Recent years have seen a substantial increase in the application of machine learning (ML)
for automated analysis of nondestructive examination (NDE) data. One of the applications of …

Self-supervised learning on graph-structured data has drawn recent interest for learning
generalizable, transferable and robust representations from unlabeled graphs. Among …

In this paper, we address the few-shot classification task from a new perspective of optimal
matching between image regions. We adopt the Earth Mover's Distance (EMD) as a metric to …

Recent interest in complex and computationally expensive machine learning models with
many hyperparameters, such as automated machine learning (AutoML) frameworks and …

This open access book presents the first comprehensive overview of general methods in
Automated Machine Learning (AutoML), collects descriptions of existing systems based on …

This paper addresses the scalability challenge of architecture search by formulating the task
in a differentiable manner. Unlike conventional approaches of applying evolution or …

We propose an algorithm for inexpensive gradient-based hyperparameter optimization that
combines the implicit function theorem (IFT) with efficient inverse Hessian approximations …

Bi-Level Optimization (BLO) is originated from the area of economic game theory and then
introduced into the optimization community. BLO is able to handle problems with a …