Bayesian Inference
Bayes' Theorem allows a program to infer the probabilities of likely causes from the probabilities of their effects, when what it is given are the probabilities of effects, given the causes.
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2 PreliminariesonBayesianAutoencoders Anautoencoder(AE)isaneuralnetworkparameterizedbyasetofparameters w,whichtransforms anunlabelled dataset,x
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The bottleneck introduced by the low-dimensional latent space is what characterizes the compression and representation learning capabilities ofautoencoders.
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DifferentiableAnnealedImportanceSampling andthePerilsofGradientNoise
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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
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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
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But it can also be problematic since it can induce inconsistencies in, e.g., Hessian-based flatness measures,
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