Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Foothill: A Quasiconvex Regularization for Edge Computing of Deep Neural
  Networks   Deep neural networks (DNNs) have demonstrated success for many supervised
learning tasks, ranging from voice recognition, object detection, to image
classification. However, their increasing complexity might yield poor
generalization error that make them hard to be deployed on edge devices.
Quantization is an effective approach to compress DNNs in order to meet these
constraints. Using a quasiconvex base function in order to construct a binary
quantizer helps training binary neural networks (BNNs) and adding noise to the
input data or using a concrete regularization function helps to improve
generalization error. Here we introduce foothill function, an infinitely
differentiable quasiconvex function. This regularizer is flexible enough to
deform towards $L_1$ and $L_2$ penalties. Foothill can be used as a binary
quantizer, as a regularizer, or as a loss. In particular, we show this
regularizer reduces the accuracy gap between BNNs and their full-precision
counterpart for image classification on ImageNet.

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The categories of this paper are: hep-ph
28
The categories of this paper are: alg-geom
8
The categories of this paper are: hep-ex
15
The categories of this paper are: nucl-ex
33
The categories of this paper are: plasm-ph
17
The categories of this paper are: q-fin
4
The categories of this paper are: cond-mat
26
The categories of this paper are: bayes-an
20
The categories of this paper are: acc-phys
10
The categories of this paper are: q-bio
12
The categories of this paper are: nucl-th
34
The categories of this paper are: chem-ph
37
The categories of this paper are: supr-con
22
The categories of this paper are: chao-dyn
30
The categories of this paper are: q-alg
23
The categories of this paper are: solv-int