[Comp-neuro] Reminder and Venue Change: Gatsby Annual Seminar
dayan at gatsby.ucl.ac.uk
Sat May 13 10:38:51 CEST 2006
There has been a change in venue for the Gatsby Unit Annual
Seminar which is happening this Wednesday.
It will take place in the Leolin Price Lecture Theatre, Institute of
Child Health, 30 Guilford Street, London, very close to Queen Square.
17 May 2006
Gatsby Unit Annual Seminar
We are delighted to announce the first in an annual series of Gatsby
Seminars, with talks by distinguished researchers in theoretical
neuroscience and machine learning.
This year's talks will be given by Dr Li Zhaoping, from the Dept of
Psychology at UCL, and Prof John Shawe-Taylor, from the Dept of
Computer Science at the University of Southampton. They will be held
at 2.30pm in the Leolin Price Lecture Theatre at the ICH, and will be
followed by a wine reception at 5.00pm at The Gatsby Unit, Alexandra
House, 17 Queen Square
Titles and abstracts below.
All are welcome.
2.30pm: Dr Li Zhaoping
The More Briefly one Looks, the More Effectively one `Sees': Vision by V1
I show that a visual search task can be better performed when one
views the search array for a shorter time, and suggest an account of
this phenomenon based on an analysis of V1's contribution to
vision. The cost of a prolonged view comes from the interference of
higher level object recognition on lower level image feature
processing. A similar effect underlies the trick for art novices of
drawing a portrait upside down in order to reproduce lower level image
features, such as contours, with less interference from higher level
In our task, the search target has an uniquely oriented bar but is
identical in shape to distractors. Lower level image feature processes
enable the unique orientation to pop out, attracting gaze towards the
target. Subsequently, higher level object processes, involving
focused attention, recognize the target object in a viewpoint
invariant manner, confusing the target as being a distractor and
interfering with the task. Lower and higher processes lead to their
respective behavioural decisions manifested in eye movements and
ultimate task performances.
I will show physiological and computational evidence implicating V1
mechanisms for the lower level feature pop out, and review data about
higher object processes in higher brain areas.
3.45pm: Prof John Shawe-Taylor
Inferring Semantic Representations from Data
The talk addresses the question of how effectively we can learn
underlying semantics from data. We concentrate on text analysis as a
domain where semantics are relatively cleanly defined and on which
learning approaches have made significant advances. The links between
Latent Semantic Indexing, Latent Semantic Kernels and kernel Principal
Components Analysis are discussed and the generalisation of such
representations is discussed. Cross-lingual information retrieval
suggests the use of Canonical Correlation Analysis as a Semantic
inference tool. Again a kernel version can be defined and with
appropriate regularisation applied in high-dimensional feature
spaces. Applications of the same approach to non-text data will also
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