Probabilistic Counting with Randomized Storage

By Benjamin Van Durme, Ashwin Lall. 2009

39 citations recorded by the source corpus.

Subfields: Algorithms & Complexity

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Algorithms & Complexity

1. Marc Marone
2. H. Brendan McMahan
3. D. Golovin
4. David Sculley
5. Michael Young
6. Benjamin Van Durme

Citations in the corpus

• Data Portraits: Recording Foundation Model Training Data (2023, methodology): “Talbot and Osborne [34] use Bloom filters to construct language models while others used similar structures to count features [16, 36].”
• Retrospective: Flipping Bits in Memory Without Accessing Them: An Experimental Study of DRAM Disturbance Errors (2023, unknown): “We also analyzed approximate methods which further reduce the storage requirement: Bloom Filters [11], Morris Coun-ters [50], and variants thereof [18, 21, 66].”
• Large-Scale Learning with Less RAM via Randomization (2013, methodology): “Recently, the related technique of randomized counting has enabled compact language models (Van Durme & Lall, 2009).”