Search Results for author: Michael J. Franklin

Found 11 papers, 1 papers with code

Understanding and Optimizing Packed Neural Network Training for Hyper-Parameter Tuning

no code implementations7 Feb 2020 Rui Liu, Sanjay Krishnan, Aaron J. Elmore, Michael J. Franklin

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.

DLHub: Model and Data Serving for Science

no code implementations27 Nov 2018 Ryan Chard, Zhuozhao Li, Kyle Chard, Logan Ward, Yadu Babuji, Anna Woodard, Steve Tuecke, Ben Blaiszik, Michael J. Franklin, Ian Foster

Here we present the Data and Learning Hub for science (DLHub), a multi-tenant system that provides both model repository and serving capabilities with a focus on science applications.

Distributed Computing

KeystoneML: Optimizing Pipelines for Large-Scale Advanced Analytics

no code implementations29 Oct 2016 Evan R. Sparks, Shivaram Venkataraman, Tomer Kaftan, Michael J. Franklin, Benjamin Recht

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.

General Classification Image Classification

ActiveClean: Interactive Data Cleaning While Learning Convex Loss Models

no code implementations15 Jan 2016 Sanjay Krishnan, Jiannan Wang, Eugene Wu, Michael J. Franklin, Ken Goldberg

Data cleaning is often an important step to ensure that predictive models, such as regression and classification, are not affected by systematic errors such as inconsistent, out-of-date, or outlier data.

Active Learning EEG

MLlib: Machine Learning in Apache Spark

no code implementations26 May 2015 Xiangrui Meng, Joseph Bradley, Burak Yavuz, Evan Sparks, Shivaram Venkataraman, Davies Liu, Jeremy Freeman, DB Tsai, Manish Amde, Sean Owen, Doris Xin, Reynold Xin, Michael J. Franklin, Reza Zadeh, Matei Zaharia, Ameet Talwalkar

Apache Spark is a popular open-source platform for large-scale data processing that is well-suited for iterative machine learning tasks.

TuPAQ: An Efficient Planner for Large-scale Predictive Analytic Queries

no code implementations31 Jan 2015 Evan R. Sparks, Ameet Talwalkar, Michael J. Franklin, Michael. I. Jordan, Tim Kraska

The proliferation of massive datasets combined with the development of sophisticated analytical techniques have enabled a wide variety of novel applications such as improved product recommendations, automatic image tagging, and improved speech-driven interfaces.

MLI: An API for Distributed Machine Learning

no code implementations21 Oct 2013 Evan R. Sparks, Ameet Talwalkar, Virginia Smith, Jey Kottalam, Xinghao Pan, Joseph Gonzalez, Michael J. Franklin, Michael. I. Jordan, Tim Kraska

MLI is an Application Programming Interface designed to address the challenges of building Machine Learn- ing algorithms in a distributed setting based on data-centric computing.

Active Learning for Crowd-Sourced Databases

no code implementations17 Sep 2012 Barzan Mozafari, Purnamrita Sarkar, Michael J. Franklin, Michael. I. Jordan, Samuel Madden

Based on this observation, we present two new active learning algorithms to combine humans and algorithms together in a crowd-sourced database.

Active Learning

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