instruction:
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.
question:
Optimal probabilistic approach in reinforcement learning is computationally infeasible. Its simplification consisting in neglecting difference between true environment and its model estimated using limited number of observations causes exploration vs exploitation problem. Uncertainty can be expressed in terms of a probability distribution over the space of environment models, and this uncertainty can be propagated to the action-value function via Bellman iterations, which are computationally insufficiently efficient though. We consider possibility of directly measuring uncertainty of the action-value function, and analyze sufficiency of this facilitated approach.
answer:
Direct Uncertainty Estimation in Reinforcement Learning

question:
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.
answer:
Character Sequence Models for Colorful Words

question:
claims trigrams trigrams dependencies AMT-trained fixed dependencies 3.699% 4.697% 5.974% 18.18% 3.99% 2.797% 3.696% 2.597% 3.297% FEATURES (TFIDFs of) SVM Classifier's Error Rate DATASET
answer:
Improving Automated Patent Claim Parsing: Dataset, System, and Experiments