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Title: A Comparison of Neuromorphic Classification Tasks

Abstract

A variety of neural network models and machine learning techniques have arisen over the past decade, and their successes with image classification have been stunning. With other classification tasks, selecting and configuring a neural network solution is not straightforward. In this paper, we evaluate and compare a variety of neural network models, trained by a variety of machine learning techniques, on a variety of classification tasks. While Deep Learning typically exhibits the best classification accuracy, we note the promise of Reservoir Computing, and evolutionary optimization on spiking neural networks. In many cases, these technologies perform as well as, or better than Deep Learning, and the resulting networks are much smaller than their Deep Learning counterparts.

Authors:
 [1];  [1]; ORCiD logo [2];  [1];  [1];  [1];  [1]
  1. University of Tennessee (UT)
  2. ORNL
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1462845
DOE Contract Number:  
AC05-00OR22725
Resource Type:
Conference
Resource Relation:
Conference: International Conference on Neuromorphic Systems (ICONS 2018) - Knoxville, Tennessee, United States of America - 7/23/2018 8:00:00 AM-7/26/2018 8:00:00 AM
Country of Publication:
United States
Language:
English

Citation Formats

Reynolds, John, Plank, James, Schuman, Catherine D., Bruer, Grant, Disney, Adam, Dean, Mark, and Rose, Garrett. A Comparison of Neuromorphic Classification Tasks. United States: N. p., 2018. Web. doi:10.1145/3229884.3229896.
Reynolds, John, Plank, James, Schuman, Catherine D., Bruer, Grant, Disney, Adam, Dean, Mark, & Rose, Garrett. A Comparison of Neuromorphic Classification Tasks. United States. doi:10.1145/3229884.3229896.
Reynolds, John, Plank, James, Schuman, Catherine D., Bruer, Grant, Disney, Adam, Dean, Mark, and Rose, Garrett. Sun . "A Comparison of Neuromorphic Classification Tasks". United States. doi:10.1145/3229884.3229896. https://www.osti.gov/servlets/purl/1462845.
@article{osti_1462845,
title = {A Comparison of Neuromorphic Classification Tasks},
author = {Reynolds, John and Plank, James and Schuman, Catherine D. and Bruer, Grant and Disney, Adam and Dean, Mark and Rose, Garrett},
abstractNote = {A variety of neural network models and machine learning techniques have arisen over the past decade, and their successes with image classification have been stunning. With other classification tasks, selecting and configuring a neural network solution is not straightforward. In this paper, we evaluate and compare a variety of neural network models, trained by a variety of machine learning techniques, on a variety of classification tasks. While Deep Learning typically exhibits the best classification accuracy, we note the promise of Reservoir Computing, and evolutionary optimization on spiking neural networks. In many cases, these technologies perform as well as, or better than Deep Learning, and the resulting networks are much smaller than their Deep Learning counterparts.},
doi = {10.1145/3229884.3229896},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2018},
month = {7}
}

Conference:
Other availability
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