QUALITY ASSESSMENT OF DIMENSIONALITY REDUCTION TECHNIQUES ON HYPERSPECTRAL DATA: A NEURAL NETWORK BASED APPROACH [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2020 Dimensionality reduction of hyperspectral images plays a vital role in remote sensing data analysis. The rapid advances in hyperspectral remote sensing has brought in a lot of opportunities to researchers to come up with advanced algorithms to analyse ...
C. Deepa, A. Shetty, A. V. Narasimhadhan
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Spiking Autoencoders With Temporal Coding
Frontiers in Neuroscience, 2021 Spiking neural networks with temporal coding schemes process information based on the relative timing of neuronal spikes. In supervised learning tasks, temporal coding allows learning through backpropagation with exact derivatives, and achieves ...
Iulia-Maria Comşa +3 more
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MONITORING DATA AGGREGATION OF DYNAMIC SYSTEMS USING INFORMATION TECHNOLOGIES
Сучасний стан наукових досліджень та технологій в промисловості, 2023 The subject matter of the article is models, methods and information technologies of monitoring data aggregation. The goal of the article is to determine the best deep learning model for reducing the dimensionality of dynamic systems monitoring data ...
Dmytro Shevchenko +2 more
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An Overview of Variational Autoencoders for Source Separation, Finance, and Bio-Signal Applications
Entropy, 2021 Autoencoders are a self-supervised learning system where, during training, the output is an approximation of the input. Typically, autoencoders have three parts: Encoder (which produces a compressed latent space representation of the input data), the ...
Aman Singh, Tokunbo Ogunfunmi
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FEATURE DESCRIPTOR BY CONVOLUTION AND POOLING AUTOENCODERS [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2015 In this paper we present several descriptors for feature-based matching based on autoencoders, and we evaluate the performance of these descriptors. In a training phase, we learn autoencoders from image patches extracted in local windows surrounding key ...
L. Chen, F. Rottensteiner, C. Heipke
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A Deep Learning Approach for Automatic Seizure Detection in Children With Epilepsy
Frontiers in Computational Neuroscience, 2021 Over the last few decades, electroencephalogram (EEG) has become one of the most vital tools used by physicians to diagnose several neurological disorders of the human brain and, in particular, to detect seizures.
Ahmed Abdelhameed, Magdy Bayoumi
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Error-Bounded Learned Scientific Data Compression with Preservation of Derived Quantities
Applied Sciences, 2022 Scientific applications continue to grow and produce extremely large amounts of data, which require efficient compression algorithms for long-term storage.
Jaemoon Lee +6 more
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Improving Large-Scale k-Nearest Neighbor Text Categorization with Label Autoencoders
Mathematics, 2022 In this paper, we introduce a multi-label lazy learning approach to deal with automatic semantic indexing in large document collections in the presence of complex and structured label vocabularies with high inter-label correlation. The proposed method is
Francisco J. Ribadas-Pena +2 more
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DANAE++: A Smart Approach for Denoising Underwater Attitude Estimation
Sensors, 2021 One of the main issues for the navigation of underwater robots consists in accurate vehicle positioning, which heavily depends on the orientation estimation phase.
Paolo Russo +2 more
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Open Set Audio Classification Using Autoencoders Trained on Few Data
Sensors, 2020 Open-set recognition (OSR) is a challenging machine learning problem that appears when classifiers are faced with test instances from classes not seen during training.
Javier Naranjo-Alcazar +4 more
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