To address this problem, we propose a novel approach based on “converting” autoencoder and lightweight DNNs. This improves upon recent work such as early-exiting framework and DNN partitioning.

During the inference cycle, we freeze the autoencoder layers and iterate the dimension ... The MPA iteration is still valid because the inference DNN of channel can be considered as a non-linear ...

Consider the limitations of using the linear model, nonlinear unmixing methods have been ... This paper proposes a nonsymmetric autoencoder network to overcome this issue. The proposed scheme benefits ...

In this paper, we propose a novel hybrid Autoencoder and Modified Particle Swarm Optimization (HAEMPSO) for feature selection and deep neural network (DNN) for classification ... While RF produces ...

This is an implementation of some Deep Neural Networks (DNN). We closely followed the ULFDL Tutorial, but using C++/CUDA instead of Matlab or Octave. Each neural network architecture is implemented in ...

Clone the repository Note: Repository may be quite large as results/ directory has results that contains the deep learning model and the images and graphs of the results Remarks: Knowledge of the ...

We employed time–frequency analysis (TFA) and a stacked autoencoder (SAE), which is a deep neural network (DNN)-based learning algorithm, to assess the mobility and fall risk of the elderly according ...