Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Improving Sample Efficiency with Normalized RBF Kernels   In deep learning models, learning more with less data is becoming more
important. This paper explores how neural networks with normalized Radial Basis
Function (RBF) kernels can be trained to achieve better sample efficiency.
Moreover, we show how this kind of output layer can find embedding spaces where
the classes are compact and well-separated. In order to achieve this, we
propose a two-phase method to train those type of neural networks on
classification tasks. Experiments on CIFAR-10 and CIFAR-100 show that networks
with normalized kernels as output layer can achieve higher sample efficiency,
high compactness and well-separability through the presented method in
comparison to networks with SoftMax output layer.

7
The categories of this paper are: hep-th
0
The categories of this paper are: hep-ph
27
The categories of this paper are: comp-gas
8
The categories of this paper are: hep-ex
36
The categories of this paper are: cmp-lg
17
The categories of this paper are: q-fin
34
The categories of this paper are: chem-ph
13
The categories of this paper are: quant-ph
15
The categories of this paper are: nucl-ex
9
The categories of this paper are: nlin
4
The categories of this paper are: cond-mat
29
The categories of this paper are: funct-an
25
The categories of this paper are: patt-sol
5
The categories of this paper are: gr-qc
23
The categories of this paper are: solv-int
3
The categories of this paper are: physics