Results 11 to 20 of about 56,672 (314)
CoRR, 2022 Recent progress in Generative Artificial Intelligence (AI) relies on efficient data representations, often featuring encoder-decoder architectures. We formalize the mathematical problem of finding the optimal encoder-decoder pair and characterize its solution, which we name the "benign autoencoder" (BAE). We prove that BAE projects data onto a manifold
Semyon Malamud +4 more
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On the Regularization of Autoencoders
CoRR, 2021 While much work has been devoted to understanding the implicit (and explicit) regularization of deep nonlinear networks in the supervised setting, this paper focuses on unsupervised learning, i.e., autoencoders are trained with the objective of reproducing the output from the input. We extend recent results [Jin et al.
Harald Steck, Dario García-García
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Autoencoding Variational Autoencoder
CoRR, 2020 Neurips ...
A. Taylan Cemgil +4 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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Energy Reports, 2021 Anomaly detection for hydropower turbine unit is a requirement for the safety of hydropower system. An unsupervised anomaly detection method employing variational modal decomposition (VMD) and deep autoencoder is proposed.
Hongteng Wang +3 more
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Autoencoder-based characterization of QCD multijet background at the LHC [PDF]
, 2023 openA proof of principle for the application of autoencoders in encoding high-dimensional multijet data is presented. A simulation with events containing four b-quark QCD jets is used to train the autoencoder. The reconstruction of events after a reduced
MARIÑO VILLADAMIGO, JAVIER
core
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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Unsupervised Outlier Detection via Transformation Invariant Autoencoder
IEEE Access, 2021 Autoencoder based methods are the majority of deep unsupervised outlier detection methods. However, these methods perform not well on complex image datasets and suffer from the noise introduced by outliers, especially when the outlier ratio is high.
Zhen Cheng +4 more
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The Variational InfoMax AutoEncoder [PDF]
, 2020 The Variational AutoEncoder (VAE) learns simultaneously an inference and a generative model, but only one of these models can be learned at optimum, this behaviour is associated to the ELBO learning objective, that is optimised by a non-informative ...
Bruce Graham +3 more
core +1 more source