Traditional industrial soft sensors often treat industrial process data as uniformly distributed or unimodal. However, in reality, due to variations in operating conditions, industrial process data ...

The promise of multimodal AI is real, but like any frontier, it demands responsible exploration.

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 ...

DeepAnT [36], a convolutional autoencoder that forecasts future time points and flags deviations as anomalies, has been effectively used for IoT monitoring across multiple deployments. Kara et al. [46 ...

Advancing Multimodal AI for Integrated Understanding and Generation explores the rapidly evolving field of multimodal artificial intelligence (AI), which aims to synthesize information from ...

To discover features and their conditional independence structure, we develop pimaDAG, a variational autoencoder framework that learns features from multimodal datasets, possibly with known physics ...