Probabilistic Counting with Randomized Storage

Van Durme, Benjamin; Lall, Ashwin

Probabilistic Counting with Randomized Storage

Download BibTeX Cite

Proceedings of the Twenty-First International Joint Conference on Artificial Intelligence. Pages 1574-1578.

Abstract

Previous work by Talbot and Osborne [2007] explored the use of randomized storage mechanisms in language modeling. These structures trade a small amount of error for significant space savings, enabling the use of larger language models on relatively modest hardware. Going beyond space efficient count storage, here we present the Talbot Osborne Morris Bloom (TOMB) Counter, an extended model for performing space efficient counting over streams of finite length. Theoretical and experimental results are given, showing the promise of approximate counting over large vocabularies in the context of limited space.Benjamin Van Durme, Ashwin Lall

Benjamin Van Durme, and Ashwin Lall. 2009. Probabilistic Counting with Randomized Storage. In Proceedings of the Twenty-First International Joint Conference on Artificial Intelligence, IJCAI-09. IJCAI, 1574–1578. https://ijcai.org/proceedings/2009/263

@inproceedings{ijcai2009p263,
  title     = {Probabilistic Counting with Randomized Storage},
  author    = {Van Durme, Benjamin and Lall, Ashwin},
  booktitle = {Proceedings of the Twenty-First International Joint Conference on
               Artificial Intelligence, {IJCAI-09}},
  publisher = {International Joint Conferences on Artificial Intelligence},
  editor    = {Hiroaki Kitano},
  pages     = {1574--1578},
  year      = {2009},
  url       = {https://ijcai.org/proceedings/2009/263},
}

TY  - CPAPER
TI  - Probabilistic Counting with Randomized Storage
AU  - Van Durme, Benjamin
AU  - Lall, Ashwin
T2  - Proceedings of the Twenty-First International Joint Conference on Artificial Intelligence, IJCAI-09
PB  - International Joint Conferences on Artificial Intelligence
PY  - 2009
SP  - 1574
EP  - 1578
UR  - https://ijcai.org/proceedings/2009/263
ER  -