Bayesian Learning
A Bayesian network, Bayes network, belief network, Bayes(ian) model or probabilistic directed acyclic graphical model is a probabilistic graphical model (a type of statistical model) that represents a set of variables and their conditional dependencies via a directed acyclic graph (DAG). (Wikipedia)
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DifferentiableAnnealedImportanceSampling andthePerilsofGradientNoise
Neural Information Processing Systems
Marginal likelihood (ML), sometimes calledevidence, is a central quantity in Bayesian learning as it measures how well a model can describe a particular dataset.
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DifferentiableAnnealedImportanceSampling andthePerilsofGradientNoise
Neural Information Processing Systems
Marginal likelihood (ML), sometimes calledevidence, is a central quantity in Bayesian learning as it measures how well a model can describe a particular dataset.
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The Geometry of Neural Nets ' Parameter Spaces Under Reparametrization
Neural Information Processing Systems
But it can also be problematic since it can induce inconsistencies in, e.g., Hessian-based flatness measures,
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