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.

Recently, bidirectional recurrent network language models (biRNNLMs) have been shown to outperform standard, unidirectional, recurrent neural network language models (uni-RNNLMs) on a range of speech recognition tasks. This indicates that future word context information beyond the word history can be useful. However, bi-RNNLMs pose a number of challenges as they make use of the complete previous and future word context information. This impacts both training efficiency and their use within a lattice rescoring framework. In this paper these issues are addressed by proposing a novel neural network structure, succeeding word RNNLMs (suRNNLMs). Instead of using a recurrent unit to capture the complete future word contexts, a feedforward unit is used to model a finite number of succeeding, future, words. This model can be trained much more efficiently than bi-RNNLMs and can also be used for lattice rescoring. Experimental results on a meeting transcription task (AMI) show the proposed model consistently outperformed uni-RNNLMs and yield only a slight degradation compared to bi-RNNLMs in N-best rescoring. Additionally, performance improvements can be obtained using lattice rescoring and subsequent confusion network decoding.
FUTURE WORD CONTEXTS IN NEURAL NETWORK LANGUAGE MODELS

Observable operator models (OOMs) and related models are one of the most important and powerful tools for modeling and analyzing stochastic systems. They can exactly describe dynamics of finite-rank systems, and be efficiently learned from data by moment based algorithms. Almost all OOM learning algorithms are developed based on the assumption of equilibrium data which is very difficult to guarantee in real life, especially for complex processes with large time scales. In this paper, we derive a nonequilibrium learning algorithm for OOMs, which dismisses this assumption and can effectively extract the equilibrium dynamics of a system from nonequilibrium observation data. In addition, we propose binless OOMs for the application of nonequilibrium learning to continuous-valued systems. In comparison with the other OOMs with continuous observations, binless OOMs can achieve consistent estimation from nonequilibrium data with only linear computational complexity.
Spectral learning of dynamic systems from nonequilibrium data

To mitigate the uncertainty of variable renewable<lb>resources, two off-the-shelf machine learning tools are deployed<lb>to forecast the solar power output of a solar photovoltaic system.<lb>The support vector machines generate the forecasts and the<lb>random forest acts as an ensemble learning method to combine<lb>the forecasts. The common ensemble technique in wind and solar<lb>power forecasting is the blending of meteorological data from<lb>several sources. In this study though, the present and the past<lb>solar power forecasts from several models, as well as the<lb>associated meteorological data, are incorporated into the random<lb>forest to combine and improve the accuracy of the day-ahead<lb>solar power forecasts. The performance of the combined model is<lb>evaluated over the entire year and compared with other<lb>combining techniques. KeywordsEnsemble learning, post-processing, random forest,<lb>solar power, support vector regression.
Random Forest Ensemble of Support Vector Regression Models for Solar Power Forecasting