Zobrazeno 1 - 10
of 463
pro vyhledávání: '"Franklin, Michael J."'
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 produce automatic contingency analysis, i.e., the range of values an aggreg
Externí odkaz:
http://arxiv.org/abs/2004.04139
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 crucial issue. To achieve better utilization of the shared resources,
Externí odkaz:
http://arxiv.org/abs/2002.02885
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 and integrate is hard because data owners lack information (who needs wh
Externí odkaz:
http://arxiv.org/abs/2002.01047
Autor:
Chard, Ryan, Li, Zhuozhao, Chard, Kyle, Ward, Logan, Babuji, Yadu, Woodard, Anna, Tuecke, Steve, Blaiszik, Ben, Franklin, Michael J., Foster, Ian
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. Furthermore, few such systems are designed to support the specialized requireme
Externí odkaz:
http://arxiv.org/abs/1811.11213
Predictive models based on machine learning can be highly sensitive to data error. Training data are often combined with a variety of different sources, each susceptible to different types of inconsistencies, and new data streams during prediction ti
Externí odkaz:
http://arxiv.org/abs/1711.01299
Autor:
Crankshaw, Daniel, Wang, Xin, Zhou, Giulio, Franklin, Michael J., Gonzalez, Joseph E., Stoica, Ion
Machine learning is being deployed in a growing number of applications which demand real-time, accurate, and robust predictions under heavy query load. However, most machine learning frameworks and systems only address model training and not deployme
Externí odkaz:
http://arxiv.org/abs/1612.03079
Autor:
Sparks, Evan R., Venkataraman, Shivaram, Kaftan, Tomer, Franklin, Michael J., Recht, Benjamin
Modern advanced analytics applications make use of machine learning techniques and contain multiple steps of domain-specific and general-purpose processing with high resource requirements. We present KeystoneML, a system that captures and optimizes t
Externí odkaz:
http://arxiv.org/abs/1610.09451
Autor:
Belletti, Francois W., Sparks, Evan R., Franklin, Michael J., Bayen, Alexandre M., Gonzalez, Joseph E.
Linear causal analysis is central to a wide range of important application spanning finance, the physical sciences, and engineering. Much of the existing literature in linear causal analysis operates in the time domain. Unfortunately, the direct appl
Externí odkaz:
http://arxiv.org/abs/1603.03336