Using brain gene expression maps from the Allen Human Brain Atlas, the researchers tested the degree to which Alzheimer’s risk genes explain the patterns of both actual and residual tau. This allowed ...

Hajjouz, S. and Avksentieva, N. (2022) Autoencoder-Based Anomaly Detection for IoT DDoS Attack Identification. Journal of Network Security, 24, 512-525.

We propose Denoising Masked Autoencoder (Deno-MAE), a novel multimodal autoencoder framework for denoising modulation signals during pretraining. DenoMAE extends the concept of masked autoencoders by ...

Utilizing an autoencoder network, our model indirectly trains the photonic energy band and transmission spectrum, by converting them into feature coding. It integrates a transmission spectrum ...

Compared to using PCA for dimensionality reduction, using a neural autoencoder has the big advantage that it works with source data that contains both numeric and categorical data, while PCA works ...

A series fault arc detection method based on denoising autoencoder and deep residual network Jianyuan Wang Xue Li * Yuhui Zhang † Key Laboratory of Modern Power System Simulation and Control and ...

The study introduces an innovative approach using an autoencoder network and embedding predictor to simplify apple images into 64 dimensions and predict fruit shapes from molecular data (SNPs).

Three-Dimensional Convolutional Autoencoder Extracts Features of Structural Brain Images With a “Diagnostic Label-Free” Approach: Application to Schizophrenia Datasets ...

An autoencoder is a neural network that predicts its own input. The diagram in Figure 3 shows the architecture of the 65-32-8-32-65 autoencoder used in the demo program.

In this article, we will define a Convolutional Autoencoder in PyTorch and train it on the CIFAR-10 dataset in the CUDA environment to create reconstructed images.