In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.

A BN20 network is a two level belief net in which parent interactions are modeled using the noisy-or interaction model. In this paper we discuss application of the SPI local expression language [1] to effi cient inference in large BN20 networks. In particular, we show that there is sig nificant structure which can be exploited to improve over the Quickscore result. We further describe how symbolic tech niques can provide information which can significantly reduce the computation required for computing all cause poste rior marginals. Finally, we present a novel approximation technique with pre liminary experimental results.
Symbolic Probabilistic Inference in large BN20 networks

Syntactic parsing, the process of obtaining the internal structure of sentences in natural languages, is a crucial task for artificial intelligence applications that need to extract meaning from natural language text or speech. Sentiment analysis is one example of application for which parsing has recently proven useful. In recent years, there have been significant advances in the accuracy of parsing algorithms. In this article, we perform an empirical, task-oriented evaluation to determine how parsing accuracy influences the performance of a state-of-the-art sentiment analysis system that determines the polarity of sentences from their parse trees. In particular, we evaluate the system using four well-known dependency parsers, including both current models with state-of-the-art accuracy and more innacurate models which, however, require less computational resources. The experiments show that all of the parsers produce similarly good results in the sentiment analysis task, without their accuracy having any relevant influence on the results. Since parsing is currently a task with a relatively high computational cost that varies strongly between algorithms, this suggests that sentiment analysis researchers and users should prioritize speed over accuracy when choosing a parser; and parsing researchers should investigate models that improve speed further, even at some cost to accuracy.
How Important is Syntactic Parsing Accuracy? An Empirical Evaluation on Sentiment Analysis

Although a number of auto-encoder models enforce sparsity explicitly in their learned representation while others don't, there has been little formal analysis on what encourages sparsity in these models in general. Therefore, our objective here is to formally study this general problem for regularized auto-encoders. We show that both regularization and activation function play an important role in encouraging sparsity. We provide sufficient conditions on both these criteria and show that multiple popular models like De-noising and Contractive auto-encoder and activations like Rectified Linear and Sigmoid satisfy these conditions; thus explaining sparsity in their learned representation. Our theoretical and empirical analysis together, throws light on the properties of regularization/activation that are conducive to sparsity. As a by-product of the insights gained from our analysis, we also propose a new activation function that overcomes the individual drawbacks of multiple existing activations (in terms of sparsity) and hence produces performance at par (or better) with the best performing activation for all auto-encoder models discussed.
Why Regularized Auto-Encoders learn Sparse Representation?