Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deep Learning using Linear Support Vector Machines   Recently, fully-connected and convolutional neural networks have been trained
to achieve state-of-the-art performance on a wide variety of tasks such as
speech recognition, image classification, natural language processing, and
bioinformatics. For classification tasks, most of these "deep learning" models
employ the softmax activation function for prediction and minimize
cross-entropy loss. In this paper, we demonstrate a small but consistent
advantage of replacing the softmax layer with a linear support vector machine.
Learning minimizes a margin-based loss instead of the cross-entropy loss. While
there have been various combinations of neural nets and SVMs in prior art, our
results using L2-SVMs show that by simply replacing softmax with linear SVMs
gives significant gains on popular deep learning datasets MNIST, CIFAR-10, and
the ICML 2013 Representation Learning Workshop's face expression recognition
challenge.

12
The categories of this paper are: nucl-th
10
The categories of this paper are: q-bio
8
The categories of this paper are: hep-ex
31
The categories of this paper are: ao-sci
28
The categories of this paper are: alg-geom
37
The categories of this paper are: supr-con
17
The categories of this paper are: q-fin
9
The categories of this paper are: nlin
33
The categories of this paper are: plasm-ph
5
The categories of this paper are: gr-qc
29
The categories of this paper are: funct-an
11
The categories of this paper are: hep-lat
35
The categories of this paper are: mtrl-th
24
The categories of this paper are: dg-ga
0
The categories of this paper are: hep-ph
27
The categories of this paper are: comp-gas