Shield tunneling has been prevalent in tunnel construction since its introduction into the
field. To take advantage of the massive data generated during tunneling and to assist in …

Deep learning (DL) in orthopaedics has gained significant attention in recent years.
Previous studies have shown that DL can be applied to a wide variety of orthopaedic tasks …

We study the challenging task of neural network quantization without end-to-end retraining,
called Post-training Quantization (PTQ). PTQ usually requires a small subset of training data …

We prove new upper and lower bounds on the VC-dimension of deep neural networks with
the ReLU activation function. These bounds are tight for almost the entire range of …

Effective screening of COVID-19 cases has been becoming extremely important to mitigate
and stop the quick spread of the disease during the current period of COVID-19 pandemic …

Abstract Background and Objective In the current COVID-19 outbreak, efficient testing of
COVID-19 individuals has proven vital to limiting and arresting the disease's accelerated …

When and why can a neural network be successfully trained? This article provides an
overview of optimization algorithms and theory for training neural networks. First, we discuss …

The rising number of cases of human Mpox has emerged as a major global concern due to
the daily increase of cases in several countries. The disease presents various skin …

Abstract Out-of-distribution (OOD) detection is a rapidly growing field due to new robustness
and security requirements driven by an increased number of AI-based systems. Existing …

Optimization is a critical component in deep learning. We think optimization for neural
networks is an interesting topic for theoretical research due to various reasons. First, its …