[CTN] TODAY: CTN Seminar: Dr. Graham Taylor (Guelph), 3:30 Dec 16, PAS 2464

[Bryan Tripp]

Hi everyone,

Just a reminder about the seminar this afternoon.

Also, there are still spots for dinner, so please let me know if you
are interested.

Bryan

On Mon, Dec 8, 2014 at 11:19 AM, Bryan Tripp <bptripp at gmail.com> wrote:
> Hi everyone,
>
> Dr. Graham Taylor will give the next seminar, next Tuesday December
> 16. The title and abstract follow below.
>
> The time and place are as usual, 3:30 in PAS 2464.
>
> There is time for one or two individual meetings with the speaker
> during the day. Please let me know if you are interested in this
> and/or would like to join us for dinner after the talk.
>
> Hope to see you there,
>
> Bryan
>
>
>
> Dr. Graham Taylor
> Assistant Professor
> School of Engineering
> University of Guelph
>
> Learning Representations with Multiplicative Interactions
>
> Representation learning algorithms are machine learning algorithms
> which involve the learning of features or explanatory factors. Deep
> learning techniques, which employ several layers of representation
> learning, have achieved much recent success in machine learning
> benchmarks and competitions, however, most of these successes have
> been achieved with purely supervised learning methods and have relied
> on large amounts of labeled data. In this talk, I will discuss a
> lesser-known but important class of representation learning algorithms
> that are capable of learning higher-order features from data. The main
> idea is to learn relations between pixel intensities rather than the
> pixel intensities themselves by structuring the model as a tri-partite
> graph which connects hidden units to pairs of images.  If the images
> are different, the hidden units learn how the images transform. If the
> images are the same, the hidden units encode within-image pixel
> covariances. Learning such higher-order features can yield improved
> results on recognition and generative tasks. I will discuss recent
> work on applying these methods to structured prediction problems.