Brain-inspired chips can slash AI energy use by as much as 100-fold, but the road to mainstream deployment is far from ...

Because of these benefits, our study focus is to design an Ensemble Multilayer Perceptron Model (E-MLP) to perform threat classification in the cloud. Combining autoencoder and E-MLP, we develop a ...

A transfer-learned hierarchical variational autoencoder model for computational design of anticancer peptides.. If you have the appropriate software installed, you can download article citation data ...

AESTETIK: AutoEncoder for Spatial Transcriptomics Expression with Topology and Image Knowledge This model is part of the paper "Representation learning for multi-modal spatially resolved ...

Specifically, transmitters jointly modulate the generated symbols using the autoencoder model and send the encoded data to the receivers over the shared communication channel. The optimal ...

I am using an autoencoder-based unsupervised model for anomaly detection. Input is the 2D vector comprising 22 features at different time stamps (Input shape: for e.g (100, 22)). The autoencoder model ...

In this paper, we suggest a novel deep learning-based PI model with feature extraction using a deep autoencoder and k -means clustering through a cross-sectional study of sleep disturbance patient ...