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

Razvan Pascanu r.pascanu at gmail.com
Lun 28 Oct 10:15:22 EDT 2013


On Fri, Oct 25, 2013 at 12:27 PM, Razvan Pascanu <r.pascanu at gmail.com>wrote:

> Kind reminder of the talks this coming Monday :
>
>
> 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.
>>
>
>
-------------- section suivante --------------
Une pièce jointe HTML a été nettoyée...
URL: https://webmail.iro.umontreal.ca/mailman/private/lisa_seminaires/attachments/20131028/0da3282c/attachment.html 


Plus d'informations sur la liste de diffusion Lisa_seminaires