2 citations · 4 across the 5 of their papers we have counts for
5 papers
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…
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…
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…
High-Dimensional Distributed Sparse Classification with Scalable Communication-Efficient Global Updates
Fred Lu, Ryan R. Curtin, Edward Raff +2
As the size of datasets used in statistical learning continues to grow, distributed training of models has attracted increasing attention. These methods partition the data and expl…
Optimizing the Optimal Weighted Average: Efficient Distributed Sparse Classification
Fred Lu, Ryan R. Curtin, Edward Raff +2
While distributed training is often viewed as a solution to optimizing linear models on increasingly large datasets, inter-machine communication costs of popular distributed approa…