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.

We present a neural network architecture to predict a point in color space from the sequence of characters in the color's name. Using large scale colorname pairs obtained from an online color design forum, we evaluate our model on a "color Turing test" and find that, given a name, the colors predicted by our model are preferred by annotators to color names created by humans. Our datasets and demo system are available online at http://colorlab.us.
Character Sequence Models for Colorful Words

Reinforcement learning systems are often concerned with balancing exploration of untested actions against exploitation of actions that are known to be good. The benefit of exploration can be estimated using the classi cal notion of Value of Informationthe expected im provement in future decision quality arising from the tnformation acquired by exploration. Estimating this quantity requires an assessment of the agent's uncer tainty about its current value estimates for states. In this paper we investigate ways to represent and rea son about this uncertainty in algorithms where the sys tem attempts to learn a model of its environment. We explicitly represent uncertainty about the parameters of the model and build probability distributions over Q values based on these. These distributions are used to compute a myopic approximation to the value of infor mation for each action and hence to select the action that best balances exploration and exploitation.
Model based Bayesian Exploration

In this study we address the problem of training a neuralnetwork for language identification using both labeled and unlabeled speech samples in the form of i-vectors. We propose a neural network architecture that can also handle out-of-set languages. We utilize a modified version of the recently proposed Ladder Network semisupervised training procedure that optimizes the reconstruction costs of a stack of denoising autoencoders. We show that this approach can be successfully applied to the case where the training dataset is composed of both labeled and unlabeled acoustic data. The results show enhanced language identification on the NIST 2015 language identification dataset.
A Semisupervised Approach for Language Identification based on Ladder Networks