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IEEE4y
LSTM-DNN Based Autoencoder Network for Nonlinear Hyperspectral Image Unmixing
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 ...
Huawei2y
Joint Message Passing and Autoencoder for Deep Learning
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 ...
Frontiers2y
A novel hybrid autoencoder and modified particle swarm optimization feature selection for intrusion detection in the internet of things network
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 ...
Frontiers4y
Using a Stacked Autoencoder for Mobility and Fall Risk Assessment via Time–Frequency Representations of the Timed Up and Go Test
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 ...
GitHub3y
CUDA Deep Neural Networks
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 ...
GitHub1y
Baidicoot/easy-sae-training
Easy Sparse Linear Autoencoder (https://arxiv.org/abs/2309.08600) training, with data generated from TransformerLens models. This code represents a simplification of ...