Results 31 to 40 of about 120,084 (296)
Deep Label Distribution Learning With Label Ambiguity [PDF]
IEEE Transactions on Image Processing, 2017 Convolutional Neural Networks (ConvNets) have achieved excellent recognition performance in various visual recognition tasks. A large labeled training set is one of the most important factors for its success. However, it is difficult to collect sufficient training images with precise labels in some domains such as apparent age estimation, head pose ...
Bin-Bin Gao +4 more
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Distributed Training and Inference of Deep Learning Models for Multi-Modal Land Cover Classification
Remote Sensing, 2020 Deep Neural Networks (DNNs) have established themselves as a fundamental tool in numerous computational modeling applications, overcoming the challenge of defining use-case-specific feature extraction processing by incorporating this stage into unified ...
Maria Aspri +2 more
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Distributed optimization for deep learning with gossip exchange [PDF]
Neurocomputing, 2019 Abstract We address the issue of speeding up the training of convolutional neural networks by studying a distributed method adapted to stochastic gradient descent. Our parallel optimization setup uses several threads, each applying individual gradient descents on a local variable.
Blot, Michael +3 more
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Quantum distributed deep learning architectures: Models, discussions, and applications
, 2022 Quantum distributed deep learning architectures: Models, discussions, and ...
WJ Yun (14375763) +7 more
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Deep Learning Beyond the Training Distribution
, 2021 One of the goals of artificial intelligence is to create machines that can think like humans. Deep learning has been at the core of the remarkable progress made towards this goal. Large artificial neural networks trained on massive datasets can master tasks across vastly different domains. Despite the progress on i.i.d.
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Natural Compression for Distributed Deep Learning
CoRR, 2019 Proceedings of 3${}^{\text{rd}}$ Annual Conference on Mathematical and Scientific Machine Learning (MSML 2022)
Samuel Horváth +5 more
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Depression Detection Based on Deep Distribution Learning [PDF]
2019 IEEE International Conference on Image Processing (ICIP), 2019 Major depressive disorder is among the most common and harmful mental health problems. Several deep learning architectures have been proposed for video-based detection of depression based on the facial expressions of subjects. To predict the depression level, these architectures are often modeled for regression with Euclidean loss.
Wheidima Carneiro de Melo +2 more
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IET Radar, Sonar & Navigation, 2021 Deep‐learning‐based radar imaging is developed with distributed frequency modulated continuous waveform multiple‐input multiple‐output (FMCW MIMO) radars in which a deep‐learning approach based on the convolutional neural network (CNN) is proposed to ...
Jiho Seo +3 more
doaj +1 more source
Skeptical Deep Learning with Distribution Correction
CoRR, 2018 Recently deep neural networks have been successfully used for various classification tasks, especially for problems with massive perfectly labeled training data. However, it is often costly to have large-scale credible labels in real-world applications. One solution is to make supervised learning robust with imperfectly labeled input. In this paper, we
Mingxiao An +6 more
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DDLS: Distributed Deep Learning Systems: A Review
, 2021 The clustered deep learning systems practice deep neural model networks with a cluster pooled resources aid. Distributed profound learning systems engineers should make multiple choices to process their diverse workloads successfully in their selected ...
et. al., Najdavan Abduljawad Kako,
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