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.

Q: Our experience of the world is multimodal we see objects, hear sounds, feel texture, smell odors, and taste flavors. Modality refers to the way in which something happens or is experienced and a research problem is characterized as multimodal when it includes multiple such modalities. In order for Artificial Intelligence to make progress in understanding the world around us, it needs to be able to interpret such multimodal signals together. Multimodal machine learning aims to build models that can process and relate information from multiple modalities. It is a vibrant multi-disciplinary field of increasing importance and with extraordinary potential. Instead of focusing on specific multimodal applications, this paper surveys the recent advances in multimodal machine learning itself and presents them in a common taxonomy. We go beyond the typical early and late fusion categorization and identify broader challenges that are faced by multimodal machine learning, namely: representation, translation, alignment, fusion, and co-learning. This new taxonomy will enable researchers to better understand the state of the field and identify directions for future research.

A: Multimodal Machine Learning: A Survey and Taxonomy
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Q: © Scott A. Hale 2016. This is the author’s version of the work. It is posted here for your personal use. Not for redistribution. The definitive version was published in CHI EA 2016, http://dx.doi.org/10.1145/2851581.2892466. Abstract The number of user reviews of tourist attractions, restaurants, mobile apps, etc. is increasing for all languages; yet, research is lacking on how reviews in multiple languages should be aggregated and displayed. Speakers of different languages may have consistently different experiences, e.g., different information available in different languages at tourist attractions or different user experiences with software due to internationalization/localization choices. This paper assesses the similarity in the ratings given by speakers of different languages to London tourist attractions on TripAdvisor. The correlations between different languages are generally high, but some language pairs are more correlated than others. The results question the common practice of computing average ratings from reviews in many languages.

A: User Reviews and Language: How Language Influences Ratings
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Q: We present a method for implementing an Efficient Unitary Neural Network (EUNN) whose computational complexity is merelyO(1) per parameter and has full tunability, from spanning part of unitary space to all of it. We apply the EUNN in Recurrent Neural Networks, and test its performance on the standard copying task and the MNIST digit recognition benchmark, finding that it significantly outperforms a non-unitary RNN, an LSTM network, an exclusively partial space URNN and a projective URNN with comparable parameter numbers.

A:
Tunable Efficient Unitary Neural Networks (EUNN) and their application to RNN
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