[Lisa_seminaires] UdeM-McGill-MITACS machine learning seminar Tue Oct. 19 at 15h00, AA-3195

Dumitru Erhan erhandum at iro.umontreal.ca
Ven 15 Oct 15:04:06 EDT 2010


The UdeM-McGill-MITACS machine learning seminar
series<http://www.iro.umontreal.ca/article.php3?id_article=107&lang=en>is
continuing its fall schedule. Next week's seminar:

*Large Scale Image and Music Annotation: Learning to Rank and Multi-Tasking
with Joint Embeddings
*
by Jason Weston
Google Research, NY

Location: Pavillon André-Aisenstadt (UdeM), room AA-3195
Time: Tuesday, October 19, *15:00*

Abstract: In the first part of the talk we will discuss large scale image
annotation. Image annotation datasets are becoming larger and larger, with
tens of millions of images and tens of thousands of possible annotations. We
propose a well performing method that scales to such datasets by
simultaneously learning to optimize precision at k of the ranked list of
annotations for a given image \em and learning a low-dimensional joint
embedding space for both images and annotations. Our method both outperforms
several baseline methods and, in comparison to them, is faster and consumes
less memory. We also demonstrate how our method learns an interpretable
model, where annotations with alternate spellings or even languages are
close in the embedding space. Hence, even when our model does not predict
the exact annotation given by a human labeler, it often predicts similar
annotations, a fact that we try to quantify by measuring the newly
introduced ``sibling’’ precision metric, where our method also obtains good
results.

In the second (shorter) part of the talk we will discuss large scale music
annotation. Music prediction tasks range from predicting the genre, style or
the artist given a song or clip of audio, predicting similar artists given
an artist, or predicting related songs given a song, clip, artist name or
genre or style tag. That is, we are in interested in pretty much every
semantic relationship between the different musical concepts in our
database. In realistic databases, the number of songs is measured in the
millions, and the number of artists in the tens of thousands or more,
providing a considerable challenge to standard machine learning techniques.
In this work, we propose a method that scales to such datasets which
attempts to capture the semantic similarities between the database items by
modeling audio, artist names, and genre and style tags in a single
low-dimensional semantic space. This choice of space is learnt by optimizing
the set of predictions tasks of interest jointly using multi-task learning.
Our method both outperforms baseline methods and, in comparison to them, is
faster and consumes less memory. We then demonstrate how our method learns
an interpretable model, where the semantic space captures well the
similarities of interest.

Joint work with Samy Bengio and Nicolas Usunier.
-------------- next part --------------
An HTML attachment was scrubbed...
URL: https://webmail.iro.umontreal.ca/mailman/private/lisa_seminaires/attachments/20101015/92f220e2/attachment.html 


Plus d'informations sur la liste de diffusion Lisa_seminaires