Han Qi and Jingqiu Liu and Xuan Zou and Allen Tang

EECS Department, University of California, Berkeley

Technical Report No. UCB/EECS-2017-99

May 12, 2017

http://www2.eecs.berkeley.edu/Pubs/TechRpts/2017/EECS-2017-99.pdf

Artificial Intelligence is a thriving field with many applications, whether in automating routinary human labors or support basic research such as diagnosing diseases (Goodfellow et. al., 2016). Deep learning, or machine learning with deep neural networks, is particularly successful in providing amazing human like intelligence, such as image captioning or translation, because it has a more general model of the world and makes relatively few assumptions on the world it trying to model (Goodfellow et. al., 2016). However, comparing to the tools for programming and debugging, the tooling for building and learning deep learning models is not as good.

In this report, we present BIDViz, a visualization platform that allows data scientists to visualize and debug his/her model interatively while it is training. BIDViz is applicable to general machine learning but is oriented toward deep neural models, which are often challenging to fully understand. BIDViz emphasizes dynamic exploration of models by allowing the execution of arbitrary metrics or commands on the model while it is training.

Advisors: John F. Canny

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BibTeX citation:

@mastersthesis{Qi:EECS-2017-99,
    Author= {Qi, Han and Liu, Jingqiu and Zou, Xuan and Tang, Allen},
    Editor= {Canny, John F.},
    Title= {BIDViz: Real-time Monitoring and Debugging of Machine Learning Training Processes},
    School= {EECS Department, University of California, Berkeley},
    Year= {2017},
    Month= {May},
    Url= {http://www2.eecs.berkeley.edu/Pubs/TechRpts/2017/EECS-2017-99.html},
    Number= {UCB/EECS-2017-99},
    Abstract= {Artificial Intelligence is a thriving field with many applications, whether in automating routinary human labors or support basic research such as diagnosing diseases (Goodfellow et. al., 2016). Deep learning, or machine learning with deep neural networks, is particularly successful in providing amazing human like intelligence, such as image captioning or translation, because it has a more general model of the world and makes relatively few assumptions on the world it trying to model (Goodfellow et. al., 2016). However, comparing to the tools for programming and debugging, the tooling for building and learning deep learning models is not as good.

In this report, we present BIDViz, a visualization platform that allows data scientists to visualize and debug his/her model interatively while it is training. BIDViz is applicable to general machine learning but is oriented toward deep neural models, which are often challenging to fully understand. BIDViz emphasizes dynamic exploration of models by allowing the execution of arbitrary metrics or commands on the model while it is training.},
}

EndNote citation:

%0 Thesis
%A Qi, Han 
%A Liu, Jingqiu 
%A Zou, Xuan 
%A Tang, Allen 
%E Canny, John F. 
%T BIDViz: Real-time Monitoring and Debugging of Machine Learning Training Processes
%I EECS Department, University of California, Berkeley
%D 2017
%8 May 12
%@ UCB/EECS-2017-99
%U http://www2.eecs.berkeley.edu/Pubs/TechRpts/2017/EECS-2017-99.html
%F Qi:EECS-2017-99