Results 51 to 60 of about 6,908 (119)
Deep Learning-Based Joint Optimization of Modulation and Power for Nonlinearity-Constrained System
IEEE Access, 2022 For wireless communication systems with a long distance or severe interference, the insufficient transmit power limits the system performance. In this case, the maximum transmit power depends on the nonlinearity and the saturation region of the power ...
Zhiyuan Liu, Meng Ma
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Detection of cyber-attacks on the power smart grids using semi-supervised deep learning models
Discrete and Continuous Models and Applied Computational Science, 2022 Modern smart energy grids combine advanced information and communication technologies into traditional energy systems for a more efficient and sustainable supply of electricity, which creates vulnerabilities in their security systems that can be used by ...
Eugeny Yu. Shchetinin +1 more
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An autoencoder-based deep learning method for genotype imputation
Frontiers in Artificial Intelligence, 2022 Genotype imputation has a wide range of applications in genome-wide association study (GWAS), including increasing the statistical power of association tests, discovering trait-associated loci in meta-analyses, and prioritizing causal variants with fine ...
Meng Song +15 more
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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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Proceedings of the AAAI Conference on Artificial Intelligence, 2018 Auto-encoding is an important task which is typically realized by deep neural networks (DNNs) such as convolutional neural networks (CNN). In this paper, we propose EncoderForest (abbrv. eForest), the first tree ensemble based auto-encoder.
Ji Feng, Zhi-Hua Zhou
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Efficient Local Image Descriptors Learned With Autoencoders
IEEE Access, 2022 Local image descriptors play a crucial role in many image processing tasks, such as object tracking, object recognition, panorama stitching, and image retrieval.
Nina Zizakic, Aleksandra Pizurica
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LSTM-Autoencoder Deep Learning Model for Anomaly Detection in Electric Motor
EnergiesAnomaly detection is the process of detecting unusual or unforeseen patterns or events in data. Many factors, such as malfunctioning hardware, malevolent activities, or modifications to the data’s underlying distribution, might cause anomalies.
Fadhila Lachekhab +4 more
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Frontiers in Artificial Intelligence, 2023 This paper presents the use of deep conditional autoencoder to predict the effect of treatments for patients suffering from hemophiliac disorders. Conditional autoencoder is a semi-supervised model that learns an abstract representation of the data and ...
Cédric Buche +3 more
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Remote SensingHigh-resolution satellite images are frequently used to measure horizontal displacements caused by earthquakes, providing valuable insights into rupture behaviors and mechanical properties of seismogenic faults.
Chenglong Li +4 more
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Frontiers in Applied Mathematics and Statistics, 2021 Data-driven methods have been revolutionizing the way physicists and engineers handle complex and challenging problems even when the physics is not fully understood. However, these models very often lack interpretability.
Emilio Jose Rocha Coutinho +3 more
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