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:
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.
answer:
On Training Deep Boltzmann Machines


question:
Given the incessant growth of documents describing the opinions of different people circulating on the web, including Web 2.0 has made it possible to give an opinion on any product in the net. In this paper, we examine the various opinions expressed in the tweets and classify them (positive, negative or neutral) by using the emoticons for the Bayesian method and adjectives and adverbs for the Turney’s method.
answer:
Detecting Opinions in Tweets


question:
A key problem of robotic environmental sensing and moni-<lb>toring is that of active sensing: How can a team of robots<lb>plan the most informative observation paths to minimize<lb>the uncertainty in modeling and predicting an environmen-<lb>tal phenomenon? This paper presents two principled ap-<lb>proaches to efficient information-theoretic path planning based<lb>on entropy and mutual information criteria for in situ ac-<lb>tive sensing of an important broad class of widely-occurring<lb>environmental phenomena called anisotropic fields. Our pro-<lb>posed algorithms are novel in addressing a trade-off between<lb>active sensing performance and time efficiency. An impor-<lb>tant practical consequence is that our algorithms can exploit<lb>the spatial correlation structure of Gaussian process-based<lb>anisotropic fields to improve time efficiency while preserv-<lb>ing near-optimal active sensing performance. We analyze<lb>the time complexity of our algorithms and prove analyti-<lb>cally that they scale better than state-of-the-art algorithms<lb>with increasing planning horizon length. We provide the-<lb>oretical guarantees on the active sensing performance of<lb>our algorithms for a class of exploration tasks called tran-<lb>sect sampling, which, in particular, can be improved with<lb>longer planning time and/or lower spatial correlation along<lb>the transect. Empirical evaluation on real-world anisotropic<lb>field data shows that our algorithms can perform better or<lb>at least as well as the state-of-the-art algorithms while of-<lb>ten incurring a few orders of magnitude less computational<lb>time, even when the field conditions are less favorable.
answer:

Multi-Robot Informative Path Planning for Active Sensing of Environmental Phenomena: A Tale of Two Algorithms