Results 31 to 40 of about 14,221 (268)
Comparison of methods for correcting outliers in ECG-based biometric identification
Metrology and Measurement Systems, 2020 The aim of this paper is to compare the efficiency of various outlier correction methods for ECG signal processing in biometric applications. The main idea is to correct anomalies in various segments of ECG waveform rather than skipping a corrupted ECG ...
Su Jun +6 more
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CoRR, 2019 Proceedings of the 37th International Conference on Machine ...
Michael Moor +3 more
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An Introduction to Autoencoders
CoRR, 2022 In this article, we will look at autoencoders. This article covers the mathematics and the fundamental concepts of autoencoders. We will discuss what they are, what the limitations are, the typical use cases, and we will look at some examples. We will start with a general introduction to autoencoders, and we will discuss the role of the activation ...
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Self-Net: Lifelong Learning via Continual Self-Modeling
Frontiers in Artificial Intelligence, 2020 Learning a set of tasks over time, also known as continual learning (CL), is one of the most challenging problems in artificial intelligence. While recent approaches achieve some degree of CL in deep neural networks, they either (1) store a new network ...
Jaya Krishna Mandivarapu +2 more
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UMYU Scientifica JournalAutoencoders have developed into neural search networks in recent years, and the majority of machine learning (ML) methods rely on the input properties to produce high-quality models.
Samuel Michael Ogbe +1 more
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Reconstruction Residuals Based Long-term Voltage Stability Assessment Using Autoencoders
Journal of Modern Power Systems and Clean Energy, 2020 Real-time voltage stability assessment (VSA) has long been an extensively research topic. In recent years, rapidly mounting deep learning methods have pushed online VSA to a new height that large amounts of learning algorithms are applied for VSA from ...
Haosen Yang, Robert C. Qiu, Houjie Tong
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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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Digital Chemical EngineeringAutoencoder-based reduced-order machine learning models have been developed for modeling and predictive control of nonlinear chemical processes with high dimensionality such as discretization of reaction–diffusion processes.
Wallace Gian Yion Tan, Ming Xiao, Zhe Wu
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Multiresolution convolutional autoencoders
Journal of Computational Physics, 2023 20 pages, 11 ...
Yuying Liu 0010 +3 more
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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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