2.5k citations · 3.6k across the 7 of their papers we have counts for
7 papers
Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour
Priya Goyal, Piotr Dollár, Ross Girshick +6
Deep learning thrives with large neural networks and large datasets. However, larger networks and larger datasets result in longer training times that impede research and developme…
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…
GraphChi-DB: Simple Design for a Scalable Graph Database System -- on Just a PC
Aapo Kyrola, Carlos Guestrin
We propose a new data structure, Parallel Adjacency Lists (PAL), for efficiently managing graphs with billions of edges on disk. The PAL structure is based on the graph storage mod…
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…
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…
Parallel Coordinate Descent for L1-Regularized Loss Minimization
Joseph K. Bradley, Aapo Kyrola, Danny Bickson +1
We propose Shotgun, a parallel coordinate descent algorithm for minimizing L1-regularized losses. Though coordinate descent seems inherently sequential, we prove convergence bounds…