ML Anthology

Impossibility Results for Grammar-Compressed Linear Algebra

Amir Abboud, Arturs Backurs, Karl Bringmann, Marvin Künnemann
NeurIPS 2020
/neurips/2020/abboud2020neurips-impossibility/

Abstract

To handle vast amounts of data, it is natural and popular to compress vectors and matrices. When we compress a vector from size N down to size n << N, it certainly makes it easier to store and transmit efficiently, but does it also make it easier to process?

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Cite

Text

Abboud et al. "Impossibility Results for Grammar-Compressed Linear Algebra." Neural Information Processing Systems, 2020.

Markdown

[Abboud et al. "Impossibility Results for Grammar-Compressed Linear Algebra." Neural Information Processing Systems, 2020.](https://mlanthology.org/neurips/2020/abboud2020neurips-impossibility/)

BibTeX

@inproceedings{abboud2020neurips-impossibility,
  title     = {{Impossibility Results for Grammar-Compressed Linear Algebra}},
  author    = {Abboud, Amir and Backurs, Arturs and Bringmann, Karl and Künnemann, Marvin},
  booktitle = {Neural Information Processing Systems},
  year      = {2020},
  url       = {https://mlanthology.org/neurips/2020/abboud2020neurips-impossibility/}
}