[Lisa_teatalk] Deep Mind Tea-talks: Wed., Feb 20th @ 1h30pm (AA3256) + Thurs. Feb 21st @ 2:00pm (AA3256)

Guillaume Desjardins guillaume.desjardins at gmail.com
Tue Feb 19 16:51:14 EST 2013


Hi everyone,

We have two upcoming tea-talks by Daan Wierstra and Karol Gregor of Deep Mind.

Dan Wierstra will start tomorrow (Wed. Feb 20th, @1:30pm) by providing
an overview of Deep Mind and their ongoing work in deep learning. On
Thursday at 2:00pm, Karol Gregor will present two of his recent papers
(title and abstract below). See you there !


Title: A lattice filter model of the visual pathway
Authors: Karol Gregor, Dmitri B Chklovskii

Abstract:
Early stages of visual processing are thought to decorrelate, or
whiten, the incoming temporally varying signals. Because the typical
correlation time of natural stimuli, as well as the extent of temporal
receptive fields of lateral geniculate nucleus (LGN) neurons, is much
greater than neuronal time constants, such decorrelation must be done
in stages combining contributions of multiple neurons. We propose to
model temporal decorrelation in the visual pathway with the lattice
filter, a signal processing device for stage-wise decorrelation of
temporal signals. The stage-wise architecture of the lattice filter
maps naturally onto the visual pathway (photoreceptors -> bipolar
cells -> retinal ganglion cells -> LGN) and its filter weights can be
learned using Hebbian rules in a stage-wise sequential manner.
Moreover, predictions of neural activity from the lattice filter model
are consistent with physiological measurements in LGN neurons and
fruit fly second-order visual neurons. Therefore, the lattice filter
model is a useful abstraction that may help unravel visual system
function.


Title: Fast Approximations to Structured Sparse Coding and
Applications to Object Classification
Authors: Arthur Szlam , Karol Gregor, and Yann LeCun

Abstract:
We describe a method for fast approximation of sparse coding. A given
input vector is passed through a binary tree. Each leaf of the tree
contains a subset of dictionary elements. The coefficients
corresponding to these dictionary elements are allowed to be nonzero
and their values are calculated quickly by multiplication with a
precomputed pseudoinverse. The tree parameters, the dictionary, and
the subsets of the dictionary corresponding to each leaf are learned.
In the process of describing this algorithm, we discuss the more
general problem of learning the groups in group structured sparse
modeling. We show that our method creates good sparse representations
by using it in the object recognition framework of [1,2]. Implementing
our own fast version of the SIFT descriptor the whole system runs at
20 frames per second on 321 × 481 sized images on a laptop with a
quad-core cpu, while sacrificing very little accuracy on the Caltech
101, Caltech 256, and 15 scenes benchmarks.


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