Corpus ID: 27819535

Tensorial Recurrent Neural Networks for Longitudinal Data Analysis

@article{Bai2017TensorialRN,
  title={Tensorial Recurrent Neural Networks for Longitudinal Data Analysis},
  author={Mingyuan Bai and Boyan Zhang and Junbin Gao},
  journal={ArXiv},
  year={2017},
  volume={abs/1708.00185}
}
  • Mingyuan Bai, Boyan Zhang, Junbin Gao
  • Published in ArXiv 2017
  • Computer Science, Mathematics
  • Traditional Recurrent Neural Networks assume vectorized data as inputs. However many data from modern science and technology come in certain structures such as tensorial time series data. To apply the recurrent neural networks for this type of data, a vectorisation process is necessary, while such a vectorisation leads to the loss of the precise information of the spatial or longitudinal dimensions. In addition, such a vectorized data is not an optimum solution for learning the representation… CONTINUE READING

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    References

    Publications referenced by this paper.
    SHOWING 1-10 OF 21 REFERENCES

    MULTILINEAR TENSOR REGRESSION FOR LONGITUDINAL RELATIONAL DATA.

    VIEW 3 EXCERPTS
    HIGHLY INFLUENTIAL

    Tensor-Factorized Neural Networks

    VIEW 1 EXCERPT

    Kronecker Recurrent Units

    VIEW 1 EXCERPT

    View Adaptive Recurrent Neural Networks for High Performance Human Action Recognition from Skeleton Data

    VIEW 1 EXCERPT

    Matrix Neural Networks

    VIEW 1 EXCERPT