Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: McKernel: A Library for Approximate Kernel Expansions in Log-linear Time   McKernel introduces a framework to use kernel approximates in the mini-batch
setting with Stochastic Gradient Descent (SGD) as an alternative to Deep
Learning. Based on Random Kitchen Sinks [Rahimi and Recht 2007], we provide a
C++ library for Large-scale Machine Learning. It contains a CPU optimized
implementation of the algorithm in [Le et al. 2013], that allows the
computation of approximated kernel expansions in log-linear time. The algorithm
requires to compute the product of matrices Walsh Hadamard. A cache friendly
Fast Walsh Hadamard that achieves compelling speed and outperforms current
state-of-the-art methods has been developed. McKernel establishes the
foundation of a new architecture of learning that allows to obtain large-scale
non-linear classification combining lightning kernel expansions and a linear
classifier. It travails in the mini-batch setting working analogously to Neural
Networks. We show the validity of our method through extensive experiments on
MNIST and FASHION MNIST [Xiao et al. 2017].

28
The categories of this paper are: alg-geom
4
The categories of this paper are: cond-mat
37
The categories of this paper are: supr-con
11
The categories of this paper are: hep-lat
32
The categories of this paper are: atom-ph
34
The categories of this paper are: chem-ph
24
The categories of this paper are: dg-ga
20
The categories of this paper are: acc-phys
8
The categories of this paper are: hep-ex
6
The categories of this paper are: astro-ph
30
The categories of this paper are: q-alg
19
The categories of this paper are: eess
25
The categories of this paper are: patt-sol
18
The categories of this paper are: econ
35
The categories of this paper are: mtrl-th
26
The categories of this paper are: bayes-an