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About 3 Bayesian Deep Learning
This chapter discusses Bayesian deep learning and variational inference techniques for approximating posteriors in Bayesian neural networks. It reviews recent advances in variational inference, including using data subsampling to approximate the objective function over minibatches instead of the full dataset. It then analyzes three Monte Carlo estimators used for variational inference - the score function estimator, the pathwise derivative estimator, and control variates - and their variance characteristics when estimating expected log likelihoods and derivatives. The chapter aims to develop practical Bayesian approaches by tying variational inference to deep learning regularization techniques like dropout.
Detailed Information
| Author: | ['Nishanth Manikandan'] |
|---|---|
| Publication Year: | 2019 |
| Pages: | 33 |
| Language: | English |
| Format: | |
| Price: | FREE |
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