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      Contradiction Detection with Contradiction-Specific Word Embedding

      , ,
      Algorithms
      MDPI AG

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          LSTM: A Search Space Odyssey

          Several variants of the long short-term memory (LSTM) architecture for recurrent neural networks have been proposed since its inception in 1995. In recent years, these networks have become the state-of-the-art models for a variety of machine learning problems. This has led to a renewed interest in understanding the role and utility of various computational components of typical LSTM variants. In this paper, we present the first large-scale analysis of eight LSTM variants on three representative tasks: speech recognition, handwriting recognition, and polyphonic music modeling. The hyperparameters of all LSTM variants for each task were optimized separately using random search, and their importance was assessed using the powerful functional ANalysis Of VAriance framework. In total, we summarize the results of 5400 experimental runs ( ≈ 15 years of CPU time), which makes our study the largest of its kind on LSTM networks. Our results show that none of the variants can improve upon the standard LSTM architecture significantly, and demonstrate the forget gate and the output activation function to be its most critical components. We further observe that the studied hyperparameters are virtually independent and derive guidelines for their efficient adjustment.
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            Aspect extraction for opinion mining with a deep convolutional neural network

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              Recognizing textual entailment: Rational, evaluation and approaches – Erratum

              Due to publisher error, this article was omitted from the printed issue of Natural Language Engineering volume 15 issue 4. It is published online in the correct volume ( journals.cambridge.org/nle ) and also printed here in volume 16 issue 1. Sincere apologies are extended to the authors for this error.
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                Author and article information

                Journal
                ALGOCH
                Algorithms
                Algorithms
                MDPI AG
                1999-4893
                June 2017
                May 24 2017
                : 10
                : 2
                : 59
                Article
                10.3390/a10020059
                4b1917fe-6b97-4363-a337-f8d5f5eba2ed
                © 2017

                https://creativecommons.org/licenses/by/4.0/

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