Uncertainty quantification (UQ) methods play a pivotal role in reducing the impact of
uncertainties during both optimization and decision making processes. They have been …

Recently deep learning (DL), as a new data‐driven technique compared to conventional
approaches, has attracted increasing attention in geophysical community, resulting in many …

Existing multi-view classification algorithms focus on promoting accuracy by exploiting
different views, typically integrating them into common representations for follow-up tasks …

The world provides us with data of multiple modalities. Intuitively, models fusing data from
different modalities outperform their uni-modal counterparts, since more information is …

Advancements in structural health monitoring (SHM) techniques have spiked in the past few
decades due to the rapid evolution of novel sensing and data transfer technologies. This …

In a real-world scenario, human actions are typically out of the distribution from training data,
which requires a model to both recognize the known actions and reject the unknown …

The inherent challenge of multimodal fusion is to precisely capture the cross-modal
correlation and flexibly conduct cross-modal interaction. To fully release the value of each …

Obtaining reliable and accurate quantification of uncertainty estimates from deep neural
networks is important in safety-critical applications. A well-calibrated model should be …

Integration of heterogeneous and high-dimensional data (eg, multiomics) is becoming
increasingly important. Existing multimodal classification algorithms mainly focus on …

A common strategy adopted by existing state-of-the-art unsupervised domain adaptation
(UDA) methods is to employ two classifiers to identify the misaligned local regions between …