NeurIPS 2020poster15 citations
Impossibility Results for Grammar-Compressed Linear Algebra
Amir Abboud, Arturs Backurs, Karl Bringmann, Marvin Künnemann
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?
BibTeX
@inproceedings{NEURIPS2020_645e6bfd,
author = {Abboud, Amir and Backurs, Arturs and Bringmann, Karl and K\"{u}nnemann, Marvin},
booktitle = {Advances in Neural Information Processing Systems},
editor = {H. Larochelle and M. Ranzato and R. Hadsell and M.F. Balcan and H. Lin},
pages = {8810--8823},
publisher = {Curran Associates, Inc.},
title = {Impossibility Results for Grammar-Compressed Linear Algebra},
url = {https://proceedings.neurips.cc/paper_files/paper/2020/file/645e6bfdd05d1a69c5e47b20f0a91d46-Paper.pdf},
volume = {33},
year = {2020}
}