Transcription of TRANSACTIONS ON NEURAL NETWORKS AND LEARNING …
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TRANSACTIONS ON NEURAL NETWORKS AND LEARNING …
TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS1 LSTM: A search space OdysseyKlaus Greff, Rupesh K. Srivastava, Jan Koutn k, Bas R. Steunebrink, J urgen SchmidhuberAbstract Several variants of the Long Short-Term Memory(LSTM) architecture for recurrent NEURAL NETWORKS have beenproposed since its inception in 1995. In recent years, thesenetworks have become the state-of-the-art models for a varietyof machine LEARNING problems. This has led to a renewed interestin understanding the role and utility of various computationalcomponents of typical LSTM variants. In this paper, we presentthe first large-scale analysis of eight LSTM variants on threerepresentative tasks: speech recognition, handwriting recognition,and polyphonic music modeling. The hyperparameters of allLSTM variants for each task were optimized separately usingrandom search , and their importance was assessed using thepowerful fANOVA framework.
LSTM: A Search Space Odyssey Klaus Greff, Rupesh K. Srivastava, Jan Koutn´ık, Bas R. Steunebrink, J urgen Schmidhuber¨ Abstract—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
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