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Oak Ridge National Lab7mon
Convolutional Variational Autoencoder-based Unsupervised Learning for Power Systems Faults
This paper presents an unsupervised learning method to classify and label transients observed in the distribution grid. A Convolutional Variational Autoencoder (CVAE) was developed for this purpose.
IEEE2y
Football Event Classification Using Convolutional Autoencoder and Multilayer Extreme Learning Machine
The proposed model consists of a convolutional autoencoder (AE) coupled with a multilayer extreme learning machine to classify the given images into defined events. The convolutional AE module is ...
Frontiers2y
Trainable Communication Systems Based on the Binary Neural Network
The decoder structure is also composed of three convolution layers, in which the parameters are consistent with the encoder. A binary linear layer is also added before the final output. In this way, ...
Frontiers4y
Use of a Sparse-Response Deep Belief Network and Extreme Learning Machine to Discriminate Alzheimer's Disease, Mild Cognitive Impairment, and Normal Controls Based on Amyloid ...
theory and an extreme learning machine (ELM) model to distinguish AD, MCI, and normal controls (NC). We used [18 F]-AV45 positron emission computed tomography (PET) and magnetic resonance imaging (MRI ...