In this paper, we apply the self-attention from the state-of-the-art Transformer in Attention Is
All You Need for the first time to a data-driven operator learning problem related to partial …

Multi-view classification optimally integrates various features from different views to improve
classification tasks. Though most of the existing works demonstrate promising performance …

Federated learning is vulnerable to various attacks, such as model poisoning and backdoor
attacks, even if some existing defense strategies are used. To address this challenge, we …

Despite their ubiquity in core AI fields like natural language processing, the mechanics of
deep attention-based neural networks like the Transformer model are not fully understood …

Multi-view learning is a widely studied topic in machine learning, which considers learning
with multiple views of samples to improve the prediction performance. Even though some …

Magnetic resonance imaging (MRI), ultrasound (US), and contrast-enhanced ultrasound
(CEUS) can provide different image data about uterus, which have been used in the …

Abstract We propose $\beta $-graph embedding for robustly learning feature vectors from
data vectors and noisy link weights. A newly introduced empirical moment $\beta $-score …

We propose shifted inner-product similarity (SIPS), which is a novel yet very simple
extension of the ordinary inner-product similarity (IPS) for neural-network based graph …

We propose $\textit {weighted inner product similarity} $(WIPS) for neural network-based
graph embedding. In addition to the parameters of neural networks, we optimize the weights …

A collection of U (∈ N) data vectors is called a U-tuple, and the association strength among
the vectors of a tuple is termed as the hyperlink weight, that is assumed to be symmetric with …