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.

Input: Consider Input: 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.

Output: On Training Deep Boltzmann Machines


Input: Consider Input: The dueling bandits problem is an online learning framework for learning from pairwise preference feedback, and is particularly wellsuited for modeling settings that elicit subjective or implicit human feedback. In this paper, we study the problem of multi-dueling bandits with dependent arms, which extends the original dueling bandits setting by simultaneously dueling multiple arms as well as modeling dependencies between arms. These extensions capture key characteristics found in many realworld applications, and allow for the opportunity to develop significantly more efficient algorithms than were possible in the original setting. We propose the SELFSPARRING algorithm, which reduces the multi-dueling bandits problem to a conventional bandit setting that can be solved using a stochastic bandit algorithm such as Thompson Sampling, and can naturally model dependencies using a Gaussian process prior. We present a no-regret analysis for multi-dueling setting, and demonstrate the effectiveness of our algorithm empirically on a wide range of simulation settings.

Output: Multi-dueling Bandits with Dependent Arms


Input: Consider Input: This paper discusses models for dialogue state tracking using recurrent neural networks (RNN). We present experiments on the standard dialogue state tracking (DST) dataset, DSTC2 [7]. On the one hand, RNN models became the state of the art models in DST, on the other hand, most state-of-the-art DST models are only turn-based and require dataset-specific preprocessing (e.g. DSTC2-specific) in order to achieve such results. We implemented two architectures which can be used in an incremental setting and require almost no preprocessing. We compare their performance to the benchmarks on DSTC2 and discuss their properties. With only trivial preprocessing, the performance of our models is close to the state-ofthe-art results.1


Output: Recurrent Neural Networks for Dialogue State Tracking