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Configurable Parallel Induction Machines

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Augmented Cognition (HCII 2021)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12776))

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Abstract

Machine Learning practice in general offers significant opportunities for parallel computing and practicing sound software engineering. More often than not, practitioners routinely write dataset specific scripts and learners focus on model building and refining. Focusing on particular models is not consistent with NFL, a fundamental theorem in Machine Learning. Not minding time-honored software engineering principles is inefficient. In this paper, we present our implementation of MISD machine, consistent with No Free Lunch Theorem, problems we encountered and our approach to solve those problems.

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Acknowledgements

IBM Power Systems Academic Initiative IBM PSAI for their generous support for all my courses.

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Authors and Affiliations

  1. Hunter College, 695 Park Avenue, New York, NY, 10065, USA

    Karina Ionkina & Monte Hancock

  2. Tandon School of Engineering, NYU, Brooklyn, NY, 11201, USA

    Monte Hancock & Raman Kannan

Authors
  1. Karina Ionkina
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  2. Monte Hancock
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  3. Raman Kannan
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Corresponding author

Correspondence to Raman Kannan .

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Editors and Affiliations

  1. Soar Technology Inc., Orlando, FL, USA

    Dylan D. Schmorrow

  2. Design Interactive, Inc., Orlando, FL, USA

    Cali M. Fidopiastis

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Ionkina, K., Hancock, M., Kannan, R. (2021). Configurable Parallel Induction Machines. In: Schmorrow, D.D., Fidopiastis, C.M. (eds) Augmented Cognition. HCII 2021. Lecture Notes in Computer Science(), vol 12776. Springer, Cham. https://doi.org/10.1007/978-3-030-78114-9_28

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  • DOI: https://doi.org/10.1007/978-3-030-78114-9_28

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-78113-2

  • Online ISBN: 978-3-030-78114-9

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