• Corpus ID: 219636258

Seq2Tens: An Efficient Representation of Sequences by Low-Rank Tensor Projections

@article{Tth2021Seq2TensAE,
  title={Seq2Tens: An Efficient Representation of Sequences by Low-Rank Tensor Projections},
  author={Csaba T{\'o}th and Patric Bonnier and Harald Oberhauser},
  journal={ArXiv},
  year={2021},
  volume={abs/2006.07027}
}
Sequential data such as time series, video, or text can be challenging to analyse as the ordered structure gives rise to complex dependencies. At the heart of this is non-commutativity, in the sense that reordering the elements of a sequence can completely change its meaning. We use a classical mathematical object -- the tensor algebra -- to capture such dependencies. To address the innate computational complexity of high degree tensors, we use compositions of low-rank tensor projections. This… 

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