139112 - Sequence classification restricted Boltzmann machines with gated units.pdf (2.38 MB)
Sequence classification restricted Boltzmann machines with gated units
journal contribution
posted on 2023-05-20, 14:32 authored by Son TranSon Tran, d'Avila Garcez, A, Weyde, T, Yin, J, Zhang, Q, Karunanithi, MFor the classification of sequential data, dynamic Bayesian networks and recurrent neural networks (RNNs) are the preferred models. While the former can explicitly model the temporal dependences between the variables, and the latter have the capability of learning representations. The recurrent temporal restricted Boltzmann machine (RTRBM) is a model that combines these two features. However, learning and inference in RTRBMs can be difficult because of the exponential nature of its gradient computations when maximizing log likelihoods. In this article, first, we address this intractability by optimizing a conditional rather than a joint probability distribution when performing sequence classification. This results in the “sequence classification restricted Boltzmann machine” (SCRBM). Second, we introduce gated SCRBMs (gSCRBMs), which use an information processing gate, as an integration of SCRBMs with long short-term memory (LSTM) models. In the experiments reported in this article, we evaluate the proposed models on optical character recognition, chunking, and multiresident activity recognition in smart homes. The experimental results show that gSCRBMs achieve the performance comparable to that of the state of the art in all three tasks. gSCRBMs require far fewer parameters in comparison with other recurrent networks with memory gates, in particular, LSTMs and gated recurrent units (GRUs).
History
Publication title
IEEE Transactions on Neural Networks and Learning SystemsPagination
1-10ISSN
2162-237XDepartment/School
School of Information and Communication TechnologyPublisher
Institute of Electrical and Electronics EngineersPlace of publication
United StatesRights statement
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- Open