Wednesday, February 12 at 13:30

Investigating Batch Inference in an Sequential Monte Carlo Framework for Deep Learning
Andrew Millard, University of Liverpool
Abstract: Bayesian Inference provides a principled framework for utilising probabilistic methods to estimate the underlying posterior distribution of various models, including neural networks. Bayesian neural networks provide uncertainty quantification while still performing competitively in terms of accuracy and optimal loss compared to traditional methods. However, direct computation of neural network posterior distributions is often intractable. Mini-batch stochastic gradient descent is often used in deep learning settings in order to speed up training and reduce computational load. We investigate extending the use of mini-batches in a Bayesian framework, specifically in sequential Monte Carlo samplers to speed up inference. We also investigate the use of data annealing schemes and demonstrate that gradually increasing the batch size dramatically reduces training time with no drop-off in loss or accuracy.
Location: Alan Turing
Organizer: Louis Ohl

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