Results 11 to 20 of about 10,369,018 (353)
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
doaj +1 more source
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
openaire +3 more sources
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
core +1 more source
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
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
openaire +5 more sources
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
IEEE Transactions on Neural Networks and Learning Systems, 2018 In this paper, we propose a novel approach for efficient training of deep neural networks in a bottom-up fashion using a layered structure. Our algorithm, which we refer to as deep cascade learning, is motivated by the cascade correlation approach of Fahlman and Lebiere, who introduced it in the context of perceptrons.
Enrique S. Marquez +2 more
openaire +4 more sources
Object Detection With Deep Learning: A Review [PDF]
IEEE Transactions on Neural Networks and Learning Systems, 2018 Due to object detection’s close relationship with video analysis and image understanding, it has attracted much research attention in recent years. Traditional object detection methods are built on handcrafted features and shallow trainable architectures.
Zhong-Qiu Zhao +3 more
semanticscholar +1 more source
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
doaj +1 more source
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
semanticscholar +1 more source