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

Guillaume Desjardins guillaume.desjardins at gmail.com
Wed Feb 20 12:40:41 EST 2013


A reminder for today's tea-talks by Daan Wierstra, at 1:30pm.

On Tue, Feb 19, 2013 at 4:51 PM, Guillaume Desjardins
<guillaume.desjardins at gmail.com> wrote:
> 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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