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 Q]: We present a character-level recurrent neural network that generates relevant and coherent text given auxiliary information such as a sentiment or topic. Using a simple input replication strategy, we preserve the signal of auxiliary input across wider sequence intervals than can feasibly be trained by back-propagation through time. Our main results center on a large corpus of 1.5 million beer reviews from BeerAdvocate. In generative mode, our network produces reviews on command, tailored to a star rating or item category. The generative model can also run in reverse, performing classification with surprising accuracy. Performance of the reverse model provides a straightforward way to determine what the generative model knows without relying too heavily on subjective analysis. Given a review, the model can accurately determine the corresponding rating and infer the beer’s category (IPA, Stout, etc.). We exploit this capability, tracking perceived sentiment and class membership as each character in a review is processed. Quantitative and qualitative empirical evaluations demonstrate that the model captures meaning and learns nonlinear dynamics in text, such as the effect of negation on sentiment, despite possessing no a priori notion of words. Because the model operates at the character level, it handles misspellings, slang, and large vocabularies without any machinery explicitly dedicated to the purpose.
[EX A]: CHARACTER-LEVEL GENERATIVE TEXT MODELS

[EX Q]: Positive unlabeled (PU) learning is useful in various practical situations, where there is a need to learn a classifier for a class of interest from an unlabeled data set, which may contain anomalies as well as samples from unknown classes. The learning task can be formulated as an optimization problem under the framework of statistical learning theory. Recent studies have theoretically analyzed its properties and generalization performance, nevertheless, little effort has been made to consider the problem of scalability, especially when large sets of unlabeled data are available. In this work we propose a novel scalable PU learning algorithm that is theoretically proven to provide the optimal solution, while showing superior computational and memory performance. Experimental evaluation confirms the theoretical evidence and shows that the proposed method can be successfully applied to a large variety of real-world problems involving PU learning.
[EX A]: Efficient Training for Positive Unlabeled Learning

[EX Q]: Deep generative models provide a powerful and flexible means to learn complex distributions over data by incorporating neural networks into latent-variable models. Variational approaches to training such models introduce a probabilistic encoder that casts data, typically unsupervised, into an entangled representation space. While unsupervised learning is often desirable, sometimes even necessary, when we lack prior knowledge about what to represent, being able to incorporate domain knowledge in characterising certain aspects of variation in the data can often help learn better disentangled representations. Here, we introduce a new formulation of semi-supervised learning in variational autoencoders that allows precisely this. It permits flexible specification of probabilistic encoders as directed graphical models via a stochastic computation graph, containing both continuous and discrete latent variables, with conditional distributions parametrised by neural networks. We demonstrate how the provision of dependency structures, along with a few labelled examples indicating plausible values for some components of the latent space, can help quickly learn disentangled representations. We then evaluate its ability to do so, both qualitatively by exploring its generative capacity, and quantitatively by using the disentangled representation to perform classification, on a variety of models and datasets.
[EX A]:
IN DEEP GENERATIVE MODELS