Abstract Machine learning methods strive to acquire a robust model during the training
process that can effectively generalize to test samples, even in the presence of distribution …

Medical image segmentation is a fundamental and critical step in many image-guided
clinical approaches. Recent success of deep learning-based segmentation methods usually …

Artificial intelligence (AI) approaches nowadays have gained remarkable success in single-
modality-dominated remote sensing (RS) applications, especially with an emphasis on …

The emergence of large language models (LLMs) has marked a significant breakthrough in
natural language processing (NLP), fueling a paradigm shift in information acquisition …

The foundation models have recently shown excellent performance on a variety of
downstream tasks in computer vision. However, most existing vision foundation models …

Abstract Out-of-distribution (OOD) detection is vital to safety-critical machine learning
applications and has thus been extensively studied, with a plethora of methods developed in …

Abstract Out-of-distribution (OOD) detection is critical to ensuring the reliability and safety of
machine learning systems. For instance, in autonomous driving, we would like the driving …

The expanding scale and inherent complexity of biological data have encouraged a growing
use of machine learning in biology to build informative and predictive models of the …

Deep learning (DL) is one of the fastest-growing topics in materials data science, with
rapidly emerging applications spanning atomistic, image-based, spectral, and textual data …

Over the past few years, Deep Learning (DL) methods have garnered substantial
recognition within the field of hydrology and water resources applications. Beginning with a …