Abstract Machine learning models have been shown to be useful for predicting and
assessing structural performance, identifying structural condition and informing preemptive …

We propose an energy-based model (EBM) in the latent space of a generator model, so that
the EBM serves as a prior model that stands on the top-down network of the generator …

Humans learn concepts both from labeled supervision and by unsupervised observation of
patterns, a process machines are being taught to mimic by training on large annotated …

For decades, co-relating different data domains to attain the maximum potential of machines
has driven research, especially in neural networks. Similarly, text and visual data (images …

This paper studies the fundamental problem of learning energy-based model (EBM) in the
latent space of the generator model. Learning such prior model typically requires running …

Machine learning (ML) is about computational methods that enable machines to learn
concepts from experience. In handling a wide variety of experience ranging from data …

A methodology is presented for performing dynamic seismic damage assessment of
distributed infrastructure systems using graph neural networks and semi-supervised …

We examine concept generalization at a large scale in the natural visual spectrum.
Established computational modes (ie, rule-based or similarity-based) are primarily studied …

Learning representations of data is an important problem in statistics and machine learning.
While the origin of learning representations can be traced back to factor analysis and …

As the first book of a three-part series, this book is offered as a tribute to pioneers in vision,
such as Béla Julesz, David Marr, King-Sun Fu, Ulf Grenander, and David Mumford. The …