Results 271 to 280 of about 113,519 (298)

Efficient Deep Wavelet Gaussian Markov Dempster-Shafer Network-Based Spectrum Sensing at Very Low SNR in Cognitive Radio Networks. [PDF]

Sensors (Basel)
Jatti S, Tyagi A.
europepmc   +1 more source

Anomaly Detection and Objective Security Evaluation Using Autoencoder, Isolation Forest, and Multi-Criteria Decision Methods. [PDF]

Sensors (Basel)
Zhang H, Zhang H.
europepmc   +1 more source

Deep Learning Approaches for Classifying Children With and Without Autism Spectrum Disorder Using Inertial Measurement Unit Hand Tracking Data: Comparative Study.

JMIR Med Inform
Mutersbaugh J, Su WC, Bhat A, Gandjbakhche A.   +3 more
europepmc   +1 more source

Representation learning of single-cell time-series with deep variational autoencoders

Fraisse A, Oyarzún DA, Karoui ME.
europepmc   +1 more source

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Autoencoders

2020
The study of psychiatric and neurologic disorders typically involves the acquisition of a wide range of different types of data, such as brain images, electronic health records, and mobile phone sensors data. Each type of data has its unique temporal and spatial characteristics, and the process of extracting useful information from them can be very ...
Lopez Pinaya, Walter Hugo, Vieira, Sandra, Garcia-dias, Rafael, Mechelli, Andrea   +3 more
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Geometry Regularized Autoencoders

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023
A fundamental task in data exploration is to extract low dimensional representations that capture intrinsic geometry in data, especially for faithfully visualizing data in two or three dimensions. Common approaches use kernel methods for manifold learning.
Andres F. Duque, Sacha Morin, Guy Wolf, Kevin R. Moon   +3 more
openaire   +2 more sources

Autoencoder in Autoencoder Networks

IEEE Transactions on Neural Networks and Learning Systems
Modeling complex correlations on multiview data is still challenging, especially for high-dimensional features with possible noise. To address this issue, we propose a novel unsupervised multiview representation learning (UMRL) algorithm, termed autoencoder in autoencoder networks (AE2-Nets).
Changqing Zhang, Yu Geng, Zongbo Han, Yeqing Liu, Huazhu Fu, Qinghua Hu   +5 more
openaire   +2 more sources