Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Bayesian Learning from Sequential Data using Gaussian Processes with
  Signature Covariances   We develop a Bayesian approach to learning from sequential data by using
Gaussian processes (GPs) with so-called signature kernels as covariance
functions. This allows to make sequences of different length comparable and to
rely on strong theoretical results from stochastic analysis. Signatures capture
sequential structure with tensors that can scale unfavourably in sequence
length and state space dimension. To deal with this, we introduce a sparse
variational approach with inducing tensors. We then combine the resulting GP
with LSTMs and GRUs to build larger models that leverage the strengths of each
of these approaches and benchmark the resulting GPs on multivariate time series
(TS) classification datasets. Code available at
https://github.com/tgcsaba/GPSig.

22
The categories of this paper are: chao-dyn
11
The categories of this paper are: hep-lat
29
The categories of this paper are: funct-an
24
The categories of this paper are: dg-ga
15
The categories of this paper are: nucl-ex
3
The categories of this paper are: physics
33
The categories of this paper are: plasm-ph
34
The categories of this paper are: chem-ph
28
The categories of this paper are: alg-geom
35
The categories of this paper are: mtrl-th
0
The categories of this paper are: hep-ph
8
The categories of this paper are: hep-ex
7
The categories of this paper are: hep-th
27
The categories of this paper are: comp-gas
20
The categories of this paper are: acc-phys
31
The categories of this paper are: ao-sci