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.
One example is below.
Q: We propose a local coherence model based on a convolutional neural network that operates over the entity grid representation of a text. The model captures long range entity transitions along with entity-specific features without loosing generalization, thanks to the power of distributed representation. We present a pairwise ranking method to train the model in an end-to-end fashion on a task and learn task-specific high level features. Our evaluation on three different coherence assessment tasks demonstrates that our model achieves state of the art results outperforming existing models by a good margin.
A: A Neural Local Coherence Model
Rationale: This statement "A Neural Local Coherence Model" is taken from the subtext "convolutional neural network" and its explanation in the passage. This is a positive example because the title belongs to the mentions in the passage
Q: An important subclass of hybrid Bayesian networks are those that represent Conditional Linear Gaussian (CLG) distributionsa distribution with a multivari ate Gaussian component for each instantiation of the discrete variables. In this paper we explore the prob lem of inference in CLGs, and provide complexity re sults for an important class of CLGs, which includes Switching Kalman Filters. In particular, we prove that even if the CLG is restricted to an extremely simple structure of a polytree, the inference task is NP-hard. Furthermore, we show that, unless P=NP, even ap proximate inference on these simple networks is in tractable. Given the often prohibitive computational cost of even approximate inference, we must take advantage of spe cial domain properties which may enable efficient in ference. We concentrate on the fault diagnosis domain, and explore several approximate inference algorithms. These algorithms try to find a small subset of Gaus sians which are a good approximation to the full mix ture distribution. We consider two Monte Carlo ap proaches and a novel approach that enumerates mix ture components in order of prior probability. We com pare these methods on a variety of problems and show that our novel algorithm is very promising for large, hybrid diagnosis problems.
A:
Inference in Hybrid Networks: Theoretical Limits and Practical Algorithms