activity
20082016
most citedGraphLab: A New Framework For Parallel Machine Learning

440 citations · 1.4k across the 24 of their papers we have counts for

collaborators

24 papers

cs.NI2016★ 3 cited

What is LTE actually used for? An answer through multi-operator, crowd-sourced measurement

Francesco Malandrino, Scott Kirkpatrick, Danny Bickson

LTE networks are commonplace nowadays; however, comparatively little is known about where (and why) they are deployed, and the demand they serve. We shed some light on these issues…

cs.LG2014★ 440 cited

GraphLab: A New Framework For Parallel Machine Learning

Yucheng Low, Joseph E. Gonzalez, Aapo Kyrola +3

Designing and implementing efficient, provably correct parallel machine learning (ML) algorithms is challenging. Existing high-level parallel abstractions like MapReduce are insuff…

cs.DB2012★ 92 cited

Distributed GraphLab: A Framework for Machine Learning in the Cloud

Yucheng Low, Joseph Gonzalez, Aapo Kyrola +3

While high-level data parallel frameworks, like MapReduce, simplify the design and implementation of large-scale data processing systems, they do not naturally or efficiently suppo…

cs.LG2011★ 9 cited

Efficient Multicore Collaborative Filtering

Yao Wu, Qiang Yan, Danny Bickson +2

This paper describes the solution method taken by LeBuSiShu team for track1 in ACM KDD CUP 2011 contest (resulting in the 5th place). We identified two main challenges: the unique…

cs.LG2011★ 21 cited

GraphLab: A Distributed Framework for Machine Learning in the Cloud

Yucheng Low, Joseph Gonzalez, Aapo Kyrola +2

Machine Learning (ML) techniques are indispensable in a wide range of fields. Unfortunately, the exponential increase of dataset sizes are rapidly extending the runtime of sequenti…

cs.LG2011★ 65 cited

Kernel Belief Propagation

Le Song, Arthur Gretton, Danny Bickson +2

We propose a nonparametric generalization of belief propagation, Kernel Belief Propagation (KBP), for pairwise Markov random fields. Messages are represented as functions in a repr…