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Found 2 papers, 1 papers with code
Posterior Inference on Shallow Infinitely Wide Bayesian Neural Networks under Weights with Unbounded Variance
From the classical and influential works of Neal (1996), it is known that the infinite width scaling limit of a Bayesian neural network with one hidden layer is a Gaussian process, \emph{when the network weights have bounded prior variance}.
Causal Scoring: A Framework for Effect Estimation, Effect Ordering, and Effect Classification
We present three valuable causal interpretations of these scores: effect estimation (EE), effect ordering (EO), and effect classification (EC).
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