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Adaptive Processing of Sequences and Data Structures

Overview of attention for book
Adaptive Processing of Sequences and Data Structures
Springer Berlin Heidelberg

Table of Contents

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    Book Overview
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    Chapter 1 Recurrent neural network architectures: An overview
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    Chapter 2 Gradient based learning methods
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    Chapter 3 Diagrammatic methods for deriving and relating temporal neural network algorithms
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    Chapter 4 An introduction to learning structured information
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    Chapter 5 Neural networks for processing data structures
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    Chapter 6 The loading problem: Topics in complexity
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    Chapter 8 Probabilistic models of neuronal spike trains
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    Chapter 9 Temporal models in blind source separation
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    Chapter 10 Recursive neural networks and automata
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    Chapter 11 The neural network pushdown automaton: Architecture, dynamics and training
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    Chapter 12 Neural dynamics with stochasticity
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    Chapter 13 Parsing the stream of time: The value of event-based segmentation in a complex real-world control problem
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    Chapter 14 Hybrid HMM/ANN systems for speech recognition: Overview and new research directions
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    Chapter 15 Predictive models for sequence modelling, application to speech and character recognition
Attention for Chapter 8: Probabilistic models of neuronal spike trains
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