In this paper, we introduce Conditional Gumbel-Softmax as a method to perform end-to-end
learning of the optimal feature subset for a given task and deep neural network (DNN) …

We propose a dynamic sensor selection approach for deep neural networks (DNNs), which
is able to derive an optimal sensor subset selection for each specific input sample instead of …

Objective. To develop an efficient, embedded electroencephalogram (EEG) channel
selection approach for deep neural networks, allowing us to match the channel selection to …

Objective. To develop an efficient, embedded electroencephalogram (EEG) channel
selection approach for deep neural networks, allowing us to match the channel selection to …

In this paper, we present a novel embedded feature selection method based on a Multi-layer
Perceptron (MLP) network and generalize it for group-feature or sensor selection problems …

Hessian-free training has become a popular parallel second order optimization technique
for Deep Neural Network training. This study aims at speeding up Hessian-free training, both …

Feature selection is an important data preprocessing strategy, has been proven empirically
that it contributes to reducing the dimensionality of feature and enhancing the performance …

Efficient model selection for identifying a suitable pre-trained neural network to a
downstream task is a fundamental yet challenging task in deep learning. Current practice …

With the rapid increase of high-dimensional data mixed with labelled and unlabelled
samples, the semi-supervised feature selection technique has received much attention in …

The history of deep learning has shown that human-designed problem-specific networks
can greatly improve the classification performance of general neural models. In most …