Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Scalable Multi-Class Bayesian Support Vector Machines for Structured and
  Unstructured Data   We introduce a new Bayesian multi-class support vector machine by formulating
a pseudo-likelihood for a multi-class hinge loss in the form of a
location-scale mixture of Gaussians. We derive a variational-inference-based
training objective for gradient-based learning. Additionally, we employ an
inducing point approximation which scales inference to large data sets.
Furthermore, we develop hybrid Bayesian neural networks that combine standard
deep learning components with the proposed model to enable learning for
unstructured data. We provide empirical evidence that our model outperforms the
competitor methods with respect to both training time and accuracy in
classification experiments on 68 structured and two unstructured data sets.
Finally, we highlight the key capability of our model in yielding prediction
uncertainty for classification by demonstrating its effectiveness in the tasks
of large-scale active learning and detection of adversarial images.

36
The categories of this paper are: cmp-lg
13
The categories of this paper are: quant-ph
17
The categories of this paper are: q-fin
4
The categories of this paper are: cond-mat
35
The categories of this paper are: mtrl-th
0
The categories of this paper are: hep-ph
28
The categories of this paper are: alg-geom
5
The categories of this paper are: gr-qc
33
The categories of this paper are: plasm-ph
20
The categories of this paper are: acc-phys
37
The categories of this paper are: supr-con
18
The categories of this paper are: econ
2
The categories of this paper are: math
10
The categories of this paper are: q-bio
34
The categories of this paper are: chem-ph
8
The categories of this paper are: hep-ex