Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Training Artificial Neural Networks by Generalized Likelihood Ratio
  Method: Exploring Brain-like Learning to Improve Robustness   In this work, we propose a generalized likelihood ratio method capable of
training the artificial neural networks with some biological brain-like
mechanisms,.e.g., (a) learning by the loss value, (b) learning via neurons with
discontinuous activation and loss functions. The traditional back propagation
method cannot train the artificial neural networks with aforementioned
brain-like learning mechanisms. Numerical results show that the robustness of
various artificial neural networks trained by the new method is significantly
improved when the input data is affected by both the natural noises and
adversarial attacks. Code is available:
\url{https://github.com/LX-doctorAI/GLR_ADV} .

36
The categories of this paper are: cmp-lg
0
The categories of this paper are: hep-ph
24
The categories of this paper are: dg-ga
3
The categories of this paper are: physics
30
The categories of this paper are: q-alg
21
The categories of this paper are: adap-org
13
The categories of this paper are: quant-ph
20
The categories of this paper are: acc-phys
2
The categories of this paper are: math
18
The categories of this paper are: econ
11
The categories of this paper are: hep-lat
6
The categories of this paper are: astro-ph
9
The categories of this paper are: nlin
10
The categories of this paper are: q-bio
32
The categories of this paper are: atom-ph
34
The categories of this paper are: chem-ph