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...
It is demonstrated that performance can be significantly improved by retaining all data and using a learning model that reflects the temporal structure of the environment.
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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