Due to the proliferation of biomedical imaging modalities, such as Photoacoustic
Tomography, Computed Tomography (CT), Optical Microscopy and Tomography, etc …

Neural networks, as powerful tools for data mining and knowledge engineering, can learn
from data to build feature‐based classifiers and nonlinear predictive models. Training neural …

We show that standard Transformers without graph-specific modifications can lead to
promising results in graph learning both in theory and practice. Given a graph, we simply …

Current neural network-based classifiers are susceptible to adversarial examples even in
the black-box setting, where the attacker only has query access to the model. In practice, the …

We study the problem of generating adversarial examples in a black-box setting in which
only loss-oracle access to a model is available. We introduce a framework that conceptually …

This paper contributes to the development of randomized methods for neural networks. The
proposed learner model is generated incrementally by stochastic configuration (SC) …

Real-time predictive modelling with desired accuracy is highly expected in industrial artificial
intelligence (IAI), where neural networks play a key role. Neural networks in IAI require …

Abstract Random Vector Functional-link (RVFL) networks, a class of learner models, can be
regarded as feed-forward neural networks built with a specific randomized algorithm, ie, the …

Complex fuzzy sets are an extension of type-1 fuzzy sets with complex-valued membership
functions. Over the last 20 years, time-series forecasting has emerged as the most important …

The concentrations of measure phenomena were discovered as the mathematical
background to statistical mechanics at the end of the nineteenth/beginning of the twentieth …