Results 11 to 20 of about 14,357 (268)
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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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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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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Journal of Cybersecurity and Privacy, 2023 An intrusion detection system (IDS), whether as a device or software-based agent, plays a significant role in networks and systems security by continuously monitoring traffic behaviour to detect malicious activities.
Fatemeh Ahmadi Abkenari +2 more
doaj +1 more source
Deep Learning for Optical Sensor Applications: A Review
Sensors, 2023 Over the past decade, deep learning (DL) has been applied in a large number of optical sensors applications. DL algorithms can improve the accuracy and reduce the noise level in optical sensors.
Nagi H. Al-Ashwal +3 more
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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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Detecting Anomalies of Satellite Power Subsystem via Stage-Training Denoising Autoencoders
Sensors, 2019 Satellite telemetry data contains satellite status information, and ground-monitoring personnel need to promptly detect satellite anomalies from these data.
Weihua Jin +4 more
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