing with important applications such as scene understanding, medical image analysis, robotic perception, video surveillance, augmented reality, and image compression, among others, and ...
Shervin Minaee +5 more
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An Improved Blind Kriging Surrogate Model for Design Optimization Problems
Mathematics, 2022 Surrogate modeling techniques are widely employed in solving constrained expensive black-box optimization problems. Therein, Kriging is among the most popular surrogates in which the trend function is considered as a constant mean.
Hau T. Mai +4 more
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Deep Learning with Differential Privacy [PDF]
Conference on Computer and Communications Security, 2016 Machine learning techniques based on neural networks are achieving remarkable results in a wide variety of domains. Often, the training of models requires large, representative datasets, which may be crowdsourced and contain sensitive information.
Martín Abadi +6 more
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