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Frontiers29d
Integrating multi-source monitoring data and deep convolutional autoencoder technology for slope failure pattern recognition
Model architecture diagram of the deep convolutional autoencoder. The input to the model is a 9 ... Given the relatively small input dimensions, this study employs a convolution kernel size of 3 to ...
GitHub16d
HDVAE : identifying spatial domains in spatial transcriptomics data with Hierarchical Decoupled Variational Autoencoder
In this study, we proposed HDVAE (Hierarchical Decoupled Variational Autoencoder) which significantly improved the identification ability of the spatial domain in ST data through multi-hop graph ...
Frontiers25d
Deep learning-based automated segmentation for the quantitative diagnosis of cerebral small vessel disease via multisequence MRI
The decoder then recovers the input data from the low-dimensional vectors via transposed convolution. Autoencoder is able to extract local features of images and apply these features to further tasks ...
IEEE18d
An Elliptic Kernel Unsupervised Autoencoder -Graph Convolutional Network Ensemble Model for Hyperspectral Unmixing
This article introduces the Autoencoder Graph Ensemble Model (AEGEM), a novel ensemble-based framework designed to enhance performance in both endmember extraction and abundance estimation. In the ...
IEEE22d
Novel Stacked Maximal Information Coefficient-weighted Autoencoder with Attribute Expansion and Its Application to Industrial Soft Sensors
As a typical deep network, stacked autoencoder (SAE) has an outstanding modeling capability in soft sensors due to its ability to extract deep features. However, SAE ignores the expanded ...
GitHub21d
Prometheus Time Series Anomaly Detection with LSTM Autoencoder
It uses an LSTM (Long Short-Term Memory) autoencoder model built with TensorFlow/Keras to learn normal patterns from your metrics and identify deviations. The system includes scripts for data ...