To Index or Not to Index: Optimizing Exact Maximum Inner Product Search

1 code implementation • 5 Jun 2017 • Firas Abuzaid, Geet Sethi, Peter Bailis, Matei Zaharia

The brute-force approach to solving exact MIPS is computationally expensive, thus spurring recent development of novel indexes and pruning techniques for this task.

Recommendation Systems

NoScope: Optimizing Neural Network Queries over Video at Scale

1 code implementation • 7 Mar 2017 • Daniel Kang, John Emmons, Firas Abuzaid, Peter Bailis, Matei Zaharia

Given a target video, object to detect, and reference neural network, NoScope automatically searches for and trains a sequence, or cascade, of models that preserves the accuracy of the reference network but is specialized to the target video and are therefore far less computationally expensive.

Binary Classification

Yggdrasil: An Optimized System for Training Deep Decision Trees at Scale

no code implementations • NeurIPS 2016 • Firas Abuzaid, Joseph K. Bradley, Feynman T. Liang, Andrew Feng, Lee Yang, Matei Zaharia, Ameet S. Talwalkar

Deep distributed decision trees and tree ensembles have grown in importance due to the need to model increasingly large datasets.

Caffe con Troll: Shallow Ideas to Speed Up Deep Learning

1 code implementation • 16 Apr 2015 • Stefan Hadjis, Firas Abuzaid, Ce Zhang, Christopher Ré

We present Caffe con Troll (CcT), a fully compatible end-to-end version of the popular framework Caffe with rebuilt internals.