Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Kernel-Based Approaches for Sequence Modeling: Connections to Neural
  Methods   We investigate time-dependent data analysis from the perspective of recurrent
kernel machines, from which models with hidden units and gated memory cells
arise naturally. By considering dynamic gating of the memory cell, a model
closely related to the long short-term memory (LSTM) recurrent neural network
is derived. Extending this setup to $n$-gram filters, the convolutional neural
network (CNN), Gated CNN, and recurrent additive network (RAN) are also
recovered as special cases. Our analysis provides a new perspective on the
LSTM, while also extending it to $n$-gram convolutional filters. Experiments
are performed on natural language processing tasks and on analysis of local
field potentials (neuroscience). We demonstrate that the variants we derive
from kernels perform on par or even better than traditional neural methods. For
the neuroscience application, the new models demonstrate significant
improvements relative to the prior state of the art.

9
The categories of this paper are: nlin
23
The categories of this paper are: solv-int
26
The categories of this paper are: bayes-an
36
The categories of this paper are: cmp-lg
6
The categories of this paper are: astro-ph
8
The categories of this paper are: hep-ex
18
The categories of this paper are: econ
33
The categories of this paper are: plasm-ph
31
The categories of this paper are: ao-sci
25
The categories of this paper are: patt-sol
3
The categories of this paper are: physics
28
The categories of this paper are: alg-geom
0
The categories of this paper are: hep-ph
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