[Lisa_seminaires] UdeM-McGill-MITACS machine learning seminar

Dumitru Erhan dumitru.erhan at umontreal.ca
Sam 20 Juin 00:27:21 EDT 2009


Correct time is now in the subject line!

On Sat, Jun 20, 2009 at 00:25, Dumitru Erhan <dumitru.erhan at umontreal.ca>wrote:

> We have a special post-ICML/UAI seminar on Monday!
>
> On unsupervised deep learning, sparsity, and hierarchical representations
>
> by Andrew Y. Ng
>
> Location: Pavillon André-Aisenstadt (UdeM), room 3195
> Time: June 22nd, 13h30
>
> The ability to learn rich feature representations from unlabeled data
> holds the promise of getting learning methods to usefully learn from a
> vastly increased amount of data, and therefore perhaps also achieving
> vastly improved performance.  In this talk, I'll describe our recent
> work on learning sparse representations from unlabeled data,
> particularly when learning deep representations.  In detail, I'll
> describe a sparse DBN algorithm that learns (from natural images) to
> detect edges at the lowest layer, and combine edges to form "corners"
> at the next layer.  I'll also describe a comparison of this method to
> biological data from visual cortical area V2.  Further, I show that a
> larger scale version of this method, developed using the idea of
> convolutional networks, automatically identifies "object parts", and
> learns to group such object parts together to form more complete
> models of objects.  For example, when trained on images of faces, the
> algorithm identifies edges at the lowest level; assembles edges to
> form parts of faces such as "eyes" and "nose" features at the next
> level; and finally learns more complete models of faces at the highest
> level.  These methods also give features that give good classification
> performance on data of many different types of modalities, including
> images, audio, handwriting, text, and 3d laser scanner data.  Finally,
> I believe that good evaluation metrics are needed to develop good
> unsupervised deep learning algorithms, and will describe some recent
> results on developing new metrics.
>
> Joint work with Honglak Lee and others.
>
> Bio:
>
> Andrew Ng is an Assistant Professor of Computer Science at Stanford
> University.  His research interests include machine learning,
> reinforcement learning/control, and broad-competence AI.  His group has
> won best paper/best student paper awards at ACL, CEAS, 3DRR and ICML.  He
> is also a recipient of the Alfred P. Sloan Fellowship, and the IJCAI 2009
> Computers and Thought award.
> _______________________________________________
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>



-- 
http://dumitru.ca, MTL: +1-514-432-8435, Calif: +1-650-906-6277
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