Advances in data preprocessing for biomedical data fusion: An overview of the methods, challenges, and prospects
Due to the proliferation of biomedical imaging modalities, such as Photoacoustic
Tomography, Computed Tomography (CT), Optical Microscopy and Tomography, etc …
Tomography, Computed Tomography (CT), Optical Microscopy and Tomography, etc …
Randomness in neural networks: an overview
S Scardapane, D Wang - Wiley Interdisciplinary Reviews: Data …, 2017 - Wiley Online Library
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 …
from data to build feature‐based classifiers and nonlinear predictive models. Training neural …
Pure transformers are powerful graph learners
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 …
promising results in graph learning both in theory and practice. Given a graph, we simply …
Black-box adversarial attacks with limited queries and information
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 …
the black-box setting, where the attacker only has query access to the model. In practice, the …
Prior convictions: Black-box adversarial attacks with bandits and priors
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 …
only loss-oracle access to a model is available. We introduce a framework that conceptually …
Stochastic configuration networks: Fundamentals and algorithms
This paper contributes to the development of randomized methods for neural networks. The
proposed learner model is generated incrementally by stochastic configuration (SC) …
proposed learner model is generated incrementally by stochastic configuration (SC) …
Insights into randomized algorithms for neural networks: Practical issues and common pitfalls
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 …
regarded as feed-forward neural networks built with a specific randomized algorithm, ie, the …
Blessing of dimensionality: mathematical foundations of the statistical physics of data
The concentrations of measure phenomena were discovered as the mathematical
background to statistical mechanics at the end of the nineteenth/beginning of the twentieth …
background to statistical mechanics at the end of the nineteenth/beginning of the twentieth …
Stochastic configuration machines for industrial artificial intelligence
D Wang, MJ Felicetti - arXiv preprint arXiv:2308.13570, 2023 - arxiv.org
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 …
intelligence (IAI), where neural networks play a key role. Neural networks in IAI require …
An investigation of complex fuzzy sets for large-scale learning
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 …
functions. Over the last 20 years, time-series forecasting has emerged as the most important …