Results 1 to 10 of about 2,731,437 (281)
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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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.
Yann LeCun +2 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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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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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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SIAM Journal on Mathematics of Data Science, 2021 Implicit deep learning prediction rules generalize the recursive rules of feedforward neural networks. Such rules are based on the solution of a fixed-point equation involving a single vector of hidden features, which is thus only implicitly defined.
Laurent El Ghaoui +4 more
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Unsupervised Anomaly Detection Using Style Distillation
IEEE Access, 2020 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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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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Scientific Reports, 2023 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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Machine Learning, 2023 AbstractThis paper presents a novel holistic deep learning framework that simultaneously addresses the challenges of vulnerability to input perturbations, overparametrization, and performance instability from different train-validation splits. The proposed framework holistically improves accuracy, robustness, sparsity, and stability over standard deep ...
Dimitris Bertsimas +5 more
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