Floods are a devastating natural calamity that may seriously harm both infrastructure and
people. Accurate flood forecasts and control are essential to lessen these effects and …

While the choice of prior is one of the most critical parts of the Bayesian inference workflow,
recent Bayesian deep learning models have often fallen back on vague priors, such as …

We devise deep nearest centroids (DNC), a conceptually elegant yet surprisingly effective
network for large-scale visual recognition, by revisiting Nearest Centroids, one of the most …

This paper describes a data organization system and genuine content-addressable memory
called the WEBSOM. It is a two-layer self-organizing map (SOM) architecture where …

There have been several recent studies concerning feedforward networks and the problem
of approximating arbitrary functionals of a finite number of real variables. Some of these …

A unified, coherent treatment of current classifier ensemble methods, from fundamentals of
pattern recognition to ensemble feature selection, now in its second edition The art and …

This tutorial/survey paper:(1) provides a concise point of departure for researchers and
practitioners alike wishing to assess the current state of the art in the control and monitoring …

Classification is one of the most active research and application areas of neural networks.
The literature is vast and growing. This paper summarizes some of the most important …

The classification problem is considered in which an outputvariable y assumes discrete
values with respectiveprobabilities that depend upon the simultaneous values of a set of …

Fisher's linear discriminant analysis is a valuable tool for multigroup classification. With a
large number of predictors, one can find a reduced number of discriminant coordinate …