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:
We can program a Real-Time (RT) music improvisation system in C++ without a formal semantic or we can model it with process calculi such as the Non-deterministic Timed Concurrent Constraint (ntcc) calculus. “A Concurrent Constraints Factor Oracle (FO) model for Music Improvisation” (Ccfomi) is an improvisation model specified on ntcc. Since Ccfomi improvises non-deterministically, there is no control on choices and therefore little control over the sequence variation during the improvisation. To avoid this, we extended Ccfomi using the Probabilistic Non-deterministic Timed Concurrent Constraint calculus. Our extension to Ccfomi does not change the time and space complexity of building the FO, thus making our extension compatible with RT. However, there was not a ntcc interpreter capable of RT to execute Ccfomi. We developed Ntccrt –a RT capable interpreter for ntcc– and we executed Ccfomi on Ntccrt. In the future, we plan to extend Ntccrt to execute our extension to Ccfomi.
answer:
Probabilistic Extension to the Concurrent Constraint Factor Oracle Model for Music Improvisation


question:
We propose a new encoder-decoder approach to learn distributed sentence representations from unlabeled sentences. The word-to-vector representation is used, and convolutional neural networks are employed as sentence encoders, mapping an input sentence into a fixed-length vector. This representation is decoded using long short-term memory recurrent neural networks, considering several tasks, such as reconstructing the input sentence, or predicting the future sentence. We further describe a hierarchical encoder-decoder model to encode a sentence to predict multiple future sentences. By training our models on a large collection of novels, we obtain a highly generic convolutional sentence encoder that performs well in practice. Experimental results on several benchmark datasets, and across a broad range of applications, demonstrate the superiority of the proposed model over competing methods.
answer:
Unsupervised Learning of Sentence Representations using Convolutional Neural Networks


question:
To cope with the high level of ambiguity faced in domains such as Computer<lb>Vision or Natural Language processing, robust prediction methods often search<lb>for a diverse set of high-quality candidate solutions or proposals. In structured<lb>prediction problems, this becomes a daunting task, as the solution space (image<lb>labelings, sentence parses, etc.) is exponentially large. We study greedy algo-<lb>rithms for finding a diverse subset of solutions in structured-output spaces by<lb>drawing new connections between submodular functions over combinatorial item<lb>sets and High-Order Potentials (HOPs) studied for graphical models. Specifically,<lb>we show via examples that when marginal gains of submodular diversity functions<lb>allow structured representations, this enables efficient (sub-linear time) approxi-<lb>mate maximization by reducing the greedy augmentation step to inference in a<lb>factor graph with appropriately constructed HOPs. We discuss benefits, trade-<lb>offs, and show that our constructions lead to significantly better proposals.
answer:

Submodular meets Structured: Finding Diverse Subsets in Exponentially-Large Structured Item Sets