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?
Key points
How the author proved effectiveness of the proposal?
Summary
Link
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
Author/Institution
What is this
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?
Key points
How the author proved effectiveness of the proposal?
Any discussions?
What should I read next?