Similarly, on the LaRED dataset the Jacobian regularization outperforms the weight decay with the difference most obvious at the smallest number of training samples. Note also that

What’s the problem to be solved?  Model Selection & Design  Content Strategy Technology Needs Operations Procedures Teacher Selection  & Training

Various deep learning architectures such as deep neural networks, convolutional deep neural networks, deep belief networks and recurrent neural networks have been

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Deep convolutional neural networks (CNNs) have recently been shown to outperform fully connected deep neural networks (DNNs) both on low-resource and on large-scale speech

Some popular deep learning architectures like Convolutional Neural Networks (CNN), Deep Neural Networks (DNN), Deep Belief Network (DBN) and Recurrent Neural Networks

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Keywords: deep learning, neural networks, recurrent neural networks, hierar- chical recurrent neural networks, multiscale recurrent neural networks, language modelling,

Deepsentibank: Visual sentiment concept classification with deep convolutional neural networks [J].. Robust image sentiment analysis using progressively trained and domain

Keywords: Deep Learning, Deep Belief Networks (DBNs), Convolution Neural Networks

The FER model demonstrated a behavior unseen in CIFAR-10, whereas switching the loss function causes a sudden drop in training accuracy and spike in each loss function’s loss value.

In this work, we proposed the Residual-CNDS and Residual Squeeze CNDS networks, which combine three robust techniques, convolutional neural networks with deep supervision (Wang

The tool can compute the extended view-factor and extended incident heat fluxes for solar, planetary and albedo contributions using the Monte Carlo Ray Tracing model. The software

Key words: Deep learning, automatic speech recognition, end-to-end training, convolutional neural networks, raw speech signal, robust speech recognition, conditional random

Motivated by the dynamical system view of neural networks, this work bridges adversarial robust- ness of deep neural models with Lyapunov stability of dynamical systems, and we

Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. Deep convolutional neural networks for hyperspectral image

Neural networks, deep learning, convolutional neural networks, image recognition, Cifar-10, RMSPROP, normalized

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