961 citations · 1.6k across the 32 of their papers we have counts for
33 papers
The Data Station: Combining Data, Compute, and Market Forces
Raul Castro Fernandez, Kyle Chard, Ben Blaiszik +7
This paper introduces Data Stations, a new data architecture that we are designing to tackle some of the most challenging data problems that we face today: access to sensitive data…
Fast and Reliable Missing Data Contingency Analysis with Predicate-Constraints
Xi Liang, Zechao Shang, Aaron J. Elmore +2
Today, data analysts largely rely on intuition to determine whether missing or withheld rows of a dataset significantly affect their analyses. We propose a framework that can produ…
Understanding and Optimizing Packed Neural Network Training for Hyper-Parameter Tuning
Rui Liu, Sanjay Krishnan, Aaron J. Elmore +1
As neural networks are increasingly employed in machine learning practice, how to efficiently share limited training resources among a diverse set of model training tasks becomes a…
Data Market Platforms: Trading Data Assets to Solve Data Problems
Raul Castro Fernandez, Pranav Subramaniam, Michael J. Franklin
Data only generates value for a few organizations with expertise and resources to make data shareable, discoverable, and easy to integrate. Sharing data that is easy to discover an…
Band-limited Training and Inference for Convolutional Neural Networks
Adam Dziedzic, John Paparrizos, Sanjay Krishnan +2
The convolutional layers are core building blocks of neural network architectures. In general, a convolutional filter applies to the entire frequency spectrum of the input data. We…
DLHub: Model and Data Serving for Science
Ryan Chard, Zhuozhao Li, Kyle Chard +7
While the Machine Learning (ML) landscape is evolving rapidly, there has been a relative lag in the development of the "learning systems" needed to enable broad adoption. Furthermo…