Nonlinear Dynamic Boltzmann Machines for Time-Series Prediction

@inproceedings{Dasgupta2017NonlinearDB,
  title={Nonlinear Dynamic Boltzmann Machines for Time-Series Prediction},
  author={Sakyasingha Dasgupta and Takayuki Osogami},
  booktitle={AAAI},
  year={2017}
}
The dynamic Boltzmann machine (DyBM) has been proposed as a stochastic generative model of multi-dimensional time series, with an exact, learning rule that maximizes the log-likelihood of a given time series. The DyBM, however, is defined only for binary valued data, without any nonlinear hidden units. Here, in our first contribution, we extend the DyBM to deal with real valued data. We present a formulation called Gaussian DyBM, that can be seen as an extension of a vector autoregressive (VAR… CONTINUE READING
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References

Publications referenced by this paper.
Showing 1-10 of 26 references

Nonlinear dynamic boltzmann machines for time-series prediction

  • S. Dasgupta, T. Osogami
  • Technical Report RT0975, IBM Research. http://ibm…
  • 2016
Highly Influential
6 Excerpts

Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude. COURSERA: Neural Networks for Machine Learning

  • T. Tieleman, G. Hinton
  • 2012
Highly Influential
8 Excerpts

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