TL;DR
DENSE is an ensemble of multi layer perceptrons (MLPs) that is trained to minimize a modified Cram´er–von Mises distance in the joint space of inputs and outputs that enables non-parametric representations of arbitrary densities without (complex) likelihood design.
A scalable and understandable neural-symbolic framework that treats neural networks as functional preconditioners for symbolic discovery, creating a solid link between neural approximation and the discovery of sparse equations for scientific machine learning.
Stochastic neural network broadens deep learning from prediction‐oriented function approximation to a richer framework for statistical inference, and two related architectures are discussed: kernel‐expanded StoNet and sublinearly structured DNNs, which can be viewed as structural or theoretical extensions of the StoNet...
This survey provides a curated list of open-source software libraries in Python, Scala, and Julia that implement PNNs, along with a selection of online tutorials and video lectures aimed at making this model accessible to a broader audience.
A simple and broadly applicable residual guided procedure that greedily constructs the hidden layer using a closed form residual decrease criterion and yields a progressive training process with a guaranteed monotonic decrease of the training objective.
The developed neural network model demonstrates effective ability to estimate the of stochastic processes parameters that form the random noise in MEMS-accelerometers signal.
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