Videos from the Inference Group

Speech Dasher, by Keith Vertanen and David MacKay (1'39)

Speech Dasher allows efficient text entry using a combination of speech and navigation via pointing. Further information.

Speech Dasher video

Parakeet, by Keith Vertanen and Per Ola Kristensson (3'37)

Parakeet is a system for efficient mobile text entry using speech and a touch-screen interface. Further information.

Parakeet video

Nomon Keyboard tutorial, by Tamara Broderick

This tutorial shows you how to write with the Nomon Keyboard. Nomon is a method for point selection with a single switch. In the application shown, Nomon enables selection of letters, words, and functions. Further information.

See also:
  • an example sentence written using Nomon (low res version)
  • the same sentence written using 'The Grid' (popular commercial scanning software) (low res version)
  • Nomon Keyboard tutorial

    Dasher on the iPhone (1'00) (port by Alan Lawrence)

    Shows Dasher driven by touching the touch-screen, either to the left or the right of the required destination; some familiarity with Dasher is assumed. The overall speed adjuster is at the bottom the display.
    Dasher on iPhone Video

    Dasher by Tilting iPhone (1'00) (port by Alan Lawrence)

    Tilt-mode uses Dasher's one-dimensional mode. The user calibrates by indicating the range of motion they wish to use. In normal driving of Dasher, just one dimension of control is used. A second tilt dimension can be used to slow down Dasher, if desired.
    Dasher on iPhone (tilt) Video

    How many light bulbs?

    David MacKay talks about energy arithmetic and energy plans for the UK. [This video was produced by the University of Cambridge, for 'Cambridge Ideas'.]

    Further reading - Sustainable Energy - without the hot air.


    Longer Videos of Research Talks

    Gaussian Process Basics David MacKay at the 2006 Workshop on Gaussian Processes in Practice, Bletchley Park

    How on earth can a plain old Gaussian distribution be useful for sophisticated regression and machine learning tasks?

    Factor models for QTL studies

    Oliver Stegle at the 2008 Workshop on Learning and Inference in Computational Systems Biology, Glasgow

    The recent availability of large scale data sets profiling single nucleotide polymorphisms (SNPs) and gene expression across different human populations, has directed much attention towards discovering patterns of genetic variation and their association with gene regulation. Two aspects of the nature of expression profiles make the identification and interpretation of such associations difficult. Firstly, we expect that a variety of environmental, developmental and other factors influence gene expression which can obscure such associations. Secondly, the regulatory network linking genes makes it difficult to pinpoint causal relationships between SNPS and regulatory elements. We address the first issue by proposing FA-eQTL, a factor-model that explicitly takes non-genetic variability into account, and thereby can significantly improve the power of an expression Quantitative Trait Loci (eQTL) study. We discuss a variational Bayesian implementation of this model, and point out rapid approximations that are applicable in certain situations. Applying our model to simulated and real world data we can demonstrate a significant improvement in performance. On data from the HapMap project, we find more than three times as many significant associations than a standard eQTL method. To address co-expression of genes, we further extended FA-eQTL by jointly reducing the dimensionality of the ex-pression profile and modelling non-genetic factors. We discuss results applying this enhanced QTL-model to biological data, including human as well as datasets from yeast.

    Nonparametric Bayesian Density Modeling with Gaussian Processes

    Ryan Adams at the 2008 ICML/UAI/COLT Workshop on Nonparametric Bayes, Helsinki

    We present the Gaussian Process Density Sampler (GPDS), an exchangeable generative model for use in nonparametric Bayesian density estimation. Samples drawn from the GPDS are consistent with exact, independent samples from a fixed density function that is a transformation of a function drawn from a Gaussian process prior. Our formulation allows us to infer an unknown density from data using Markov chain Monte Carlo, which gives samples from the posterior distribution over density functions and from the predictive distribution on data space. We describe two such MCMC methods. Both methods also allow inference of the hyperparameters of the Gaussian process.

    Gaussian Process Product Models for Nonparametric Nonstationarity

    Oliver Stegle at the 2008 International Conference on Machine Learning, Helsinki

    Stationarity is often an unrealistic prior assumption for Gaussian process regression. One solution is to predefine an explicit nonstationary covariance function, but such covariance functions can be difficult to specify and require detailed prior knowledge of the nonstationarity. We propose the Gaussian process product model (GPPM) which models data as the pointwise product of two latent Gaussian processes to nonparametrically infer nonstationary variations of amplitude. This approach differs from other nonparametric approaches to covariance function inference in that it operates on the outputs rather than the inputs, resulting in a significant reduction in computational cost and required data for inference, while improving scalability to high-dimensional input spaces. We present an approximate inference scheme using Expectation Propagation. This variational approximation yields convenient GP hyperparameter selection and compact approximate predictive distributions.