#### Problem addressed: 
Learning recursive structure from natural data - sentences and images

#### Summary: 
This paper introduces recursive neural networks in order to learn recursive structure in data. This recursive structure is illustrated by using examples of scene understanding using superpixel labels and parsing of natural language sentences. The authors go on define a novel neural network architecture, an objective function based on max-margin estimation and provide methods for backpropagation to train this network. They then evaluate its performance for accuracy in pixel labelling on the Stanford background dataset as well as for parsing sentences of Wall Street Journal section of the Penn TreeBank database. They achieve state-of-the-art and near state-of-the-art results respectively.

#### Novelty:
Introduction of recursive neural networks, framing an objective function to learn a recursive tree structure from data
 
 State of the art results on scene understanding and pixel labels for Stanford background dataset

#### Drawbacks:
Computational analysis of method not provided Not many details about the backpropagation method.

#### Datasets:
Stanford background dataset, Penn TreeBank

#### Additional remarks:
The thesis of the first author, Chapter 3 was referred during presentation. This paper almost identical to that and the thesis provides slightly more details.

#### Resources:
paper: http://www-nlp.stanford.edu/pubs/SocherLinNgManning_ICML2011.pdf
 
 thesis: http://nlp.stanford.edu/~socherr/thesis.pdf

#### Presenter:
Bhargava U. Kota