Environmental Bayesian Optimization of Gerneral-Purpose Convolutional Neural Networks

With the advent of Big Data and modern Deep Learning technology, areas such as computer vision are entering a renaissance. Performance on standard problems such as visual object recognition is soaring, and very difficult challenges such as automatic image captioning can now be tackled with good results. Yet, despite the advances in such specific tasks, deep networks are still far beyond general-purposes intelligences such as humans. A key missing feature is the ability of excelling in *complex environments*, where thousands of interrelated tasks need to be solved together. One of the outstanding technical challenges in scaling deep learning to general-purpose cognition systems is large scale optimization in such complex environments. In this project, we propose to develop Bayesian Optimization as a potential solution to this challenge.
Bayesian Optimization has proved indispensable in automatically tuning the hyper-parameters of commonly used machine learning algorithms, but has been under-explored in the training and deployment phases as well. Nevertheless, the strengths of Bayesian optimization make it an ideal candidate in large scale distributed optimization as well as optimization on multiple tasks. We will demonstrate this technology on computer vision applications. Whereas current vision algorithms focus on specific tasks (e.g. recognizing 1000 object categories or reading text), we will learn general-purpose and efficient computer vision systems from different and complementary tasks.
The company Mathworks is involved. This project falls within the EPSRC information and communication technologies research area.