Supervised learning algorithms have shown limited use in the field of anomaly detection due
to the unpredictability and difficulty in acquiring abnormal samples. In recent years …

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 …

Counterfactual examples for an input---perturbations that change specific features but not
others---have been shown to be useful for evaluating bias of machine learning models, eg …

Learning from multimodal data is an important research topic in machine learning, which
has the potential to obtain better representations. In this work, we propose a novel approach …

A wide range of applications require learning image generation models whose latent space
effectively captures the high-level factors of variation present in the data distribution. The …

This paper looks into the modeling of hallucination in the human's brain. Hallucinations are
known to be causally associated with some malfunctions within the interaction of different …