Results 21 to 30 of about 14,357 (268)
IEEE Access, 2021 Efficient modeling of high-dimensional data requires extracting only relevant dimensions through feature learning. Unsupervised feature learning has gained tremendous attention due to its unbiased approach, no need for prior knowledge or expensive manual
Chathurika S. Wickramasinghe +2 more
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Biological Cybernetics, 2022 AbstractIn Bourlard and Kamp (Biol Cybern 59(4):291–294, 1998), it was theoretically proven that autoencoders (AE) with single hidden layer (previously called “auto-associative multilayer perceptrons”) were, in the best case, implementing singular value decomposition (SVD) Golub and Reinsch (Linear algebra, Singular value decomposition and least ...
Hervé Bourlard, Selen Hande Kabil
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CoRR, 2023 The Variational Autoencoder (VAE) is a seminal approach in deep generative modeling with latent variables. Interpreting its reconstruction process as a nonlinear transformation of samples from the latent posterior distribution, we apply the Unscented Transform (UT) -- a well-known distribution approximation used in the Unscented Kalman Filter (UKF ...
Faris Janjos +3 more
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Applied Sciences, 2020 As defect detection using machine vision is diversifying and expanding, approaches using deep learning are increasing. Recently, there have been much research for detecting and classifying defects using image segmentation, image detection, and image ...
Young-Joo Han, Ha-Jin Yu
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Structuring Autoencoders [PDF]
2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW), 2019 In this paper we propose Structuring AutoEncoders (SAE). SAEs are neural networks which learn a low dimensional representation of data which are additionally enriched with a desired structure in this low dimensional space. While traditional Autoencoders have proven to structure data naturally they fail to discover semantic structure that is hard to ...
Marco Rudolph +2 more
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CoRR, 2023 Synthetic data generation is increasingly important due to privacy concerns. While Autoencoder-based approaches have been widely used for this purpose, sampling from their latent spaces can be challenging. Mixture models are currently the most efficient way to sample from these spaces.
Aymene Mohammed Bouayed, David Naccache
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Self-Net: Lifelong Learning via Continual Self-Modeling
Frontiers in Artificial Intelligence, 2020 Learning a set of tasks over time, also known as continual learning (CL), is one of the most challenging problems in artificial intelligence. While recent approaches achieve some degree of CL in deep neural networks, they either (1) store a new network ...
Jaya Krishna Mandivarapu +2 more
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Feedback Recurrent Autoencoder [PDF]
ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2020 In this work, we propose a new recurrent autoencoder architecture, termed Feedback Recurrent AutoEncoder (FRAE), for online compression of sequential data with temporal dependency. The recurrent structure of FRAE is designed to efficiently extract the redundancy along the time dimension and allows a compact discrete representation of the data to be ...
Yang Yang 0010 +3 more
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Reconstruction Residuals Based Long-term Voltage Stability Assessment Using Autoencoders
Journal of Modern Power Systems and Clean Energy, 2020 Real-time voltage stability assessment (VSA) has long been an extensively research topic. In recent years, rapidly mounting deep learning methods have pushed online VSA to a new height that large amounts of learning algorithms are applied for VSA from ...
Haosen Yang, Robert C. Qiu, Houjie Tong
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