Results 41 to 50 of about 14,357 (268)
Entropy, 2021 Autoencoders are commonly used in representation learning. They consist of an encoder and a decoder, which provide a straightforward method to map n-dimensional data in input space to a lower m-dimensional representation space and back.
Viktoria Schuster, Anders Krogh
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The Optimally Designed Deep Autoencoder-Based Compressive Sensing Framework for 1D and 2D Signals
IEEE AccessThe capacity of Compressive Sensing (CS) to recreate original data from a limited number of samples has led to a surge in attention in recent years.
Irfan Ahmed +3 more
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, 2020 Learning the true density in high-dimensional feature spaces is a well-known problem in machine learning. In this work, we consider generative autoencoders based on maximum-mean discrepancy (MMD) and provide theoretical insights. In particular, (i) we prove that MMD coupled with Coulomb kernels has optimal convergence properties, which are similar to ...
Emanuele Sansone +2 more
openaire +2 more sources
Symmetric Wasserstein Autoencoders
CoRR, 2021 37th Conference on Uncertainty in Artificial Intelligence, UAI 2021, July 27-30, 2021, Virtual ...
Sun, Sun, Guo, Hongyu
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Fast and Effective Techniques for LWIR Radiative Transfer Modeling: A Dimension-Reduction Approach
Remote Sensing, 2019 The increasing spatial and spectral resolution of hyperspectral imagers yields detailed spectroscopy measurements from both space-based and airborne platforms.
Nicholas Westing +2 more
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Sensors, 2022 Trustworthy AI applications such as biometric authentication must be implemented in a secure manner so that a malefactor is not able to take advantage of the knowledge and use it to make decisions.
Alexey Sulavko
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Molecular Oncology, EarlyView.Single‐cell DNA methylation (scDNAme) profiling maps epimutational clonal evolution, revealing mechanisms of malignancy and therapeutic resistance across diverse cancer types. By providing a high‐resolution landscape of intratumoral heterogeneity, these technologies empower precise patient stratification, guide the development of enhanced ...
Ik Soo Kim
wiley +1 more source
Interpretability-Aware Industrial Anomaly Detection Using Autoencoders
IEEE Access, 2023 The past decade has witnessed wide applications of deep neural networks in anomaly detection. However, the dearth of interpretability in neural networks often hinders their reliability, especially for industrial applications where practical users heavily
Rui Jiang, Yijia Xue, Dongmian Zou
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The deep kernelized autoencoder [PDF]
Applied Soft Computing, 2018 Autoencoders learn data representations (codes) in such a way that the input is reproduced at the output of the network. However, it is not always clear what kind of properties of the input data need to be captured by the codes. Kernel machines have experienced great success by operating via inner-products in a theoretically well-defined reproducing ...
Michael Kampffmeyer +4 more
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dynoGP: Deep Gaussian Processes for Dynamic System Identification
International Journal of Adaptive Control and Signal Processing, EarlyView.This work introduces a novel class of deep models for system identification, dynamical deep Gaussian processes, which combine the strengths of data‐driven methods, such as those based on neural network architectures, with the ability to output a probability distribution for uncertainty representation.
Alessio Benavoli +2 more
wiley +1 more source