In this paper, we prove a conjecture published in 1989 and also partially address an open
problem announced at the Conference on Learning Theory (COLT) 2015. For an expected …

Mixup is a popular data augmentation technique based on taking convex combinations of
pairs of examples and their labels. This simple technique has been shown to substantially …

Pancreatic involvement in primary sclerosing cholangitis (PSC) is an extremely rare condition,
and its pathologic features are poorly documented. We report two cases of an unusual …

Using in situ DNA polymerase I‐mediated biotin‐dATP nick‐translation (PANT) and terminal
deoxynucleotidyl‐transferase‐mediated dUTP nick end‐labeling (TUNEL), we investigated …

In this paper, we theoretically prove that adding one special neuron per output unit eliminates
all suboptimal local minima of any deep neural network, for multi-class classification, …

We introduce Interpolation Consistency Training (ICT), a simple and computation efficient
algorithm for training Deep Neural Networks in the semi-supervised learning paradigm. ICT …

We employ adaptive activation functions for regression in deep and physics-informed neural
networks (PINNs) to approximate smooth and discontinuous functions as well as solutions …

The motility of kinesin motors is explained by a “hand-over-hand” model in which two heads
of kinesin alternately repeat single-headed and double-headed binding with a microtubule. …

A simple approach to prepare carbon quantum dots is presented in this communication by
laser rapid passivation of nano carbon particles in ordinary organic solvent. The as-prepared …

This chapter provides theoretical insights into why and how deep learning can generalize
well, despite its large capacity, complexity, possible algorithmic instability, non-robustness, …