Two talks – “Behavioural Biometrics for Mobile Touchscreen Devices” and “Interactive Visualisation of Big Music Data”

Behavioural Biometrics for Mobile Touchscreen Devices

Speaker: Daniel Buschek
Date: 22 August, 2014
Time: 15:00 – 15:30
Location: Sir Alwyn Williams Building, 423 Seminar Room

Interactive Visualisation of Big Music Data.
Speaker: Beatrix Vad
Date: 22 August, 2014
Time: 15:30 – 16:00
Location: Sir Alwyn Williams Building, 423 Seminar Room

Musical content can be described by a variety of features that are measured or inferred through the analysis of audio data. For a large music collection this establishes the possibility to retrieve information about its structure and underlying patterns. Dimensionality reduction techniques can be used to gain insight into such a high dimensional dataset and to enable visualisation on two-dimensional screens. In this talk we investigate the usability of these techniques with respect to an interactive exploration interface for large music collections based on moods. A method employing Gaussian Processes to extend the visualisation with additional information about its composition is presented and evaluated

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Carl Rasmussen Seminar, Inference in non‐linear dynamical systems – a machine learning perspective, Tuesday 8th July, 14:00, SAWB 422

Inference in non‐linear dynamical systems – a machine learning perspective

Carl E. Rasmussen

University of Cambridge, Dept of Engineering.

Inference in discrete-time non-linear dynamical systems is often done using the Extended Kalman Filtering and Smoothing (EKF) algorithm, which provides a Gaussian approximation to the posterior based on local linearisation of the dynamics. In challenging problems, when the non-linearities are significant and the signal to noise ratio is poor, the EKF performs poorly. In this talk we will discuss an alternative algorithm developed in the machine learning community which is based message passing in Factor Graphs and the Expectation Propagation (EP) approximation. We will show this method provides a consistent and accurate Gaussian approximation to the posterior enabling system identification using Expectation Maximisation (EM) even in cases when the EKF fails.

Tuesday 8th July, 14:00, SAWB 422

Talk slides available here: http://mlg.eng.cam.ac.uk/carl/talks/consem.pdf 

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IDI @ CHI

IDI members have two papers appearing in CHI 2014:

Uncertain Text Entry on Mobile Devices. Weir, D., Pohl, H., Rogers, S., Vertanen, K., Kristensson, P-O.

and

28 frames later: predicting screen touches from back-of-device grip changes Noor, F.M., Ramsay, A., Hughes, S., Rogers, S., Williamson, J., and Murray-Smith, R.

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Seminar 17/12/13 – Daniel Buschek (LMU University, Germany)

Machine Learning for Back-of-the-Device Multitouch Typing

Tuesday 17th December, 11am, Sir Alwyn Williams Building, Room 422

 

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