Results 21 to 30 of about 56,672 (314)
Missing-Insensitive Short-Term Load Forecasting Leveraging Autoencoder and LSTM
IEEE Access, 2020 In most deep learning-based load forecasting, an intact dataset is required. Since many real-world datasets contain missing values for various reasons, missing imputation using deep learning is actively studied.
Kyungnam Park +3 more
doaj +1 more source
, 2023 (a) Diagram of the autoencoder components (b) Original spike and the reconstruction made by an autoencoder.
Ana-Maria Ichim (14648702) +4 more
core +1 more source
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
openaire +3 more sources
Schematics for autoencoder and variational autoencoder.
, 2023 Both models are based on the encoder-decoder neural network structure to a learn latent space. A) An autoencoder is a deterministic model where z is a mapping of the input data. B) A variational autoencoder is a probabilistic model where the mapping z is
Mostafa Eltager (17102176) +5 more
core +1 more source
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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Penalized Variational Autoencoder for Molecular Design [PDF]
, 2019 Variational autoencoders have emerged as one of the most common approaches for automating molecular generation. We seek to learn a cross-domain latent space capturing chemical and biological information, simultaneously.
Linus, Goerlitz +3 more
core +2 more sources
Topological obstructions to autoencoding [PDF]
Journal of High Energy Physics, 2021 Abstract Autoencoders have been proposed as a powerful tool for model-independent anomaly detection in high-energy physics. The operating principle is that events which do not belong to the space of training data will be reconstructed poorly, thus flagging them as anomalies.
Joshua Batson +3 more
openaire +6 more sources
, 2020 This is the first release of Hadamard Autoencoder based Social network prediction ...
gunjanmahindre
core +1 more source
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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Autoencoder-SAD: An Autoencoder-based Model for Security Attacks Detection
, 2023 In recent years, a variety of cybersecurity attacks affected national infrastructures, big companies, and even medium size organizations. As countermeasures are implemented, new attack variants appear.
Canavese, Daniele +2 more
core +1 more source