Results 31 to 40 of about 6,908 (119)
Autoencoding With a Classifier System [PDF]
IEEE Transactions on Evolutionary Computation, 2021 Autoencoders are data-specific compression algorithms learned automatically from examples. The predominant approach has been to construct single large global models that cover the domain. However, training and evaluating models of increasing size comes at the price of additional time and computational cost.
Richard John Preen +2 more
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CoRR, 2020 High dimensional data is often assumed to be concentrated on or near a low-dimensional manifold. Autoencoders (AE) is a popular technique to learn representations of such data by pushing it through a neural network with a low dimension bottleneck while minimizing a reconstruction error.
Matan Atzmon, Amos Gropp, Yaron Lipman
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BAE: Anomaly Detection Algorithm Based on Clustering and Autoencoder
Mathematics, 2023 In this paper, we propose an outlier-detection algorithm for detecting network traffic anomalies based on a clustering algorithm and an autoencoder model.
Dongqi Wang, Mingshuo Nie, Dongming Chen
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CoRR, 2017 In this paper, we describe the "PixelGAN autoencoder", a generative autoencoder in which the generative path is a convolutional autoregressive neural network on pixels (PixelCNN) that is conditioned on a latent code, and the recognition path uses a generative adversarial network (GAN) to impose a prior distribution on the latent code.
Alireza Makhzani, Brendan J. Frey
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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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Enhancing Radar Resolution and Target Detection Probability with a Denoising Autoencoder [PDF]
Journal of Electromagnetic Engineering and ScienceWe propose the use of a denoising autoencoder to improve radar resolution and target detection probability in noise-contaminated range-Doppler diagrams. Conventionally, target detection has been performed using constant false alarm rate (CFAR) algorithms,
Wonhyo Kim, Daegun Oh, Youngwook Kim
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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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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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