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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]
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]
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 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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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
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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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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Unsupervised Anomaly Detection Using Style Distillation
Autoencoders (AEs) have been widely used for unsupervised anomaly detection. They learn from normal samples such that they produce high reconstruction errors for anomalous samples.
Hwehee Chung+4 more
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Cast-in-place anchors are being increasingly used in many applications including building construction, bridge, and power plants. The anchorage to concrete systems are subjected to tensile, shear and combined loads from a variety of loading circumstances
Quoc To Bao+4 more
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An Improved Blind Kriging Surrogate Model for Design Optimization Problems
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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