Yarin Gal

Deep Bayesian Active Learning with Image Data.

Even though active learning forms an important pillar of machine learning, deep learning tools are not prevalent within it. Deep learning poses several difficulties when used in an active learning setting. First, active learning (AL) methods generally rely on being able to learn and update models from small amounts of data. Recent advances in deep learning, on the other hand, are notorious for their dependence on large amounts of data. Second, many AL acquisition functions rely on model uncertainty, yet deep learning methods rarely represent such model uncertainty. In this paper we combine recent advances in Bayesian deep learning into the active learning framework in a practical way. We develop an active learning framework for high dimensional data, a task which has been extremely challenging so far, with very sparse existing literature. Taking advantage of specialised models such as Bayesian convolutional neural networks, we demonstrate our active learning techniques with image data, obtaining a significant improvement on existing active learning approaches. We demonstrate this on both the MNIST dataset, as well as for skin cancer diagnosis from lesion images (ISIC2016 task).

Concrete Problems for Autonomous Vehicle Safety: Advantages of Bayesian Deep Learning

Adrian Weller acknowledges support by the Alan Turing Institute under the EPSRC grant EP/N510129/1, and by the Leverhulme Trust via the CFI.

Overcoming Alpha-Beta Limitations Using Evolved Artificial Neural Networks

In order to give the computer the ability to play against human opponents, one could utilize the Alpha-Beta algorithm. However, this algorithm has several limitations restricting its playing capabilities. Over the years, many variants of this algorithm were developed, among them a couple that make use of neural networks: a neural network to focus the search in the game tree, and a neural network trained without expert knowledge that substitutes the heuristic function in the Alpha-Beta algorithm. In this paper the weaknesses of the Alpha-Beta algorithm are reviewed alongside its variants that use neural networks. It is explained how each approach overcomes different limitations of the Alpha-Beta algorithm, and an attempt to overcome its weaknesses by the use of a combination of the neural network algorithms is presented. The proposed hybrid algorithm, which was developed using Evolutionary Strategies, still keeps the advantages of each of the individual neural algorithms, and shows a significant improvement in play against them.