Text-level discourse parsing remains a challenge: most approaches employ fea-tures that fail to capture the intentional, se-mantic, and syntactic aspects that govern discourse coherence. In this paper, we pro-pose a recursive model for discourse pars-ing that jointly models distributed repre

Semantic word spaces have been very useful but cannot express the meaning of longer phrases in a principled way. Further progress towards understanding compositionality in tasks such as sentiment detection requires richer supervised training and evaluation resources and more powerful models

This supplementary material contains four sections. The first section details the design and methods we used to create the Sentiment Treebank. The sec-ond provides an analysis of how much this tree-bank helps performance on recursive models. The third section gives several examples of the data we

We show how to use “complementary priors ” to eliminate the explaining away effects that make inference difficult in densely-connected belief nets that have many hidden layers. Using complementary priors, we derive a fast, greedy algorithm that can learn deep, directed belief networks one layer

Abstract We trained a large, deep convolutional neural network to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes. On the test data, we achieved top-1 and top-5 error rates of 37.5% and 17.0% which is considerably better than

A long-standing difficulty for connectionist modeling has been how to represent variable-sized recursive data structures, such as trees and lists, in fixed-width patterns. This paper presents a connectionist architecture which automatically develops compact distributed representations

) are automatically determined by the data. This procedure is motivated by the recursive partitioning approach to regression and shares its attractive properties. Unlike recursive partitioning, however, this method produces continuous models with continuous derivatives. It has more power and flexibility to model

Abstract-The description of a novel type of rn-gram language model is given. The model offers, via a nonlinear recursive procedure, a com-putation and space efficient solution to the problem of estimating prob-abilities from sparse data. This solution compares favorably to other proposed methods

In 1960, R.E. Kalman published his famous paper describing a recursive solution to the discrete-data linear filtering problem. Since that time, due in large part to advances in digital computing, the Kalman filter has been the subject of extensive research and application, particularly in the area

only the intrinsic value of the customer (i.e, the expected pro t from sales to her). We propose to model also the customer's network value: the expected pro t from sales to other customers she may inuence to buy, the customers those may inuence, and so on recursively. Instead of viewing a market