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@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@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.