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 neural network architecture to predict a point in color space from the sequence of characters in the color’s name. Using large scale color–name 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.
[EX A]: Character Sequence Models for Colorful Words

[EX Q]: The basic features of some of the most versatile and popular open source frameworks for machine learning (TensorFlow, Deep Learning4j, and H2O) are considered and compared. Their comparative analysis was performed and conclusions were made as to the advantages and disadvantages of these platforms. The performance tests for the de facto standard MNIST data set were carried out on H2O framework for deep learning algorithms designed for CPU and GPU platforms for single-threaded and multithreaded modes of operation. Keywords—machine learning; deep learning; TensorFlow; Deep Learning4j; H2O; MNIST; multicore CPU; GPU.
[EX A]: Comparative Analysis of Open Source Frameworks for Machine Learning with Use Case in Single- Threaded and Multi-Threaded Modes

[EX Q]: <lb>This work considers the problem of learning the structure of multivariate linear tree models, which<lb>include a variety of directed tree graphical models with continuous, discrete, and mixed latent variables<lb>such as linear-Gaussian models, hidden Markov models, Gaussian mixture models, and Markov evolu-<lb>tionary trees. The setting is one where we only have samples from certain observed variables in the tree,<lb>and our goal is to estimate the tree structure (i.e., the graph of how the underlying hidden variables are<lb>connected to each other and to the observed variables). We propose the Spectral Recursive Grouping al-<lb>gorithm, an efficient and simple bottom-up procedure for recovering the tree structure from independent<lb>samples of the observed variables. Our finite sample size bounds for exact recovery of the tree structure<lb>reveal certain natural dependencies on underlying statistical and structural properties of the underlying<lb>joint distribution. Furthermore, our sample complexity guarantees have no explicit dependence on the<lb>dimensionality of the observed variables, making the algorithm applicable to many high-dimensional set-<lb>tings. At the heart of our algorithm is a spectral quartet test for determining the relative topology of a<lb>quartet of variables from second-order statistics.
[EX A]:
Spectral Methods for Learning Multivariate Latent Tree Structure