A transfer-learned hierarchical variational autoencoder model for computational design of anticancer peptides. Hossein Abbasi, Mahdi Malekpour, Shahin Yaghoobi, Sina Abdous, Mohammad Hossein Rohban, ...

Specifically, a variational autoencoder firstly trains a generative distribution and extracts reconstruction based features. Then we adopt a deep brief network to estimate the component mixture ...

In recent years, data-driven soft sensors, especially deep learning soft sensors show great potential for application in the process industry. As a typical deep network, stacked autoencoder (SAE) has ...

This paper introduces GeneA-SLAM2, an RGB-D SLAM system for dynamic environments. It eliminates dynamic object interference via depth statistical information and enhances keypoint distribution ...

The variational autoencoder (VAE) was employed to reduce feature redundancy and to accomplish noise reduction. Some studies have been conducted using autoencoders ... Figure 3 shows the schematic ...

More recently, Huang et al. and Igashov et al. have tackled this challenge using equivariant variational autoencoder and diffusion-based models, respectively. Each of these models was explicitly ...

Pytorch implementation for image compression and reconstruction via autoencoder. This is an autoencoder with cylic loss and coding parsing loss for image compression and reconstruction. Network ...