Results 1 to 10 of about 2,716,934 (278)

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, Alehina Anna, Safonova Anastasia, Dmitriev Egor   +3 more
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Deep, deep learning with BART

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, Luo, Guanxiong, Schilling, Martin, Holme, H. Christian M., Uecker, Martin, Luo, Guanxiong; 1 Institute for Diagnostic and Interventional Radiology University Medical Center Göttingen Göttingen Germany, Schilling, Martin; 1 Institute for Diagnostic and Interventional Radiology University Medical Center Göttingen Göttingen Germany, Holme, H. Christian M.; 2 Institute of Biomedical Imaging Graz University of Technology Graz Austria   +7 more
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Deep learning [PDF]

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, Yoshua Bengio, Geoffrey E. Hinton   +2 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
doaj   +1 more source

Deep Learning

CoRR, 2018
arXiv admin note: text overlap with arXiv:1602 ...
Nicholas G. Polson, Vadim O. Sokolov
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Implicit Deep Learning [PDF]

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, Fangda Gu, Bertrand Travacca, Armin Askari, Alicia Y. Tsai   +4 more
openaire   +3 more sources

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, Jongho Park, Jongsoo Keum, Hongdo Ki, Seokho Kang   +4 more
doaj   +1 more source

Multiagent Reinforcement Learning for Strategic Decision Making and Control in Robotic Soccer Through Self-Play

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, Telma Woerle De Lima, Anderson Soares, Luckeciano Melo, Marcos R. O. A. Maximo   +4 more
doaj   +1 more source

Holistic deep learning

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, Kimberly Villalobos Carballo, Léonard Boussioux, Michael Lingzhi Li, Alex Paskov, Ivan S. Paskov   +5 more
openaire   +3 more sources

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, Patrick Zschech, Kai Heinrich   +2 more
openaire   +4 more sources