Impact Statement: Autoencoder is a popular data-driven modeling technology in deep learning. It can deal with the nonlinear relationships among process variables, and has a powerful feature extraction ...

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

A prototype system that uses a CNN encoder and edge-based features to retrieve visually similar fashion items from the Fashion MNIST dataset using cosine similarity.

Geometric Dynamic Variational Autoencoders (GD-VAEs) for learning embedding maps for nonlinear dynamics into general latent spaces. This includes methods for standard latent spaces or manifold latent ...