Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Deep Unsupervised Feature Learning Spiking Neural Network with
  Binarized Classification Layers for EMNIST Classification using SpykeFlow   End user AI is trained on large server farms with data collected from the
users. With ever increasing demand for IOT devices, there is a need for deep
learning approaches that can be implemented (at the edge) in an energy
efficient manner. In this work we approach this using spiking neural networks.
The unsupervised learning technique of spike timing dependent plasticity (STDP)
using binary activations are used to extract features from spiking input data.
Gradient descent (backpropagation) is used only on the output layer to perform
the training for classification. The accuracies obtained for the balanced
EMNIST data set compare favorably with other approaches. The effect of
stochastic gradient descent (SGD) approximations on learning capabilities of
our network are also explored.

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The categories of this paper are: atom-ph
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The categories of this paper are: bayes-an
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The categories of this paper are: chem-ph
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The categories of this paper are: hep-ph
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The categories of this paper are: hep-lat
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The categories of this paper are: stat
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The categories of this paper are: ao-sci
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The categories of this paper are: math-ph
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The categories of this paper are: chao-dyn
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The categories of this paper are: mtrl-th
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The categories of this paper are: q-fin
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The categories of this paper are: hep-ex
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The categories of this paper are: nucl-th
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The categories of this paper are: nucl-ex
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The categories of this paper are: patt-sol
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The categories of this paper are: econ