A model for learning to segment temporal sequences, utilizing a mixture of RNN experts together with adaptive variance

DOI10.1016/J.NEUNET.2008.09.005zbMath1254.68215DBLPjournals/nn/NamikawaT08arXiv0706.1317OpenAlexW2020621358WikidataQ48872888 ScholiaQ48872888MaRDI QIDQ1932118

Jun Tani, Jun Namikawa

Publication date: 17 January 2013

Published in: Neural Networks (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/0706.1317

zbMATH Keywords

maximum likelihood estimationrecurrent neural networkmixture of expertsself-organizationsegmentation of temporal sequences

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Numerical analysis or methods applied to Markov chains (65C40)

Related Items (1)

Learning to imitate stochastic time series in a compositional way by chaos

Uses Software

• LSTM

Cites Work

• Unnamed Item
• Dynamical networks in function dynamics
• Functional dynamics. I: Articulation process
• Learning to imitate stochastic time series in a compositional way by chaos
• Learning Nonregular Languages: A Comparison of Simple Recurrent Networks and LSTM
• Recurrent Neural Networks with Small Weights Implement Definite Memory Machines
• Dynamics and computation in functional shifts
• Chaotic itinerancy
• Functional dynamics. II: Syntactic structure

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