A comprehensive artificial intelligence system needs to not only perceive the environment
with different “senses”(eg, seeing and hearing) but also infer the world's conditional (or even …

Real-world data often exhibit imbalanced distributions, where certain target values have
significantly fewer observations. Existing techniques for dealing with imbalanced data focus …

Deep networks are well-known to be fragile to adversarial attacks. We conduct an empirical
analysis of deep representations under the state-of-the-art attack method called PGD, and …

Generalization under distributional shift is an open challenge for machine learning. Invariant
Risk Minimization (IRM) is a promising framework to tackle this issue by extracting invariant …

Although existing face anti-spoofing (FAS) methods achieve high accuracy in intra-domain
experiments, their effects drop severely in cross-domain scenarios because of poor …

In recent years, replacing internal combustion engine vehicles with electric vehicles has
been a significant option for supporting reducing carbon emissions because of fossil fuel …

Abstract Domain adaptation aims to mitigate distribution shifts among different domains.
However, traditional formulations are mostly limited to categorical domains, greatly …

Existing regression models tend to fall short in both accuracy and uncertainty estimation
when the label distribution is imbalanced. In this paper, we propose a probabilistic deep …

The healthcare industry is the premier domain that has been significantly influenced by
incorporation of Internet of Things (IoT) technology resulting in smart healthcare application …

Interpretable time series prediction is crucial for safety-critical areas such as healthcare and
autonomous driving. Most existing methods focus on interpreting predictions by assigning …