Summer School Tutorials: Monday 2nd July, 2018
Simula Research Laboratory
Evrim received her MS and PhD in Computer Science from Rensselaer Polytechnic Institute (Troy, NY) in December 2006 and May 2008. She got her BS in Computer Engineering from Bogazici University (Istanbul, Turkey) in July 2003.
Tensor Factorizations, Data Fusion & Applications
Slides for this tutorial
In this tutorial, we will start with an introduction to tensor factorizations with a focus on the CANDECOMP/PARAFAC (CP) model, then formulate data fusion as a coupled matrix and tensor factorization (CMTF) problem and discuss CMTF-based fusion models. The tutorial will include discussions on various algorithmic approaches, handling missing data, applications from chemometrics, metabolomics, neuroscience, and recommender systems, as well as hands-on exercises on real data sets using the Tensor Toolbox and CMTF Toolbox.
Richard E. Turner
Computational Perception Group
Computational & Biological Learning Lab
University of Cambridge, UK
Bayesian Inference and Deep Learning
Slides for this tutorial
This tutorial will provide an introduction to Bayesian inference through a set of simple inference problems. Popular approximation methods, including Laplace’s approximation and variational free-energy methods, will be covered along the way. I will then introduce some recent exciting applications in which Bayesian inference has been used to address some of the key limitations of deep learning. First, we will show how Bayesian methods can mitigate deep learning methods' tendency to be highly over-confident which is critical for decision making (e.g. autonomous driving or medical imaging). Second, we will show that Bayesian deep learning methods are less easy to fool by adversarial inputs and more robust. Third, we will show that Bayesian deep learning methods can learn incrementally in an online fashion, rather than suffering from the catastrophic forgetting problem that plagues conventional techniques in this setting. The tutorial will conclude by highlighting current and future research directions at the interface between Bayesian inference and deep learning.
Russell Mason, Ryan Chungeun Kim,
IoSR / CVSSP
University of Surrey, UK
Ryan Chungeun Kim studied electrical engineering in Seoul National University in Korea, Sound and Vibration in Southampton University, UK, and psychoacoustic engineering in University of Surrey, UK. Since then he has worked as postdoctoral researcher at University of Surrey, Eindhoven University of Technology in the Netherlands, and Imperial College London. His research area covers spatial audio production and coding for multimedia streaming, auditory perception/cognition, quality evaluation, computational modelling of peripheral auditory processing, and perception-based prediction models of audio quality attributes.
Dominic Ward is a research fellow at the Centre for Vision, Speech and Signal Processing (CVSSP); University of Surrey. As part of the EPSRC-funded project “Musical Audio Repurposing using Source Separation”, he is investigating the subjective quality of musical audio source separation algorithms and developing open-source tools to facilitate and demonstrate source searation related work. His PhD thesis, completed at Birmingham City University, explored the application of psychoacoustic loudness models to audio engineering, with a particular focus on measuring and modelling the loudness of musical sounds and developing real-time audio applications. Dominic worked as a research fellow at The Sensory Motor Neuroscience Centre, University of Birmingham, where he conducted psychophysical experiments to study inter-player timing synchronisation of musical ensembles.
Evaluation of Musical Audio Source Separation
Slides for this tutorial
The success of the source separation can be evaluated using objective algorithms or subjective judgements made by a listener. We will introduce objective toolkits, such as BSS Eval and PEASS, commonly used by the research community to evaluate separation performance, and demonstrate their effectiveness and limitations given different applications. This will also include the importance of selecting appropriate test signals, and appropriate statistical methods for summarising the results. The topic of subjective perception will be presented within the context of MASS, highlighting important subjective attributes of both isolated sources and music mixtures remixed through source separation. In addition, we will demonstrate how new open-source software for assessing the audio quality of different systems was used for the perceptual evaluation of the MUS challenge of SiSEC 2018. The tutorial will conclude by discussing current problems in modelling source separation perception and present directions for future work.