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Expressive power of tensor-network factorizations for probabilistic
  modeling, with applications from hidden Markov models to quantum machine
  learning

Expressive power of tensor-network factorizations for probabilistic modeling, with applications from hidden Markov models to quantum machine learning

8 July 2019
I. Glasser
R. Sweke
Nicola Pancotti
Jens Eisert
J. I. Cirac
ArXivPDFHTML

Papers citing "Expressive power of tensor-network factorizations for probabilistic modeling, with applications from hidden Markov models to quantum machine learning"

16 / 16 papers shown
Title
On the Relationship Between Monotone and Squared Probabilistic Circuits
On the Relationship Between Monotone and Squared Probabilistic Circuits
Benjie Wang
Mathias Niepert
TPM
45
5
0
01 Aug 2024
Non-negative Tensor Mixture Learning for Discrete Density Estimation
Non-negative Tensor Mixture Learning for Discrete Density Estimation
Kazu Ghalamkari
Jesper L. Hinrich
Morten Mørup
65
1
0
28 May 2024
Accuracy vs Memory Advantage in the Quantum Simulation of Stochastic
  Processes
Accuracy vs Memory Advantage in the Quantum Simulation of Stochastic Processes
L. Banchi
23
3
0
20 Dec 2023
Potential and limitations of random Fourier features for dequantizing quantum machine learning
Potential and limitations of random Fourier features for dequantizing quantum machine learning
R. Sweke
Erik Recio
Sofiene Jerbi
Elies Gil-Fuster
Bryce Fuller
Jens Eisert
Johannes Jakob Meyer
30
12
0
20 Sep 2023
Tensor Networks Meet Neural Networks: A Survey and Future Perspectives
Tensor Networks Meet Neural Networks: A Survey and Future Perspectives
Maolin Wang
Y. Pan
Zenglin Xu
Xiangli Yang
Guangxi Li
A. Cichocki
Andrzej Cichocki
55
19
0
22 Jan 2023
Grokking phase transitions in learning local rules with gradient descent
Grokking phase transitions in learning local rules with gradient descent
Bojan Žunkovič
E. Ilievski
63
16
0
26 Oct 2022
Deep tensor networks with matrix product operators
Deep tensor networks with matrix product operators
Bojan Žunkovič
72
4
0
16 Sep 2022
Generative modeling with projected entangled-pair states
Generative modeling with projected entangled-pair states
Tom Vieijra
L. Vanderstraeten
F. Verstraete
50
19
0
16 Feb 2022
Learnability of the output distributions of local quantum circuits
Learnability of the output distributions of local quantum circuits
M. Hinsche
M. Ioannou
A. Nietner
J. Haferkamp
Yihui Quek
D. Hangleiter
Jean-Pierre Seifert
Jens Eisert
R. Sweke
35
17
0
11 Oct 2021
F-Divergences and Cost Function Locality in Generative Modelling with
  Quantum Circuits
F-Divergences and Cost Function Locality in Generative Modelling with Quantum Circuits
Chiara Leadbeater
Louis Sharrock
Brian Coyle
Marcello Benedetti
20
11
0
08 Oct 2021
Tensor networks for unsupervised machine learning
Tensor networks for unsupervised machine learning
Jing Liu
Sujie Li
Jiang Zhang
Pan Zhang
SSL
9
25
0
24 Jun 2021
Tensor networks and efficient descriptions of classical data
Tensor networks and efficient descriptions of classical data
Sirui Lu
Márton Kanász-Nagy
I. Kukuljan
J. I. Cirac
24
24
0
11 Mar 2021
Positive Semidefinite Matrix Factorization: A Connection with Phase
  Retrieval and Affine Rank Minimization
Positive Semidefinite Matrix Factorization: A Connection with Phase Retrieval and Affine Rank Minimization
D. Lahat
Yanbin Lang
Vincent Y. F. Tan
Cédric Févotte
13
3
0
24 Jul 2020
Evaluation of Parameterized Quantum Circuits: on the relation between
  classification accuracy, expressibility and entangling capability
Evaluation of Parameterized Quantum Circuits: on the relation between classification accuracy, expressibility and entangling capability
T. Hubregtsen
Josef Pichlmeier
Patrick Stecher
K. Bertels
19
13
0
22 Mar 2020
A Tensor Network Approach to Finite Markov Decision Processes
A Tensor Network Approach to Finite Markov Decision Processes
E. Gillman
Dominic C. Rose
J. P. Garrahan
19
4
0
12 Feb 2020
From probabilistic graphical models to generalized tensor networks for
  supervised learning
From probabilistic graphical models to generalized tensor networks for supervised learning
I. Glasser
Nicola Pancotti
J. I. Cirac
AI4CE
69
75
0
15 Jun 2018
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