[Lisa_seminaires] UdeM-McGill-mPrime machine learning seminar Tues. Oct. 25th @ 14h00, AA3195

Guillaume Desjardins guillaume.desjardins at gmail.com
Mar 25 Oct 09:29:22 EDT 2011


A reminder for today's mPrime talk. The talk will be held in AA3195 as usual.

We will also be testing an experimental feature today: live broadcast
of the talk via Google Hangouts ! If you are unable to attend, you can
thus simply join our hangout by adding the user
"lisa.umontreal at gmail.com" to your circles. Hangout should start
around 13h50.

See you there !

On Fri, Oct 21, 2011 at 4:18 PM, Guillaume Desjardins
<guillaume.desjardins at gmail.com> wrote:
> A UdeM-McGill-mPrime machine learning seminar will be held this
> Tuesday, Oct. 25th. The talk given by Richard Socher, will
> take place from 14h00-15h00. The talk will be held at the Université
> de Montréal, room number to be confirmed shortly. Hope to see you there !
>
> Title: Recursive Deep Learning in Natural Language Processing and Computer
> Vision
> Abstract:
> Hierarchical and recursive structure is commonly found in different
> modalities, including natural language sentences and scene images.  I
> will present some of our recent work on three recursive neural network
> architectures that learn meaning representations for such hierarchical
> structure. These models obtain state-of-the-art performance on several
> language and vision tasks.
> The meaning of phrases and sentences is determined by the meanings of
> its words and the rules of compositionality. We introduce a recursive
> neural network (RNN) for syntactic parsing which can learn vector
> representations that capture both syntactic and semantic information
> of phrases and sentences. For instance, the phrases "declined to
> comment" and "would not disclose" have similar representations.
> Since our RNN does not depend on specific assumptions for language, it
> can also be used to find hierarchical structure in complex scene
> images. This algorithm obtains state-of-the-art performance for
> semantic scene segmentation on the Stanford Background and the MSRC
> datasets and outperforms Gist descriptors for scene classification by 4%.
> The ability to identify sentiments about personal experiences,
> products, movies etc. is crucial to understand user generated content
> in social networks, blogs or product reviews. The second architecture
> I will talk about is based on recursive autoencoders (RAE). RAEs learn
> vector representations for phrases sufficiently well as to outperform
> other traditional supervised sentiment classification methods on
> several standard datasets.
> We also show that without supervision RAEs can learn features which
> outperform previous approaches for paraphrase detection on the
> Microsoft Research Paraphrase corpus.
> This talk presents joint work with Andrew Ng and Chris Manning.


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