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cs.LG2024★ 1 cited
Finite Neural Networks as Mixtures of Gaussian Processes: From Provable Error Bounds to Prior Selection
Steven Adams, Andrea Patanè, Morteza Lahijanian +1
Infinitely wide or deep neural networks (NNs) with independent and identically distributed (i.i.d.) parameters have been shown to be equivalent to Gaussian processes. Because of th…
eess.SY2024★ 1 cited
Uncertainty Propagation in Stochastic Systems via Mixture Models with Error Quantification
Eduardo Figueiredo, Andrea Patane, Morteza Lahijanian +1
Uncertainty propagation in non-linear dynamical systems has become a key problem in various fields including control theory and machine learning. In this work we focus on discrete-…