# Talks

## An Introduction to PAC-Bayes (Cambridge Machine Learning Reading Group)

Along with David Burt and Javier Antoran, I gave an introductory talk on the statistical learning framework and PAC-Bayes. Video, slides.

## Understanding Approximate Inference in Bayesian Neural Networks: A Joint Talk

Together with Sebastian Farquhar, I gave a talk on my NeurIPS 2020 paper on approximate inference in BNNs. Our talks present different perspectives on the effectiveness of the mean-field approximation in these models. Video, slides.

## On the Expressiveness of Approximate Inference in Bayesian Neural Networks (NeurIPS 2020)

A short video explaining my NeurIPS 2020 paper on BNN inference. Video.

## Meta-learning Stationary Stochastic Process Prediction with Convolutional Neural Processes (NeurIPS 2020)

A short video explaining my NeurIPS 2020 paper on neural processes. Video.

## Neural Processes (Cambridge Machine Learning Reading Group)

A reading group talk given with Sebastian Ober and Stratis Markou, introducing various neural processes and covering much of the material in this blog post. Slides.

## Recent Advances in Bayesian Deep Learning (Cambridge Machine Learning Reading Group)

A reading group talk given with Siddharth Swaroop, covering modern stochastic gradient Markov chain Monte Carlo (SGMCMC) and natural gradient variational inference methods for Bayesian deep learning. Slides.

## ‘In-between’ Uncertainty in Bayesian Neural Networks (Contributed talk at ICML 2019 Workshop on Uncertainty in Deep Learning)

This is a short talk explaining my workshop paper on the lack of ‘in-between’ uncertainty when using the mean-field approximation in BNNs. Video (begins at 28:30), slides.

## Implicit Variational Inference (Cambridge Machine Learning Reading Group)

With David Burt, I gave a reading group presentation introducing implicit variational inference, a way to obtain very flexible approximate posterior distributions. Slides.