TL;DR
The developed neural network model demonstrates effective ability to estimate the of stochastic processes parameters that form the random noise in MEMS-accelerometers signal.
Analytical and particle-based methods for uncertainty propagation in random neural network models, where both the inputs and network parameters are allowed to be random, are developed and extended to autonomous dynamical systems whose one-step evolution map is represented by a random neural network.
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.
It is demonstrated that the posterior density can be written as a mixture density with suitably defined auxiliary random variables, where the mixture components are log-concave, and neuron parameters can be sampled from the posterior by only sampling log-concave densities.
The proliferation of capable and efficient machine learning (ML) models marks one of the strongest methodological shifts in signal processing (SP) in its nearly 100-year history. ML models support the development of SP systems that represent complex, nonlinear relationships with high predictive accuracy. Adapting these...
Estimation of a regression function from independent and identically distributed data is considered. The $L_2$ error with integration with respect to the design variable is used as the error criterion. An initially randomly pruned fully connected deep neural network with logistic squasher as activation function is fitt...
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