In the wide-width limit under Gaussian likelihood, the posterior of the network output is identified when the random covariance matrix is positive definite, with mild conditions ensuring invertibility and order-independent sequential limits.
Title resolution pending
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
stat.ML 1years
2025 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Posterior Bayesian Neural Networks with Dependent Weights
In the wide-width limit under Gaussian likelihood, the posterior of the network output is identified when the random covariance matrix is positive definite, with mild conditions ensuring invertibility and order-independent sequential limits.