Abstract

The aim of this meetup in Microsoft Research, Cambridge is to bring together people interested in machine learning and artificial intelligence, be it applied or theory. The meetup features two 15-minute research talks followed by a small research poster session during which we serve light refreshments.

This Friday, we will have two exciting talks:

Speaker: Marc Brockschmidt (MSR Cambridge)

Title: Editing Sequences by Copying Spans

Abstract: Neural sequence-to-sequence models are finding increasing use in editing of documents, for example in correcting a text document or repairing source code. In this paper, we argue that existing seq2seq models (with a facility to copy single tokens) are not a natural fit for such tasks, as they have to explicitly copy each unchanged token. We present an extension of seq2seq models capable of copying entire spans of the input to the output in one step, greatly reducing the number of decisions required during inference. This extension means that there are now many ways of generating the same output, which we handle by deriving a new objective for training and a variation of beam search for inference that explicitly handle this problem.

Speaker: Vincent Dutordoir (Prowler)

Title: Bayesian Image Classification with Deep Convolutional Gaussian Processes

Abstract: There is a lot of focus on Bayesian deep learning at the moment, with many researchers tackling this problem by building on top of neural networks and making the inference look more Bayesian. In this talk, I’m going to follow a different strategy and use a Gaussian process, which is a well-understood probabilistic method with many attractive properties, as a primitive building block to construct fully Bayesian deep learning models. We show that the accuracy of these Bayesian methods, and the quality of their posterior uncertainties, depend strongly on the suitability of the modelling assumptions made in the prior, and that Bayesian inference by itself is often not enough. This motivates the development of a novel convolutional kernel, which leads to improved uncertainty and accuracy on a range of different problems.