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GitHub2y
zshu115x/Deep-Learning-Final-Project: Deep Learning Final Project: Built and trained Stacked CNN AutoEncoder and Deep CNN AutoEncoder based on STL-10 dataset - GitHub
Deep Learning Final Project: Built and trained Stacked CNN AutoEncoder and Deep CNN AutoEncoder based on STL-10 dataset - GitHub ... the process of convolution in keras using Theano. See details in ...
IEEE8mon
An Integrated Stacked Sparse Autoencoder and CNN-BLSTM Model for Ultra-Short-Term Wind Power Forecasting with Advanced Feature Learning - IEEE Xplore
The framework employs a stacked sparse multi-layer CNN autoencoder to distil inputs into a robust feature set capturing complex temporal dependencies. These features are then processed by a CNN-BLSTM ...
GitHub4mon
GitHub - liel2544/Activity-Recognition-Deep-Learning: Deep learning project for human activity recognition using time-series data from smartwatches and VICON motion capture ...
Deep learning project for human activity recognition using time-series data from smartwatches and VICON motion capture systems. Includes 1D-CNN, LSTM, and autoencoder pretraining, with improved models ...
IEEE20d
A CNN-Based End-to-End Learning Framework Toward Intelligent Communication Systems - IEEE Xplore
Deep learning has been applied in physical-layer communications systems in recent years and has demonstrated fascinating results that were comparable or even better than human expert systems. In this ...
Frontiers7y
Deep Learning Methods to Process fMRI Data and Their Application in the Diagnosis of Cognitive Impairment: A Brief Overview and Our Opinion - Frontiers
Deep Learning Methods in fMRI Data Analysis CNN. In recent years, CNN has drawn much attention and has been widely used in fMRI data processing, because valid features can be automatically extracted ...