Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Tensor-based framework for training flexible neural networks   Activation functions (AFs) are an important part of the design of neural
networks (NNs), and their choice plays a predominant role in the performance of
a NN. In this work, we are particularly interested in the estimation of
flexible activation functions using tensor-based solutions, where the AFs are
expressed as a weighted sum of predefined basis functions. To do so, we propose
a new learning algorithm which solves a constrained coupled matrix-tensor
factorization (CMTF) problem. This technique fuses the first and zeroth order
information of the NN, where the first-order information is contained in a
Jacobian tensor, following a constrained canonical polyadic decomposition
(CPD). The proposed algorithm can handle different decomposition bases. The
goal of this method is to compress large pretrained NN models, by replacing
subnetworks, {\em i.e.,} one or multiple layers of the original network, by a
new flexible layer. The approach is applied to a pretrained convolutional
neural network (CNN) used for character classification.

31
The categories of this paper are: ao-sci
4
The categories of this paper are: cond-mat
10
The categories of this paper are: q-bio
35
The categories of this paper are: mtrl-th
18
The categories of this paper are: econ
13
The categories of this paper are: quant-ph
36
The categories of this paper are: cmp-lg
32
The categories of this paper are: atom-ph
28
The categories of this paper are: alg-geom
20
The categories of this paper are: acc-phys
22
The categories of this paper are: chao-dyn
21
The categories of this paper are: adap-org
19
The categories of this paper are: eess
27
The categories of this paper are: comp-gas
8
The categories of this paper are: hep-ex
37
The categories of this paper are: supr-con