To address these challenges, we propose a Noise-Consistent hypeRgraph AutoEncoder framework with denoising strategies, termed NCRAE, aimed at achieving robust node embeddings in ceRNA regulatory ...

Hi, Thanks for your great work with MAISI. The mask_generation_autoencoder has 8 input channels. What is the kind of data that is required as input? Would you have an example on how to use the mask ...

Due to the complexity of samples and the limitations in spatial resolution, the spectra in hyperspectral imaging (HSI) are generally contributed to by multiple components, making univariate analysis ...

This study aims to explore an autoencoder-based method for generating brain MRI images of patients with Autism Spectrum Disorder (ASD) and non-ASD individuals, and to discriminate ASD based on the ...

Autoencoder for Product Matching This was an experiment for a possible PhD topic. The main idea was to use different Autoencoder for entity resolution / product matching. The core idea was to pretrain ...

This paper presents a dual autoencoder network model based on the retinex theory to perform the low-light enhancement and noise reduction by combining the stacked and convolutional autoencoders. The ...

Learn how autoencoders and GANs can help you with anomaly detection and data compression, and what are their differences and trade-offs.

Modeling complex correlations on multiview data is still challenging, especially for high-dimensional features with possible noise. To address this issue, we propose a novel unsupervised multiview ...