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: In this paper we compare different types of recurrent units in recurrent neural networks (RNNs). Especially, we focus on more sophisticated units that implement a gating mechanism, such as a long short-term memory (LSTM) unit and a recently proposed gated recurrent unit (GRU). We evaluate these recurrent units on the tasks of polyphonic music modeling and speech signal modeling. Our experiments revealed that these advanced recurrent units are indeed better than more traditional recurrent units such as tanh units. Also, we found GRU to be comparable to LSTM.

Output: Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling


Input: Consider Input: Attack detection problems in the smart grid are posed as statistical learning problems for different attack scenarios in which the measurements are observed in batch or online settings. In this approach, machine learning algorithms are used to classify measurements as being either secure or attacked. An attack detection framework is provided to exploit any available prior knowledge about the system and surmount constraints arising from the sparse structure of the problem in the proposed approach. Well-known batch and online learning algorithms (supervised and semi-supervised) are employed with decision and feature level fusion to model the attack detection problem. The relationships between statistical and geometric properties of attack vectors employed in the attack scenarios and learning algorithms are analyzed to detect unobservable attacks using statistical learning methods. The proposed algorithms are examined on various IEEE test systems. Experimental analyses show that machine learning algorithms can detect attacks with performances higher than the attack detection algorithms which employ state vector estimation methods in the proposed attack detection framework.

Output: Machine Learning Methods for Attack Detection in the Smart Grid


Input: Consider Input: Many sequential processing tasks require complex nonlinear transition functions from one step to the next. However, recurrent neural networks with such “deep" transition functions remain difficult to train, even when using Long Short-Term Memory networks. We introduce a novel theoretical analysis of recurrent networks based on Geršgorin’s circle theorem that illuminates several modeling and optimization issues and improves our understanding of the LSTM cell. Based on this analysis we propose Recurrent Highway Networks (RHN), which are long not only in time but also in space, generalizing LSTMs to larger step-to-step depths. Experiments indicate that the proposed architecture results in complex but efficient models, beating previous models for character prediction on the Hutter Prize dataset with less than half of the parameters.


Output: Recurrent Highway Networks