Transcription of Supervised Sequence Labelling with Recurrent Neural Networks
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Supervised Sequence Labelling with Recurrent Neural Networks
Technische Universit at M unchenFakult at f ur InformatikLehrstuhl VI: Echtzeitsysteme und RobotikSupervised Sequence Labellingwith Recurrent Neural NetworksAlex GravesVollst andiger Abdruck der von der Fakult at f ur Informatik der TechnischenUniversit at M unchen zur Erlangung des akademischen Grades einesDoktors der Naturwissenschaften (Dr. rer. nat.)genehmigten Neural Networks are powerful Sequence learners. They are ableto incorporate context information in a flexible way, and are robust to lo-calised distortions of the input data. These properties make them well suitedto Sequence Labelling , where input sequences are transcribed with streams short-term memoryis an especially promising Recurrent archi-tecture, able to bridge long time delays between relevant input and outputevents, and thereby access long range context.
ing and bioinformatics. For this reason, LSTM will be the RNN of choice 1. CHAPTER 1. INTRODUCTION 2 throughout the thesis, and a major subgoal is to analyse, apply and extend the LSTM architecture. 1.1 Contributions In the original formulation of LSTM (Hochreiter and Schmidhuber, 1997)
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