Dependence between Bayesian neural network units
Résumé
The connection between Bayesian neural networks and Gaussian processes gained a lot of attention in the last few years, with the flagship result that hidden units converge to a Gaussian process limit when the layers width tends to infinity. Underpinning this result is the fact that hidden units become independent in the infinite-width limit. Our aim is to shed some light on hidden units dependence properties in practical finite-width Bayesian neural networks. In addition to theoretical results, we assess empirically the depth and width impacts on hidden units dependence properties.
Domaines
Machine Learning [stat.ML]
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BDL_Dependence_between_units-3.pdf (3.78 Mo)
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main.pdf (3.78 Mo)
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