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 …

Isotropic Gaussian priors are the de facto standard for modern Bayesian neural network
inference. However, it is unclear whether these priors accurately reflect our true beliefs …

The recent growth in the field of data mining and machine learning has remitted into more
recognition of outcome prediction and classification. However, the application of these …

Cyberattacks from within an organization's trusted entities are known as insider threats.
Anomaly detection using deep learning requires comprehensive data, but insider threat data …

We develop a predictive prognosis model to support medical experts in their clinical
decision-making process in Intensive Care Units (ICUs)(a) to enhance early mortality …

An Individual Survival Distribution (ISD) models a patient's personalized survival probability
at all future time points. Previously, ISD models have been shown to produce accurate and …

Bayesian neural networks (BNNs) have recently regained a significant amount of attention in
the deep learning community due to the development of scalable approximate Bayesian …

This thesis explores the applications of deep learning in clinical and epidemiologic data
analysis, focusing on neural networks for causal effect estimation and clinical risk prediction …

To design a device that is robust to process-induced random variation, this study proposes a
machine-learning-based predictive model that can simulate the electrical characteristics of …

Next-generation risk assessment (NGRA) involves the combination of in vitro and in silico
models for more human-relevant, ethical, and sustainable human chemical safety …