Arjun Mishra

EECS Department, University of California, Berkeley

Technical Report No. UCB/EECS-2021-190

August 13, 2021

http://www2.eecs.berkeley.edu/Pubs/TechRpts/2021/EECS-2021-190.pdf

In recent years, the use of machine learning for solving complex problems has spread like wildfire. Specifically, machine learning has proved to be very effective in generating embeddings, both for tasks related to simple words/images and for those involving complex data arising in the domains of biology and chemistry. Inspired by these breakthroughs, we look at the problem of generating embeddings from an underlying dataset of circuits and prove their utility on several posterior tasks.

Advisors: Vladimir Stojanovic

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BibTeX citation:

@mastersthesis{Mishra:EECS-2021-190,
    Author= {Mishra, Arjun},
    Title= {Characterizing Circuits with Deep Embeddings},
    School= {EECS Department, University of California, Berkeley},
    Year= {2021},
    Month= {Aug},
    Url= {http://www2.eecs.berkeley.edu/Pubs/TechRpts/2021/EECS-2021-190.html},
    Number= {UCB/EECS-2021-190},
    Abstract= {In recent years, the use of machine learning for solving complex problems has spread like wildfire. Specifically, machine learning has proved to be very effective in generating embeddings, both for tasks related to simple words/images and for those involving complex data arising in the domains of biology and chemistry. Inspired by these breakthroughs, we look at the problem of generating embeddings from an underlying dataset of circuits and prove their utility on several posterior tasks.},
}

EndNote citation:

%0 Thesis
%A Mishra, Arjun 
%T Characterizing Circuits with Deep Embeddings
%I EECS Department, University of California, Berkeley
%D 2021
%8 August 13
%@ UCB/EECS-2021-190
%U http://www2.eecs.berkeley.edu/Pubs/TechRpts/2021/EECS-2021-190.html
%F Mishra:EECS-2021-190