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.

Example Input: 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.
Example Output: Character Sequence Models for Colorful Words

Example Input: In this paper, we introduce a new deep convolutional neural network (ConvNet) module that promotes competition among a set of multi-scale convolutional filters. This new module is inspired by the inception module, where we replace the original collaborative pooling stage (consisting of a concatenation of the multi-scale filter outputs) by a competitive pooling represented by a maxout activation unit. This extension has the following two objectives: 1) the selection of the maximum response among the multi-scale filters prevents filter co-adaptation and allows the formation of multiple sub-networks within the same model, which has been shown to facilitate the training of complex learning problems; and 2) the maxout unit reduces the dimensionality of the outputs from the multi-scale filters. We show that the use of our proposed module in typical deep ConvNets produces classification results that are either better than or comparable to the state of the art on the following benchmark datasets: MNIST, CIFAR-10, CIFAR-100 and SVHN.
Example Output: Competitive Multi-scale Convolution

Example Input: The analysis of the current integration attempts of some modes and use cases of user-machine interaction is presented. The new concept of the user-driven intelligent interface is proposed on the basis of multimodal augmented reality and brain-computer interaction for various applications: in disabilities studies, education, home care, health care, etc. The several use cases of multimodal augmentation are presented. The perspectives of the better human comprehension by the immediate feedback through neurophysical channels by means of brain-computer interaction are outlined. It is shown that brain– computer interface (BCI) technology provides new strategies to overcome limits of the currently available user interfaces, especially for people with functional disabilities. The results of the previous studies of the low end consumer and open-source BCI-devices allow us to conclude that combination of machine learning (ML), multimodal interactions (visual, sound, tactile) with BCI will profit from the immediate feedback from the actual neurophysical reactions classified by ML methods. In general, BCI in combination with other modes of AR interaction can deliver much more information than these types of interaction themselves. Even in the current state the combined AR-BCI interfaces could provide the highly adaptable and personal services, especially for people with functional disabilities. Keywords— augmented reality, interfaces for accessibility, multimodal user interface, brain-computer interface, eHealth, machine learning, machine-to-machine interactions, human-tohuman interactions, human-to-machine interactions
Example Output:
User-driven Intelligent Interface on the Basis of Multimodal Augmented Reality and Brain-Computer Interaction for People with Functional Disabilities