Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Tensor-Train Networks for Learning Predictive Modeling of
  Multidimensional Data   In this work, we firstly apply the Train-Tensor (TT) networks to construct a
compact representation of the classical Multilayer Perceptron, representing a
reduction of up to 95% of the coefficients. A comparative analysis between
tensor model and standard multilayer neural networks is also carried out in the
context of prediction of the Mackey-Glass noisy chaotic time series and NASDAQ
index. We show that the weights of a multidimensional regression model can be
learned by means of TT network and the optimization of TT weights is a more
robust to the impact of coefficient initialization and hyper-parameter setting.
Furthermore, an efficient algorithm based on alternating least squares has been
proposed for approximating the weights in TT-format with a reduction of
computational calculus, providing a much faster convergence than the well-known
adaptive learning-method algorithms, widely applied for optimizing neural
networks.

24
The categories of this paper are: dg-ga
33
The categories of this paper are: plasm-ph
18
The categories of this paper are: econ
14
The categories of this paper are: math-ph
28
The categories of this paper are: alg-geom
19
The categories of this paper are: eess
31
The categories of this paper are: ao-sci
23
The categories of this paper are: solv-int
21
The categories of this paper are: adap-org
15
The categories of this paper are: nucl-ex
16
The categories of this paper are: stat
12
The categories of this paper are: nucl-th
7
The categories of this paper are: hep-th
27
The categories of this paper are: comp-gas
6
The categories of this paper are: astro-ph
2
The categories of this paper are: math