Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Tensor networks for unsupervised machine learning   Modeling the joint distribution of high-dimensional data is a central task in
unsupervised machine learning. In recent years, many interests have been
attracted to developing learning models based on tensor networks, which have
advantages of theoretical understandings of the expressive power using
entanglement properties, and as a bridge connecting the classical computation
and the quantum computation. Despite the great potential, however, existing
tensor-network-based unsupervised models only work as a proof of principle, as
their performances are much worse than the standard models such as the
restricted Boltzmann machines and neural networks. In this work, we present the
Autoregressive Matrix Product States (AMPS), a tensor-network-based model
combining the matrix product states from quantum many-body physics and the
autoregressive models from machine learning. The model enjoys exact calculation
of normalized probability and unbiased sampling, as well as a clear theoretical
understanding of expressive power. We demonstrate the performance of our model
using two applications, the generative modeling on synthetic and real-world
data, and the reinforcement learning in statistical physics. Using extensive
numerical experiments, we show that the proposed model significantly
outperforms the existing tensor-network-based models and the restricted
Boltzmann machines, and is competitive with the state-of-the-art neural network
models.

28
The categories of this paper are: alg-geom
3
The categories of this paper are: physics
10
The categories of this paper are: q-bio
27
The categories of this paper are: comp-gas
22
The categories of this paper are: chao-dyn
32
The categories of this paper are: atom-ph
33
The categories of this paper are: plasm-ph
29
The categories of this paper are: funct-an
34
The categories of this paper are: chem-ph
19
The categories of this paper are: eess
37
The categories of this paper are: supr-con
23
The categories of this paper are: solv-int
12
The categories of this paper are: nucl-th
8
The categories of this paper are: hep-ex
21
The categories of this paper are: adap-org
35
The categories of this paper are: mtrl-th