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Deep variational auto-encoders: A promising tool for dimensionality reduction and ball bearing elements fault diagnosis
(SAGE Publications Ltd, 2019)
© The Author(s) 2018.One of the main challenges that the industry faces when dealing with massive data for failure diagnosis is high dimensionality of such data. This can be tackled by dimensionality reduction method such ...
Shedding light on variational autoencoders
(2018-10-01)
Deep neural networks provide the canvas to create models of millions of parameters to fit distributions involving an equally large number of random variables. The contribution of this study is twofold. First, we introduce ...
Diagnóstico da doença de Alzheimer usando autoencoders aplicados a imagens de ressonância magnética
(Universidade Federal de São CarlosUFSCarCâmpus São CarlosCiência da Computação - CC, 2022-04-14)
Alzheimer's disease (AD) is a neurodegenerative disease that causes damage associated with memory and thinking, causing a gradual decline in judgment, reasoning and learning. One of the ways to aid in the diagnosis of AD ...
Improving automatic speech recognition containing additive noise using deep denoising autoencoders of lstm networks
(2016)
Automatic speech recognition systems (ASR) suffer from performance degradation under noisy conditions. Recent work, using deep neural networks to denoise spectral input features for robust ASR, have proved to be successful. ...