[Lisa_seminaires] [Lisa_labo] Talks by Chris Williams Monday 28th AA 3195

Yoshua Bengio yoshua.bengio at gmail.com
Dim 20 Oct 06:40:50 EDT 2013


I encourage you all to come. Chris Williams is among the best researchers
of our field and I always enjoy my exchanges with him.

-- Yoshua


On Sat, Oct 19, 2013 at 10:01 PM, Razvan Pascanu <r.pascanu at gmail.com>wrote:

> Hi all,
>
>  Monday 28th October we will have Chris Williams giving to talks. Both of
> them will be in the rmm AA3195 (pavillon Andre-Aisendstandt, click here<http://maps.google.ca/maps?q=2920,+chemin+de+la+tour&hl=en&z=16&iwloc=A> for
> directions) at the Université de Montréal. Abstract and title below. Hope
> to see you there !
>
> First talk at 10:30 in AA3195
>
> Title: Missing Components in Visual Scene Understanding: Hierarchy and
> Perceptual Organization?
>
> Chris Williams, School of Informatics, University of Edinburgh
>
> There has been considerable progress in object recognition and scene
> understanding over the last decade. However, we argue that much of
> this work uses relatively shallow architectures, and that more
> attention needs to be paid to hierarchical models and to processes of
> perceptual organization, within a probabilistic inference framework.
>
> -----------------------
>
> Second talk  at 13:30 in AA3195
>
>
> Title: Switching Linear Dynamical Systems for Condition
> Monitoring in the Intensive Care Unit
>
> Abstract:
>
> Data drawn from an observed system is often usefully described by a
> number of hidden (or latent) factors.  Given a sequence of
> observations, the task is to infer which latent factors are active at
> each time frame.  In this talk I will describe the application of a
> switching linear dynamical model to monitoring the condition
> of a patient receiving intensive care.  The state of health of
> a patient cannot be observed directly, but different underlying factors
> are associated with particular patterns of measurements, e.g. in the
> heart rate, blood pressure and temperature.
>
> We demonstrate how to exploit knowledge of the structure of how the
> various latent factors interact so as to reduce the amount of training
> data needed for the system. A combination of domain knowledge
> engineering and learning is used to produce an effective solution. We
> use the model to infer the presence of two different types of factors:
> common, recognisable regimes (e.g. certain artifacts or common
> physiological phenomena), and novel patterns which are clinically
> significant but have unknown cause. Experimental results are given
> showing the potential of the system for the early detection of
> neonatal sepsis, a major clinical concern in the care of premature
> babies.
>
> Chris Williams. Joint work with Yvonne Freer, Neil McIntosh, John
> Quinn, Ioan Stanculescu.
>
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>
>
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