Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Revisiting Gaussian Neurons for Online Clustering with Unknown Number of
  Clusters   Despite the recent success of artificial neural networks, more biologically
plausible learning methods may be needed to resolve the weaknesses of
backpropagation trained models such as catastrophic forgetting and adversarial
attacks. A novel local learning rule is presented that performs online
clustering with a maximum limit of the number of cluster to be found rather
than a fixed cluster count. Instead of using orthogonal weight or output
activation constraints, activation sparsity is achieved by mutual repulsion of
lateral Gaussian neurons ensuring that multiple neuron centers cannot occupy
the same location in the input domain. An update method is also presented for
adjusting the widths of the Gaussian neurons in cases where the data samples
can be represented by means and variances. The algorithms were applied on the
MNIST and CIFAR-10 datasets to create filters capturing the input patterns of
pixel patches of various sizes. The experimental results demonstrate stability
in the learned parameters across a large number of training samples.

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The categories of this paper are: cond-mat
30
The categories of this paper are: q-alg
11
The categories of this paper are: hep-lat
37
The categories of this paper are: supr-con
19
The categories of this paper are: eess
36
The categories of this paper are: cmp-lg
27
The categories of this paper are: comp-gas
22
The categories of this paper are: chao-dyn
18
The categories of this paper are: econ
24
The categories of this paper are: dg-ga
31
The categories of this paper are: ao-sci
9
The categories of this paper are: nlin
10
The categories of this paper are: q-bio
20
The categories of this paper are: acc-phys
8
The categories of this paper are: hep-ex
28
The categories of this paper are: alg-geom