Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Tensor-Train Density Estimation   Estimation of probability density function from samples is one of the central
problems in statistics and machine learning. Modern neural network-based models
can learn high dimensional distributions but have problems with hyperparameter
selection and are often prone to instabilities during training and inference.
We propose a new efficient tensor train-based model for density estimation
(TTDE). Such density parametrization allows exact sampling, calculation of
cumulative and marginal density functions, and partition function. It also has
very intuitive hyperparameters. We develop an efficient non-adversarial
training procedure for TTDE based on the Riemannian optimization. Experimental
results demonstrate the competitive performance of the proposed method in
density estimation and sampling tasks, while TTDE significantly outperforms
competitors in training speed.

24
The categories of this paper are: dg-ga
18
The categories of this paper are: econ
19
The categories of this paper are: eess
6
The categories of this paper are: astro-ph
4
The categories of this paper are: cond-mat
30
The categories of this paper are: q-alg
37
The categories of this paper are: supr-con
27
The categories of this paper are: comp-gas
3
The categories of this paper are: physics
13
The categories of this paper are: quant-ph
17
The categories of this paper are: q-fin
9
The categories of this paper are: nlin
14
The categories of this paper are: math-ph
32
The categories of this paper are: atom-ph
23
The categories of this paper are: solv-int
21
The categories of this paper are: adap-org