(MPO)$^2$: Multivariate Polynomial Optimization based on Matrix Product Operators | Paper Citation Record

Observation 4eaab7dc-4327-4d3a-9d86-d6af06f991bf · outbound

This paper cites (MPO)ˆ2: Multivariate polynomial optimization based on matrix product operators,.

(MPO)$^2$: Multivariate Polynomial Optimization based on Matrix Product Operators (MPO)ˆ2: Multivariate polynomial optimization based on matrix product operators,

Reference 1

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Observation 6b5ae02a-a4d1-45ed-b9f0-b58eb2167078 · outbound

This paper cites Multilayer feedfor- ward networks are universal approximators,.

(MPO)$^2$: Multivariate Polynomial Optimization based on Matrix Product Operators Multilayer feedfor- ward networks are universal approximators,

Reference 2

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(MPO)$^2$: Multivariate Polynomial Optimization based on Matrix Product Operators Unresolved cited work

Reference 3

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Observation b4771a61-202b-4f62-9fec-8c8856bae1fc · outbound

This paper cites The variational gaussian process,.

(MPO)$^2$: Multivariate Polynomial Optimization based on Matrix Product Operators The variational gaussian process,

Reference 4

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Observation 31e73baa-e482-4d35-b957-98366614ecca · outbound

This paper cites Attention is all you need,.

(MPO)$^2$: Multivariate Polynomial Optimization based on Matrix Product Operators Attention is all you need,

Reference 5

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Observation 4815d265-28dc-443b-aa18-87320cbdc584 · outbound

This paper cites Long short-term memory,.

(MPO)$^2$: Multivariate Polynomial Optimization based on Matrix Product Operators Long short-term memory,

Reference 6

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Observation 7e00eb8c-0499-4b6e-a018-5ae186e5545d · outbound

This paper cites Language modeling with gated convolutional networks,.

(MPO)$^2$: Multivariate Polynomial Optimization based on Matrix Product Operators Language modeling with gated convolutional networks,

Reference 7

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Observation 6451aca5-65a3-4973-b1b9-9b0a5de6b0c5 · outbound

This paper cites Multiplicative interactions and where to find them,.

(MPO)$^2$: Multivariate Polynomial Optimization based on Matrix Product Operators Multiplicative interactions and where to find them,

Reference 8

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Observation 50efc767-e731-4aab-a6bb-f79c1d7ab785 · outbound

This paper cites Why does deep and cheap learning work so well?.

(MPO)$^2$: Multivariate Polynomial Optimization based on Matrix Product Operators Why does deep and cheap learning work so well?

Reference 9

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Observation 631c2dad-d259-42a4-a85e-366841cc43d6 · outbound

This paper cites The pi-sigma network: An efficient higher- order neural network for pattern classification and function ap- proximation,.

(MPO)$^2$: Multivariate Polynomial Optimization based on Matrix Product Operators The pi-sigma network: An efficient higher- order neural network for pattern classification and function ap- proximation,

Reference 10

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Observation 1b97bad6-7aa2-4554-a5d6-c2c736b14734 · outbound

This paper cites Ridge polynomial networks,.

(MPO)$^2$: Multivariate Polynomial Optimization based on Matrix Product Operators Ridge polynomial networks,

Reference 11

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Observation a0a63dfb-021d-4aed-8082-1a98bac1fe5c · outbound

This paper cites A sigma-pi-sigma neural network (spsnn),.

(MPO)$^2$: Multivariate Polynomial Optimization based on Matrix Product Operators A sigma-pi-sigma neural network (spsnn),

Reference 12

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Observation 2974ec7d-a636-43be-8279-9a794f6b6039 · outbound

This paper cites A new sigma-pi-sigma neural network based on l1 and l2 regularization and applications,.

(MPO)$^2$: Multivariate Polynomial Optimization based on Matrix Product Operators A new sigma-pi-sigma neural network based on l1 and l2 regularization and applications,

Reference 13

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Observation f29bec81-3519-461d-a495-776341864ada · outbound

This paper cites A recurrent sigma- pi-sigma neural network,.

(MPO)$^2$: Multivariate Polynomial Optimization based on Matrix Product Operators A recurrent sigma- pi-sigma neural network,

Reference 14

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Observation 6470152b-2432-4312-92c4-336ee966f8a8 · outbound

This paper cites Training sigma-pi neural networks with the grey wolf optimization algorithm,.

(MPO)$^2$: Multivariate Polynomial Optimization based on Matrix Product Operators Training sigma-pi neural networks with the grey wolf optimization algorithm,

Reference 15

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Observation 49f43c95-c3e4-4aed-a99d-45350cc032b9 · outbound

This paper cites Exploring the Approximation Capabilities of Multiplicative Neural Networks for Smooth Functions.

(MPO)$^2$: Multivariate Polynomial Optimization based on Matrix Product Operators Exploring the Approximation Capabilities of Multiplicative Neural Networks for Smooth Functions

Reference 16

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Observation f750f7b2-4daa-433a-b2d5-cfd76842c301 · outbound

This paper cites Multiplicative couplings facilitate rapid learning and information gating in recurrent neural networks,.

(MPO)$^2$: Multivariate Polynomial Optimization based on Matrix Product Operators Multiplicative couplings facilitate rapid learning and information gating in recurrent neural networks,

Reference 17

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Observation 9ed6df83-aab2-4a23-a659-63edb2c52296 · outbound

This paper cites Algebraic and optimization based algorithms for multivariate regression using symmetric tensor decomposition,.

(MPO)$^2$: Multivariate Polynomial Optimization based on Matrix Product Operators Algebraic and optimization based algorithms for multivariate regression using symmetric tensor decomposition,

Reference 18

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Observation 34df7276-98a3-43a1-b1b0-6b8699d2d836 · outbound

This paper cites Tensor-based multivariate polynomial optimization with application in blind identification,.

(MPO)$^2$: Multivariate Polynomial Optimization based on Matrix Product Operators Tensor-based multivariate polynomial optimization with application in blind identification,

Reference 19

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Observation c11f0069-77d7-439c-8e99-e99ee9201ad1 · outbound

This paper cites Regres- sion and classification with spline-based separable expansions,.

(MPO)$^2$: Multivariate Polynomial Optimization based on Matrix Product Operators Regres- sion and classification with spline-based separable expansions,

Reference 20

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Observation fbc70a75-1de3-4e34-b095-77ffb9ff1dee · outbound

This paper cites CPD-Structured Multivariate Polynomial Optimization,.

(MPO)$^2$: Multivariate Polynomial Optimization based on Matrix Product Operators CPD-Structured Multivariate Polynomial Optimization,

Reference 21

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Observation c44b6f0b-92ac-4a66-8e20-2129418627fc · outbound

This paper cites Interpretable bayesian tensor network kernel machines with automatic rank and feature selection,.

(MPO)$^2$: Multivariate Polynomial Optimization based on Matrix Product Operators Interpretable bayesian tensor network kernel machines with automatic rank and feature selection,

Reference 22

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Observation 15b6ebfa-efad-4319-ad51-ca032cc8b7b1 · outbound

This paper cites Deep polynomial neural networks,.

(MPO)$^2$: Multivariate Polynomial Optimization based on Matrix Product Operators Deep polynomial neural networks,

Reference 23

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Observation 34c514be-5d44-4d46-b6a4-1344e4bee593 · outbound

This paper cites Augmenting deep classifiers with polynomial neural networks,.

(MPO)$^2$: Multivariate Polynomial Optimization based on Matrix Product Operators Augmenting deep classifiers with polynomial neural networks,

Reference 24

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Observation 53af52b9-30e6-4edf-9297-31c986b7271c · outbound

This paper cites Supervised learning with tensor networks,.

(MPO)$^2$: Multivariate Polynomial Optimization based on Matrix Product Operators Supervised learning with tensor networks,

Reference 25

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Observation 5c2a80aa-8980-4ba5-a68e-c2a389309afb · outbound

This paper cites A block-sparse Tensor Train Format for sample-efficient high-dimensional Polynomial Regression.

(MPO)$^2$: Multivariate Polynomial Optimization based on Matrix Product Operators A block-sparse Tensor Train Format for sample-efficient high-dimensional Polynomial Regression

Reference 26

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(MPO)$^2$: Multivariate Polynomial Optimization based on Matrix Product Operators Unresolved cited work

Reference 28

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Observation 085bd154-dba8-41ed-a0ba-77642d113fd6 · outbound

This paper cites Tensor methods in computer vision and deep learning,.

(MPO)$^2$: Multivariate Polynomial Optimization based on Matrix Product Operators Tensor methods in computer vision and deep learning,

Reference 29

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Observation 3a12fc98-94fe-4d5e-8298-b0091d3ca9fa · outbound

This paper cites TensorNetwork for Machine Learning.

(MPO)$^2$: Multivariate Polynomial Optimization based on Matrix Product Operators TensorNetwork for Machine Learning

Reference 30

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Observation 2e871a23-5ca2-4ab4-97e5-d60d47c4eb2f · outbound

This paper cites Tensor-train decomposition,.

(MPO)$^2$: Multivariate Polynomial Optimization based on Matrix Product Operators Tensor-train decomposition,

Reference 31

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Observation b51b2aa7-c0de-4c81-a68e-0cf2c91a6134 · outbound

This paper cites Random feature maps for dot product kernels,.

(MPO)$^2$: Multivariate Polynomial Optimization based on Matrix Product Operators Random feature maps for dot product kernels,

Reference 32

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This paper cites Emergence of simple-cell receptive field properties by learning a sparse code for natural images,.

(MPO)$^2$: Multivariate Polynomial Optimization based on Matrix Product Operators Emergence of simple-cell receptive field properties by learning a sparse code for natural images,

Reference 33

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This paper cites The alternating linear scheme for tensor optimization in the tensor train format,.

(MPO)$^2$: Multivariate Polynomial Optimization based on Matrix Product Operators The alternating linear scheme for tensor optimization in the tensor train format,

Reference 34

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This paper cites The density-matrix renormalization group in the age of matrix product states,.

(MPO)$^2$: Multivariate Polynomial Optimization based on Matrix Product Operators The density-matrix renormalization group in the age of matrix product states,

Reference 35

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This paper cites Exponentially-convergent strategies for defeating the runge phenomenon for the approximation of non-periodic functions, part i: single-interval schemes,.

(MPO)$^2$: Multivariate Polynomial Optimization based on Matrix Product Operators Exponentially-convergent strategies for defeating the runge phenomenon for the approximation of non-periodic functions, part i: single-interval schemes,

Reference 36

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(MPO)$^2$: Multivariate Polynomial Optimization based on Matrix Product Operators Unresolved cited work

Reference 37

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This paper cites The Alternating Linear Scheme for Tensor Optimization in the Tensor Train Format,.

(MPO)$^2$: Multivariate Polynomial Optimization based on Matrix Product Operators The Alternating Linear Scheme for Tensor Optimization in the Tensor Train Format,

Reference 38

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Observation 7e8f26a6-3076-46ee-9e29-dc28cf728efe · outbound

This paper cites Xgboost: A scalable tree boosting system,.

(MPO)$^2$: Multivariate Polynomial Optimization based on Matrix Product Operators Xgboost: A scalable tree boosting system,

Reference 39

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Observation 6ea11460-7f6c-4705-8811-f8fc66b1c4c0 · outbound

This paper cites Scikit-learn: Machine learning in Python,.

(MPO)$^2$: Multivariate Polynomial Optimization based on Matrix Product Operators Scikit-learn: Machine learning in Python,

Reference 40

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Observation f3d8df7a-23cc-4c29-a5f6-323d57d0e49c · outbound

This paper cites The uci machine learning repository,.

(MPO)$^2$: Multivariate Polynomial Optimization based on Matrix Product Operators The uci machine learning repository,

Reference 41

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Observation c0df3b20-504d-4c16-835d-ee879b515cbc · outbound

This paper cites Decoupled Weight Decay Regularization.

(MPO)$^2$: Multivariate Polynomial Optimization based on Matrix Product Operators Decoupled Weight Decay Regularization

Reference 42

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Observation b047469a-247c-4aad-80c9-967205aee9f0 · outbound

This paper cites Supervised Learning with Quantum-Inspired Tensor Networks.

(MPO)$^2$: Multivariate Polynomial Optimization based on Matrix Product Operators Supervised Learning with Quantum-Inspired Tensor Networks

Reference 43

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Observation 770ae26c-6a1e-430e-bbc9-7bd45bfca2ad · outbound

This paper cites Adam: A Method for Stochastic Optimization.

(MPO)$^2$: Multivariate Polynomial Optimization based on Matrix Product Operators Adam: A Method for Stochastic Optimization

Reference 44

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Observation b655ef7b-cacd-4477-bd51-0c3a269552bf · outbound

This paper cites Ciolli, “Mpo2,” https://git.kosmon.org/nicco/MPO2, 2026.

(MPO)$^2$: Multivariate Polynomial Optimization based on Matrix Product Operators Ciolli, “Mpo2,” https://git.kosmon.org/nicco/MPO2, 2026

Reference 45

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Observation 94ef0dcb-4a9e-4723-909f-162ce80e0587 · outbound

This paper cites Optimization of cement-slag-based stabilizer proportions and macro-micro properties research of solidified soil,.

(MPO)$^2$: Multivariate Polynomial Optimization based on Matrix Product Operators Optimization of cement-slag-based stabilizer proportions and macro-micro properties research of solidified soil,

Reference 46

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Observation 616904bf-6249-42dc-b005-469c3ccb2395 · outbound

This paper cites Discovering interpretable elastoplasticity models via the neural polynomial method enabled symbolic regressions,.

(MPO)$^2$: Multivariate Polynomial Optimization based on Matrix Product Operators Discovering interpretable elastoplasticity models via the neural polynomial method enabled symbolic regressions,

Reference 47

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Observation a8948dbd-123d-4633-aa21-1a76b39b2f79 · outbound

This paper cites Characterization of hybrid composites with polyester waste fibers, olive root fibers and coir pith micro-particles using mixture design analysis for structural applications,.

(MPO)$^2$: Multivariate Polynomial Optimization based on Matrix Product Operators Characterization of hybrid composites with polyester waste fibers, olive root fibers and coir pith micro-particles using mixture design analysis for structural applications,

Reference 48

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Observation f986c9b0-5a57-4eca-9414-bdce303a06c5 · outbound

This paper cites Multi-objective optimisation of the mechanical properties of rice husk ash–modified lateritic concrete,.

(MPO)$^2$: Multivariate Polynomial Optimization based on Matrix Product Operators Multi-objective optimisation of the mechanical properties of rice husk ash–modified lateritic concrete,

Reference 49

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Observation 8b420ad9-279e-4e43-8b10-eba6dd08536c · outbound

This paper cites Using response surface models to analyze drug combinations,.

(MPO)$^2$: Multivariate Polynomial Optimization based on Matrix Product Operators Using response surface models to analyze drug combinations,

Reference 50

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Observation d325057b-6119-4313-8ba0-5780ba66da4e · outbound

This paper cites Oil-recovery predictions for surfactant polymer flooding,.

(MPO)$^2$: Multivariate Polynomial Optimization based on Matrix Product Operators Oil-recovery predictions for surfactant polymer flooding,

Reference 51

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Observation c4dd0944-9271-4db2-9002-c03596d784fd · outbound

This paper cites The mnist database of handwritten digit images for machine learning research [best of the web],.

(MPO)$^2$: Multivariate Polynomial Optimization based on Matrix Product Operators The mnist database of handwritten digit images for machine learning research [best of the web],

Reference 52

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Observation 231015ba-a7a8-479f-849a-70056abe0a68 · outbound

This paper cites Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms.

(MPO)$^2$: Multivariate Polynomial Optimization based on Matrix Product Operators Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms

Reference 53

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Observation f54f15f3-588a-4e8a-a476-b3d11b58f287 · outbound

This paper cites The probabilistic tensor decomposition toolbox,.

(MPO)$^2$: Multivariate Polynomial Optimization based on Matrix Product Operators The probabilistic tensor decomposition toolbox,

Reference 54

Resolution: verified exact; doi, observed 2026-08-01T22:03:19.902649Z

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Observation 29d7ba00-3873-4a55-9df0-fe327fd07c61 · outbound

This paper cites Tensor machines for learning target-specific polynomial features.

(MPO)$^2$: Multivariate Polynomial Optimization based on Matrix Product Operators Tensor machines for learning target-specific polynomial features

Reference 2015

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Observation 6cfd6e2f-e42f-4df2-b1a2-2993f0c03335 · outbound

This paper cites Interpretable Bayesian Tensor Network Kernel Machines with Automatic Rank and Feature Selection.

(MPO)$^2$: Multivariate Polynomial Optimization based on Matrix Product Operators Interpretable Bayesian Tensor Network Kernel Machines with Automatic Rank and Feature Selection

Reference 2025

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