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Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning #39

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nagataka opened this issue Oct 27, 2021 · 0 comments
Open

Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning #39

nagataka opened this issue Oct 27, 2021 · 0 comments
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Dissertation Uncertainty

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@nagataka
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Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning

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Show that the use of dropout (and its variants) in NNs can be interpreted as a Bayesian approximation of a well known probabilistic model: the Gaussian process (GP)

Comparison with previous researches. What are the novelties/good points?

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How the author proved effectiveness of the proposal?

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