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.

Ex Input:
Recurrent Neural Networks (RNNs), and specifically a variant with Long ShortTerm Memory (LSTM), are enjoying renewed interest as a result of successful applications in a wide range of machine learning problems that involve sequential data. However, while LSTMs provide exceptional results in practice, the source of their performance and their limitations remain rather poorly understood. Using character-level language models as an interpretable testbed, we aim to bridge this gap by providing a comprehensive analysis of their representations, predictions and error types. In particular, our experiments reveal the existence of interpretable cells that keep track of long-range dependencies such as line lengths, quotes and brackets. Moreover, an extensive analysis with finite horizon n-gram models suggest that these dependencies are actively discovered and utilized by the networks. Finally, we provide detailed error analysis that suggests areas for further study.

Ex Output:
Visualizing and Understanding Recurrent Networks


Ex Input:
We seek decision rules for prediction-time cost reduction, where complete data is available for training, but during prediction-time, each feature can only be acquired for an additional cost. We propose a novel random forest algorithm to minimize prediction error for a user-specified average feature acquisition budget. While random forests yield strong generalization performance, they do not explicitly account for feature costs and furthermore require low correlation among trees, which amplifies costs. Our random forest grows trees with low acquisition cost and high strength based on greedy minimax cost-weighted-impurity splits. Theoretically, we establish near-optimal acquisition cost guarantees for our algorithm. Empirically, on a number of benchmark datasets we demonstrate superior accuracy-cost curves against state-of-the-art prediction-time algorithms.

Ex Output:
Feature-Budgeted Random Forest


Ex Input:
Whereas acausal Bayesian networks rep­ resent probabilistic independence, causal Bayesian networks represent causal relation­ ships. In this paper, we examine Bayesian methods for learning both types of networks. Bayesian methods for learning acausal net­ works are fairly well developed. These meth­ ods often employ assumptions to facilitate the construction of priors, including the as­ sumptions of parameter independence, pa­ rameter modularity, and likelihood equiva­ lence. We show that although these assump­ tions also can be appropriate for learning causal networks, we need additional assump­ tions in order to learn causal networks. We introduce two sufficient assumptions, called mechanism independence and component in­ dependence. We show that these new as­ sumptions, when combined with parame­ ter independence, parameter modularity, and likelihood equivalence, allow us to apply methods for learning acausal networks to learn causal networks.

Ex Output:

A Bayesian Approach to Learning Causal Networks