3 papers
cs.DS2025
Intermediate N-Gramming: Deterministic and Fast N-Grams For Large N and Large Datasets
Ryan R. Curtin, Fred Lu, Edward Raff +1
The number of n-gram features grows exponentially in n, making it computationally demanding to compute the most frequent n-grams even for n as small as 3. Motivated by our producti…
cs.CR2025
Zipf-Gramming: Scaling Byte N-Grams Up to Production Sized Malware Corpora
Edward Raff, Ryan R. Curtin, Derek Everett +2
A classifier using byte n-grams as features is the only approach we have found fast enough to meet requirements in size (sub 2 MB), speed (multiple GB/s), and latency (sub 10 ms) f…
cs.DB2024
FaaS and Furious: abstractions and differential caching for efficient data pre-processing
Jacopo Tagliabue, Ryan Curtin, Ciro Greco
Data pre-processing pipelines are the bread and butter of any successful AI project. We introduce a novel programming model for pipelines in a data lakehouse, allowing users to int…