… the dropout from Bayesian standpoint. Bayesian interpretation enables us to optimize the
dropout … In the following section, we will show a clear interpretation of dropout from Bayesian …

We show that a neural network with arbitrary depth and non-linearities, with dropout applied …
known Bayesian model. This interpretation might offer an explanation to some of dropout's …

… as Bayesian models – without changing either the models or the optimisation. We show that
the use of dropout (and its variants) in NNs can be interpreted as a Bayesian approximation …

… advanced dropout against nine dropout … dropout outperforms all the referred techniques
on all the datasets. We further compare the effectiveness ratios and find that advanced dropout …

… For example, inclusive KL encourages the coverage of the support set (referred as mass-…
For the dropout trained networks we perform MC dropout at test time with Ktest = 10 MC …

… about the role Dropout as regularization applies well to a large class of Bayesian models, …
While the first term encourages new model βt to fluctuate around the previously learned βt−…

… If the dropout mechanism depends on the observed responses … or after the time of dropout,
we have a random dropout (RD). … to encourage patients to stay longer in the MMT program. …

… inspired by the Bayesian interpretation of Dropout regularization. Concretely, we focus … in
Dropout posterior and then propose an improved method called Variational Structured Dropout …

… 2015), the general case of dropout in deep neural networks is analytically intractable, which
… a novel Bayesian interpretation of regularization via multiplicative noise—with dropout being …

… the aforementioned dropout techniques is that they all interpret the dropout via … a dropout
variant named soft dropout which can be considered as a generalization of the discrete dropout …