The recent wave of AI-generated content (AIGC) has witnessed substantial success in
computer vision, with the diffusion model playing a crucial role in this achievement. Due to …

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 …

Most of the existing research on unsupervised cross-domain intelligent fault diagnosis is
based on single-source domain adaptation, which fails to simultaneously utilize various …

An increasing number of decisions regarding the daily lives of human beings are being
controlled by artificial intelligence and machine learning (ML) algorithms in spheres ranging …

Generative adversarial network (GAN) studies have grown exponentially in the past few
years. Their impact has been seen mainly in the computer vision field with realistic image …

We are performing the digital transition of industry, living the 4th industrial revolution,
building a new World in which the digital, physical and human dimensions are interrelated in …

Large single-cell atlases are now routinely generated to serve as references for analysis of
smaller-scale studies. Yet learning from reference data is complicated by batch effects …

Supervised learning aims to train a classifier under the assumption that training and test
data are from the same distribution. To ease the above assumption, researchers have …

The conjoining of dynamical systems and deep learning has become a topic of great
interest. In particular, neural differential equations (NDEs) demonstrate that neural networks …

Generalization to out-of-distribution (OOD) data is a capability natural to humans yet
challenging for machines to reproduce. This is because most learning algorithms strongly …