Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
Paper summary #### Problem addressed: Bayesian approximation of neural netowrks #### Summary: This paper gives an alternative view of dropout as Bayesian approximation, which allow one to obtain uncertainty from the predictions. The result is surprisingly simple, both the predictive mean and variance can be obtained by calculating the mean and variance (with some minor adjustment) of multiple passes through the network with dropout. #### Novelty: A new interpretation of dropout as a Bayesian approximation. #### Drawbacks: Some computational overhead, since calculating the predictive mean and variance need multiple passes through the network. #### Datasets: MNIST, solar irradiance, Maunua Loa Co2, #### Resources: Paper: http://arxiv.org/pdf/1506.02142v1.pdf Blog post: http://mlg.eng.cam.ac.uk/yarin/blog_3d801aa532c1ce.html #### Presenter: Yingbo Zhou"
arxiv.org
scholar.google.com
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
Gal, Yarin and Ghahramani, Zoubin
arXiv e-Print archive - 2015 via Local Bibsonomy
Keywords: dblp


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