On top of machine learning (ML) models, uncertainty quantification (UQ) functions as an
essential layer of safety assurance that could lead to more principled decision making by …

While the choice of prior is one of the most critical parts of the Bayesian inference workflow,
recent Bayesian deep learning models have often fallen back on vague priors, such as …

Since out-of-distribution generalization is a generally ill-posed problem, various proxy
targets (eg, calibration, adversarial robustness, algorithmic corruptions, invariance across …

Deep ensembles have recently gained popularity in the deep learning community for their
conceptual simplicity and efficiency. However, maintaining functional diversity between …

Popular approaches for quantifying predictive uncertainty in deep neural networks often
involve distributions over weights or multiple models, for instance via Markov Chain …

Facial Expression Recognition (FER) has recently emerged as a crucial area in Human-
Computer Interaction (HCI) system for understanding the user's inner state and intention …

Several recent studies have reported negative results when using heteroskedastic neural
regression models to model real-world data. In particular, for overparameterized models, the …

Heteroscedastic classifiers, which learn a multivariate Gaussian distribution over prediction
logits, have been shown to perform well on image classification problems with hundreds to …

From an empirical perspective, the subset chosen through active learning cannot guarantee
an advantage over random sampling when transferred to another model. While it …

Deep learning has positioned itself as one of the most promising directions of machine
learning in recent years. Nonetheless, deep neural networks have many shortcomings, for …