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: The deep Boltzmann machine (DBM) has been an important development in the quest for powerful "deep" probabilistic models. To date, simultaneous or joint training of all layers of the DBM has been largely unsuccessful with existing training methods. We introduce a simple regularization scheme that encourages the weight vectors associated with each hidden unit to have similar norms. We demonstrate that this regularization can be easily combined with standard stochastic maximum likelihood to yield an effective training strategy for the simultaneous training of all layers of the deep Boltzmann machine.

A: On Training Deep Boltzmann Machines
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Q: We describe how to use propositional model counting for a quantitative analysis of product configuration data. Our approach computes valuable meta information such as the total number of valid configurations or the relative frequency of components. This information can be used to assess the severity of documentation errors or to measure documentation quality. As an application example we show how we apply these methods to product documentation formulas of the Mercedes-Benz line of vehicles. In order to process these large formulas we developed and implemented a new model counter for non-CNF formulas. Our model counter can process formulas, whose CNF representations could not be processed up till now.

A: Model Counting in Product Configuration
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Q: Recent works on end-to-end neural network-based architectures for machine translation have shown promising results for English-French and English-German translation. Unlike these language pairs, however, in the majority of scenarios, there is a lack of high quality parallel corpora. In this work, we focus on applying neural machine translation to challenging/low-resource languages Turkish and low-resource domains such as parallel corpora of Chinese chat messages. In particular, we investigated how to leverage abundant monolingual data for these low-resource translation tasks. Without the use of external alignment tools, we obtained up to a 1.96 BLEU score improvement with our proposed method compared to the previous best result in Turkish-to-English translation on the IWLST 2014 dataset. On Chinese-toEnglish translation by using the OpenMT 2015 dataset, we were able to obtain up to a 1.59 BLEU score improvement over phrase-based and hierarchical phrase-based baselines.

A:
On Using Monolingual Corpora in Neural Machine Translation
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