Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Reliable uncertainty estimate for antibiotic resistance classification
  with Stochastic Gradient Langevin Dynamics   Antibiotic resistance monitoring is of paramount importance in the face of
this on-going global epidemic. Deep learning models trained with traditional
optimization algorithms (e.g. Adam, SGD) provide poor posterior estimates when
tested against out-of-distribution (OoD) antibiotic resistant/non-resistant
genes. In this paper, we introduce a deep learning model trained with
Stochastic Gradient Langevin Dynamics (SGLD) to classify antibiotic resistant
genes. The model provides better uncertainty estimates when tested against OoD
data compared to traditional optimization methods such as Adam.

18
The categories of this paper are: econ
2
The categories of this paper are: math
6
The categories of this paper are: astro-ph
7
The categories of this paper are: hep-th
20
The categories of this paper are: acc-phys
25
The categories of this paper are: patt-sol
37
The categories of this paper are: supr-con
34
The categories of this paper are: chem-ph
5
The categories of this paper are: gr-qc
27
The categories of this paper are: comp-gas
21
The categories of this paper are: adap-org
8
The categories of this paper are: hep-ex
3
The categories of this paper are: physics
17
The categories of this paper are: q-fin
13
The categories of this paper are: quant-ph
4
The categories of this paper are: cond-mat