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Paper Citation Record · LEDGER

Symmetry-preserving neural networks in lattice field theories

As of 9 August 2026, this Paper Citation Record lists 100 of 146 outbound references and 1 inbound Pith citation observation for arXiv:2506.12493.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2506.12493 v1

Coverage vector

measured 100 of 146 reference resolution

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Source: paper_references, paper_reference_links, observed 2026-08-07T00:58:40.750257Z

measured 101 of 101 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-03T04:24:13.569884Z

measured 0 of 1 external citation measurements

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Source: arxiv_reference, observed 2026-07-03T04:27:36.143568Z

Reference resolution

100 of 146 outbound references displayed

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Outbound references

Observation 0f5e69d0-6038-4a89-a508-0a1abadd28dc · outbound

This paper cites Generalization capabilities of translationally equivariant neural networks.

Symmetry-preserving neural networks in lattice field theories Generalization capabilities of translationally equivariant neural networks

Reference 1

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Observation 490dfe16-8ae4-4ef3-8c08-24cd9507ff99 · outbound

This paper cites Lattice gauge equivariant convolutional neural networks.

Symmetry-preserving neural networks in lattice field theories Lattice gauge equivariant convolutional neural networks

Reference 2

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Observation 913926da-69ec-4ed9-b7ac-05219c412ce5 · outbound

This paper cites Applications of Lattice Gauge Equivariant Neural Networks.

Symmetry-preserving neural networks in lattice field theories Applications of Lattice Gauge Equivariant Neural Networks

Reference 3

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Observation 7e237a4c-345b-480d-8779-1e6a74848aeb · outbound

This paper cites Weinberg, A Model of Leptons , Phys.

Symmetry-preserving neural networks in lattice field theories Weinberg, A Model of Leptons , Phys

Reference 4

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Observation 875148e4-8971-48a8-b66b-03883fb89dfd · outbound

This paper cites Salam, Weak and Electromagnetic Interactions , Conf.

Symmetry-preserving neural networks in lattice field theories Salam, Weak and Electromagnetic Interactions , Conf

Reference 5

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Symmetry-preserving neural networks in lattice field theories Unresolved cited work

Reference 6

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Observation f067a44e-875c-4d04-986c-1d363d96e595 · outbound

This paper cites The anomalous magnetic moment of the muon in the Standard Model.

Symmetry-preserving neural networks in lattice field theories The anomalous magnetic moment of the muon in the Standard Model

Reference 7

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Observation 7854c8bf-3740-4f64-baf9-d920c085bbe5 · outbound

This paper cites Metropolis and S.

Symmetry-preserving neural networks in lattice field theories Metropolis and S

Reference 8

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This paper cites Sokal, Monte Carlo Methods in Statistical Mechanics: Foundations and New Algorithms , Springer US, Boston, MA (1997), 10.1007/978-1-4899-0319-8 6.

Symmetry-preserving neural networks in lattice field theories Sokal, Monte Carlo Methods in Statistical Mechanics: Foundations and New Algorithms , Springer US, Boston, MA (1997), 10.1007/978-1-4899-0319-8 6

Reference 9

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Observation bc84b37e-c689-443d-8403-8d524f8724d7 · outbound

This paper cites Aarts, Introductory lectures on lattice QCD at nonzero baryon number, Journal of Physics: Conference Series 706 (2016) 022004.

Symmetry-preserving neural networks in lattice field theories Aarts, Introductory lectures on lattice QCD at nonzero baryon number, Journal of Physics: Conference Series 706 (2016) 022004

Reference 10

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Observation 3875f6e0-e4c9-4b0f-9f64-fc830c44b169 · outbound

This paper cites Approaches to the sign problem in lattice field theory.

Symmetry-preserving neural networks in lattice field theories Approaches to the sign problem in lattice field theory

Reference 11

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Observation c78f62b6-ef41-49b6-868c-0983e5acf47f · outbound

This paper cites ImageNet Large Scale Visual Recognition Challenge.

Symmetry-preserving neural networks in lattice field theories ImageNet Large Scale Visual Recognition Challenge

Reference 12

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Observation 62b8aae8-26a6-422f-a7d9-ef4b5b011025 · outbound

This paper cites GPT-4 Technical Report.

Symmetry-preserving neural networks in lattice field theories GPT-4 Technical Report

Reference 13

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This paper cites Silver, T.

Symmetry-preserving neural networks in lattice field theories Silver, T

Reference 14

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This paper cites Jumper, R.

Symmetry-preserving neural networks in lattice field theories Jumper, R

Reference 15

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Symmetry-preserving neural networks in lattice field theories Unresolved cited work

Reference 16

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Observation 4a5ac0fc-72f5-4e9b-b20d-67a6bec6609b · outbound

This paper cites Dubey, S.K.

Symmetry-preserving neural networks in lattice field theories Dubey, S.K

Reference 17

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This paper cites Rosenblatt, The perceptron - A perceiving and recognizing automaton, Tech.

Symmetry-preserving neural networks in lattice field theories Rosenblatt, The perceptron - A perceiving and recognizing automaton, Tech

Reference 18

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This paper cites Rosenblatt, The perceptron: A probabilistic model for information storage and organization in the brain.

Symmetry-preserving neural networks in lattice field theories Rosenblatt, The perceptron: A probabilistic model for information storage and organization in the brain

Reference 19

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This paper cites LeCun, Y.

Symmetry-preserving neural networks in lattice field theories LeCun, Y

Reference 20

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This paper cites An overview of gradient descent optimization algorithms.

Symmetry-preserving neural networks in lattice field theories An overview of gradient descent optimization algorithms

Reference 21

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This paper cites Ying, An Overview of Overfitting and its Solutions , Journal of Physics: Conference Series 1168 (2019) 022022.

Symmetry-preserving neural networks in lattice field theories Ying, An Overview of Overfitting and its Solutions , Journal of Physics: Conference Series 1168 (2019) 022022

Reference 22

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This paper cites Cybenko, Approximation by superpositions of a sigmoidal function, Mathematics of Control, Signals and Systems 2 (1989) 303.

Symmetry-preserving neural networks in lattice field theories Cybenko, Approximation by superpositions of a sigmoidal function, Mathematics of Control, Signals and Systems 2 (1989) 303

Reference 23

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Symmetry-preserving neural networks in lattice field theories The Expressive Power of Neural Networks: A View from the Width

Reference 24

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Symmetry-preserving neural networks in lattice field theories Carrasquilla and R.G

Reference 25

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This paper cites Regressive and generative neural networks for scalar field theory.

Symmetry-preserving neural networks in lattice field theories Regressive and generative neural networks for scalar field theory

Reference 26

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This paper cites Continuous-mixture Autoregressive Networks for efficient variational calculation of many-body systems.

Symmetry-preserving neural networks in lattice field theories Continuous-mixture Autoregressive Networks for efficient variational calculation of many-body systems

Reference 27

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This paper cites Mapping distinct phase transitions to a neural network.

Symmetry-preserving neural networks in lattice field theories Mapping distinct phase transitions to a neural network

Reference 28

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This paper cites Towards Novel Insights in Lattice Field Theory with Explainable Machine Learning.

Symmetry-preserving neural networks in lattice field theories Towards Novel Insights in Lattice Field Theory with Explainable Machine Learning

Reference 29

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This paper cites Machine learning action parameters in lattice quantum chromodynamics.

Symmetry-preserving neural networks in lattice field theories Machine learning action parameters in lattice quantum chromodynamics

Reference 30

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This paper cites Estimation of Thermodynamic Observables in Lattice Field Theories with Deep Generative Models.

Symmetry-preserving neural networks in lattice field theories Estimation of Thermodynamic Observables in Lattice Field Theories with Deep Generative Models

Reference 31

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This paper cites Complex Paths Around The Sign Problem.

Symmetry-preserving neural networks in lattice field theories Complex Paths Around The Sign Problem

Reference 32

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Observation 439989d9-0f85-4b30-9a26-d51a8e95fe21 · outbound

This paper cites Towards learning optimized kernels for complex Langevin.

Symmetry-preserving neural networks in lattice field theories Towards learning optimized kernels for complex Langevin

Reference 33

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Observation a9e91b95-ddcc-413c-aa1d-41f495f4a234 · outbound

This paper cites Lattice real-time simulations with learned optimal kernels.

Symmetry-preserving neural networks in lattice field theories Lattice real-time simulations with learned optimal kernels

Reference 34

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This paper cites Mitigating Green's function Monte Carlo signal-to-noise problems using contour deformations.

Symmetry-preserving neural networks in lattice field theories Mitigating Green's function Monte Carlo signal-to-noise problems using contour deformations

Reference 35

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Observation 0da7ad59-3cdb-4079-8ce3-4cfdf130f0e0 · outbound

This paper cites Machine Learning Holographic Mapping by Neural Network Renormalization Group.

Symmetry-preserving neural networks in lattice field theories Machine Learning Holographic Mapping by Neural Network Renormalization Group

Reference 36

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Observation 620ef128-042f-4f38-8a49-f25567b11968 · outbound

This paper cites Inverse Renormalization Group in Quantum Field Theory.

Symmetry-preserving neural networks in lattice field theories Inverse Renormalization Group in Quantum Field Theory

Reference 37

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Observation 741c05bc-359e-46d9-a546-be10b0c355d9 · outbound

This paper cites Adding machine learning within Hamiltonians: Renormalization group transformations, symmetry breaking and restoration.

Symmetry-preserving neural networks in lattice field theories Adding machine learning within Hamiltonians: Renormalization group transformations, symmetry breaking and restoration

Reference 38

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Observation 8fceb8d3-533b-4d8b-a6af-ad0aa19663ac · outbound

This paper cites Reducing Autocorrelation Times in Lattice Simulations with Generative Adversarial Networks.

Symmetry-preserving neural networks in lattice field theories Reducing Autocorrelation Times in Lattice Simulations with Generative Adversarial Networks

Reference 39

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source=pdf_text observed=2026-08-07T00:58:33.467235Z digest=sha256:120a2e04bdc8f5cfc47f2f1c758fecc7f52137bc39ac9d58544ea75d79a5cec6

Observation ac5c7eba-1143-451f-8159-06ac5acfa3e5 · outbound

This paper cites Generative Diffusion Models for Lattice Field Theory.

Symmetry-preserving neural networks in lattice field theories Generative Diffusion Models for Lattice Field Theory

Reference 40

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source=pdf_text observed=2026-08-07T00:58:33.632085Z digest=sha256:0f18f8b3308a305cc96548b567ea8b96a4fa486fef8cc8d6a203a7a0fceae5e7

Observation e296c230-c761-4ea4-9e8b-c1a2eb6b01e4 · outbound

This paper cites Stochastic Normalizing Flows.

Symmetry-preserving neural networks in lattice field theories Stochastic Normalizing Flows

Reference 41

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source=pdf_text observed=2026-08-07T00:58:33.760555Z digest=sha256:ee5edf11a2388c6d42f9b6cdd4931a23ceaf75a2da398a89b817b4e2e181c73e

Observation 3f37507f-6683-4a7e-b32c-5551564ab7ad · outbound

This paper cites Stochastic normalizing flows as non-equilibrium transformations.

Symmetry-preserving neural networks in lattice field theories Stochastic normalizing flows as non-equilibrium transformations

Reference 42

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source=pdf_text observed=2026-08-07T00:58:33.889217Z digest=sha256:b5b1d4330f20d957c7bdfa147812475109833bf4a890074f028b16b0b5684bf1

Observation e2b2d704-e01a-4274-96c5-cab974efb82b · outbound

This paper cites Mitigating topological freezing using out-of-equilibrium simulations.

Symmetry-preserving neural networks in lattice field theories Mitigating topological freezing using out-of-equilibrium simulations

Reference 43

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source=pdf_text observed=2026-08-07T00:58:34.003489Z digest=sha256:015107f0df3cb605ee36a48720699872d566084f2fc194fc976bdba00531d61f

Observation f77cd1a5-475a-4b78-951b-389f8e5a22bf · outbound

This paper cites Sampling the lattice Nambu-Goto string using Continuous Normalizing Flows.

Symmetry-preserving neural networks in lattice field theories Sampling the lattice Nambu-Goto string using Continuous Normalizing Flows

Reference 44

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source=pdf_text observed=2026-08-07T00:58:34.125151Z digest=sha256:24a334a9991ebc13718bd79711a3123076b904f84f5abaaae7d41a7e623ebe83

Observation da759037-5da0-480b-a583-2bb17dcdbde5 · outbound

This paper cites Noether, Invariante Variationsprobleme, Nachrichten von der Gesellschaft der Wissenschaften zu G¨ ottingen, Mathematisch-Physikalische Klasse 1918 (1918) 235.

Symmetry-preserving neural networks in lattice field theories Noether, Invariante Variationsprobleme, Nachrichten von der Gesellschaft der Wissenschaften zu G¨ ottingen, Mathematisch-Physikalische Klasse 1918 (1918) 235

Reference 45

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source=pdf_text observed=2026-08-07T00:58:34.282738Z digest=sha256:847b3e2c9e1ab5763ddb2df75bd87a60214c212235bf254feadffced2eab672c

Observation 559207c0-ab19-4da3-8ad6-78ac5c1d06b5 · outbound

This paper cites J¨ ahne,Digital Image Processing, 5th revised and extended edition , Berlin: Springer-Verlag (2002), 10.1088/0957-0233/13/9/711.

Symmetry-preserving neural networks in lattice field theories J¨ ahne,Digital Image Processing, 5th revised and extended edition , Berlin: Springer-Verlag (2002), 10.1088/0957-0233/13/9/711

Reference 46

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doi, observed 2026-08-07T00:58:46.598143Z

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source=pdf_text observed=2026-08-07T00:58:34.401117Z digest=sha256:0cd3b4e9a03a0abc7693dd15510cd97d22a3cd27da71b57cc7c8ab6a5e12ea43

Observation 32b492b6-a49d-483c-a94f-681e06f81986 · outbound

This paper cites Yarotsky, Universal Approximations of Invariant Maps by Neural Networks, Constructive Approximation 55 (2022) 407.

Symmetry-preserving neural networks in lattice field theories Yarotsky, Universal Approximations of Invariant Maps by Neural Networks, Constructive Approximation 55 (2022) 407

Reference 47

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source=pdf_text observed=2026-08-07T00:58:34.551263Z digest=sha256:cdc841ea205e8aee9133378daee5e03288a893cdb5ef7db17ec52175030aeb85

Observation 8dd83a8d-85fc-402d-b6a4-1659c39f301d · outbound

This paper cites Zhou, Universality of deep convolutional neural networks , Applied and Computational Harmonic Analysis 48 (2020) 787.

Symmetry-preserving neural networks in lattice field theories Zhou, Universality of deep convolutional neural networks , Applied and Computational Harmonic Analysis 48 (2020) 787

Reference 48

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source=pdf_text observed=2026-08-07T00:58:34.679671Z digest=sha256:80860dea12468efde4c1453fd162ea21b83ab87dc6c18f4cd6cc9d08dd07dd21

Observation 673f2168-705f-4203-b4f0-104a6993d96c · outbound

This paper cites Fukushima, Neocognitron: A Self-organizing Neural Network Model for a Mechanism of Pattern Recognition Unaffected by Shift in Position, Biological Cybernetics 36 (1980) 193.

Symmetry-preserving neural networks in lattice field theories Fukushima, Neocognitron: A Self-organizing Neural Network Model for a Mechanism of Pattern Recognition Unaffected by Shift in Position, Biological Cybernetics 36 (1980) 193

Reference 49

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source=pdf_text observed=2026-08-07T00:58:34.824742Z digest=sha256:8e892726cc73915207b77852533e49d8f3049aeb9c239d9a28cbefd5e2c908e2

Observation ee7d0ea0-1f45-4c70-ae85-5d7bf27291ac · outbound

This paper cites Krizhevsky, I.

Symmetry-preserving neural networks in lattice field theories Krizhevsky, I

Reference 50

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source=pdf_text observed=2026-08-07T00:58:34.928536Z digest=sha256:959ee51ddde4911b2cd2d1eba9c6f7a2f0ae0b0bc064c070aaadbbf88c442220

Observation 2114cddb-4e27-4efa-bedd-3ef4319261c0 · outbound

This paper cites Network In Network.

Symmetry-preserving neural networks in lattice field theories Network In Network

Reference 51

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source=pdf_text observed=2026-08-07T00:58:35.093299Z digest=sha256:1bbf726a06030f1b3d7c4cb99a7da532ca5926c118c0ef82ccae5b3b7165ed68

Observation 7ec491da-6fee-4415-a6f8-4639989c1379 · outbound

This paper cites an unresolved cited work.

Symmetry-preserving neural networks in lattice field theories Unresolved cited work

Reference 52

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source=pdf_text observed=2026-08-07T00:58:35.197113Z digest=sha256:5ecd1b6240976a14d0b9fdfc703eaa57e7b3549ea0f4bc224d867e972255ff4c

Observation 3af39853-6264-465c-b76a-8a3663719808 · outbound

This paper cites Machine Learning of Explicit Order Parameters: From the Ising Model to SU(2) Lattice Gauge Theory.

Symmetry-preserving neural networks in lattice field theories Machine Learning of Explicit Order Parameters: From the Ising Model to SU(2) Lattice Gauge Theory

Reference 53

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source=pdf_text observed=2026-08-07T00:58:35.330614Z digest=sha256:2d59272e17fa70eda2024db63f0c072136f9b5084306b41535278d52c67bbd6d

Observation b312d348-720b-487c-9a1e-02f9837e0ea0 · outbound

This paper cites Machine Learned Phase Transitions in a System of Anisotropic Particles on a Square Lattice.

Symmetry-preserving neural networks in lattice field theories Machine Learned Phase Transitions in a System of Anisotropic Particles on a Square Lattice

Reference 54

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source=pdf_text observed=2026-08-07T00:58:35.459064Z digest=sha256:57a41b3986dbcf9b31ba4e669544a75e8e3850a22441ab86b13401723e331d2b

Observation 0f7eaa36-3ac2-4f3b-8e92-153e90d16656 · outbound

This paper cites Deep Reinforcement Learning Optimizes Graphene Nanopores for Efficient Desalination.

Symmetry-preserving neural networks in lattice field theories Deep Reinforcement Learning Optimizes Graphene Nanopores for Efficient Desalination

Reference 55

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source=pdf_text observed=2026-08-07T00:58:35.601422Z digest=sha256:d6da170b1a3882c89d34f153d14714d3593a62694cd63a9190109579874ac48d

Observation eef887d7-b54f-4118-82c7-c479b1fa7d3b · outbound

This paper cites Karniadakis, I.G.

Symmetry-preserving neural networks in lattice field theories Karniadakis, I.G

Reference 56

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source=pdf_text observed=2026-08-07T00:58:35.740103Z digest=sha256:4d768f48fc188364533e9646c09b624a738a031b32b7ff19fdb2587d662f903b

Observation 267ce6c6-dc3c-4786-a56f-4d3d639dd0cc · outbound

This paper cites Hamiltonian Neural Networks.

Symmetry-preserving neural networks in lattice field theories Hamiltonian Neural Networks

Reference 57

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source=pdf_text observed=2026-08-07T00:58:35.856533Z digest=sha256:5f011f2a4c71c8db494d81ff6c7e01eb71b407ba3ec17b6c9f492783c3e110e5

Observation 2b4dfeac-babb-4072-a533-2cf50c7ad473 · outbound

This paper cites Lagrangian Neural Networks.

Symmetry-preserving neural networks in lattice field theories Lagrangian Neural Networks

Reference 58

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source=pdf_text observed=2026-08-07T00:58:35.953422Z digest=sha256:27c07ce5484dd823129fb5e7945defee4de613e6561ecfa5ed3309667214471b

Observation 27acd6f3-b03a-4969-be02-613888bbaf71 · outbound

This paper cites M¨ uller,Exact conservation laws for neural network integrators of dynamical systems , Journal of Computational Physics 488 (2023) 112234.

Symmetry-preserving neural networks in lattice field theories M¨ uller,Exact conservation laws for neural network integrators of dynamical systems , Journal of Computational Physics 488 (2023) 112234

Reference 59

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source=pdf_text observed=2026-08-07T00:58:36.047396Z digest=sha256:6a0982d5c9b2fdc98f297b31611130e88c2e7a101ec1ef84bba0f2629c53806b

Observation a725f05d-ba25-4c98-8ead-d870c8bd3d7b · outbound

This paper cites Group Equivariant Convolutional Networks.

Symmetry-preserving neural networks in lattice field theories Group Equivariant Convolutional Networks

Reference 60

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source=pdf_text observed=2026-08-07T00:58:36.200164Z digest=sha256:b02dc49ef431fe66c811a80284aa1020d0bff03d34d251bd2d0eabec492d20f3

Observation 5e96cee4-6250-4f62-94fd-71de449ddd36 · outbound

This paper cites Steerable CNNs.

Symmetry-preserving neural networks in lattice field theories Steerable CNNs

Reference 61

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source=pdf_text observed=2026-08-07T00:58:36.289416Z digest=sha256:34cd9f2a223ea04bfae675369ca376e6a9f9ea9d7c6a5330dd18c35504a5f3ca

Observation b815964e-7a45-4cbf-a309-ba7fbd123dce · outbound

This paper cites Harmonic Networks: Deep Translation and Rotation Equivariance.

Symmetry-preserving neural networks in lattice field theories Harmonic Networks: Deep Translation and Rotation Equivariance

Reference 62

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source=pdf_text observed=2026-08-07T00:58:36.372277Z digest=sha256:26c3783bc39fb99d30419449e2634e21f5adc8dc061d300b5dfc66c9e543e3fb

Observation 2bee4283-698f-438a-9090-2662b52140cd · outbound

This paper cites CubeNet: Equivariance to 3D Rotation and Translation.

Symmetry-preserving neural networks in lattice field theories CubeNet: Equivariance to 3D Rotation and Translation

Reference 63

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source=pdf_text observed=2026-08-07T00:58:36.477054Z digest=sha256:b8f64fffde3c89ad6d9c793781e7338346d2181bad701e61f90078c894c3d2e2

Observation 2997f42e-2144-4e73-8953-b8f96c98be92 · outbound

This paper cites A rotation-equivariant convolutional neural network model of primary visual cortex.

Symmetry-preserving neural networks in lattice field theories A rotation-equivariant convolutional neural network model of primary visual cortex

Reference 64

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source=pdf_text observed=2026-08-07T00:58:36.563770Z digest=sha256:07e64c87424d0d5e96fb56b86f6770f2b11f66766ce821be436eeea3331ae9fb

Observation a74d1b8d-6cd3-4985-96fa-b26acc262f21 · outbound

This paper cites Rotation Equivariant CNNs for Digital Pathology.

Symmetry-preserving neural networks in lattice field theories Rotation Equivariant CNNs for Digital Pathology

Reference 65

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source=pdf_text observed=2026-08-07T00:58:36.715649Z digest=sha256:09edf4626ac8599f6f7940aec560474ed94b3d78aeeb7633f5c7aac57578a2cf

Observation accbe9f4-e101-43e5-a6ff-edeed870b061 · outbound

This paper cites Roto-Translation Equivariant Convolutional Networks: Application to Histopathology Image Analysis.

Symmetry-preserving neural networks in lattice field theories Roto-Translation Equivariant Convolutional Networks: Application to Histopathology Image Analysis

Reference 66

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source=pdf_text observed=2026-08-07T00:58:36.850216Z digest=sha256:8bc7eb9c8312273fd209562f2532d77babd409ef82450de60c2e5fb0f40105f0

Observation 2d480661-f0e8-49ae-a067-a5c033f8eeb2 · outbound

This paper cites Beyond CNNs: Exploiting Further Inherent Symmetries in Medical Images for Segmentation.

Symmetry-preserving neural networks in lattice field theories Beyond CNNs: Exploiting Further Inherent Symmetries in Medical Images for Segmentation

Reference 67

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local_arxiv, observed 2026-08-07T00:58:49.844399Z

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source=pdf_text observed=2026-08-07T00:58:36.967900Z digest=sha256:c629b97395278263860b4cbbd494639f520e5c54b27c2f82b850068daca8b9f2

Observation 8af01af8-55c8-4e05-b92d-9d902b2b6a5d · outbound

This paper cites Fanaroff-Riley classification of radio galaxies using group-equivariant convolutional neural networks.

Symmetry-preserving neural networks in lattice field theories Fanaroff-Riley classification of radio galaxies using group-equivariant convolutional neural networks

Reference 68

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source=pdf_text observed=2026-08-07T00:58:37.076834Z digest=sha256:7e208d34d8ce0a4016531ca863200741fbedc1e4b2f08061e8095f310bf35076

Observation 29e012bb-adc1-429d-b0c5-ae014c616bab · outbound

This paper cites On the Generalization of Equivariance and Convolution in Neural Networks to the Action of Compact Groups.

Symmetry-preserving neural networks in lattice field theories On the Generalization of Equivariance and Convolution in Neural Networks to the Action of Compact Groups

Reference 69

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source=pdf_text observed=2026-08-07T00:58:37.199994Z digest=sha256:544a180818e87758560de2e83da5eb5358134115fae38dce52c32ec36604e140

Observation d30ea498-0796-497f-9872-792d4f8238ab · outbound

This paper cites Covariance in Physics and Convolutional Neural Networks.

Symmetry-preserving neural networks in lattice field theories Covariance in Physics and Convolutional Neural Networks

Reference 70

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local_arxiv, observed 2026-08-07T00:58:49.492821Z

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source=pdf_text observed=2026-08-07T00:58:37.348370Z digest=sha256:86e29b79a87a99b9df650567b2429c598271c15b8b5b2d3eff822a3f2cebc151

Observation df285526-dd37-4060-bb1e-bcaedd286087 · outbound

This paper cites Theoretical Aspects of Group Equivariant Neural Networks.

Symmetry-preserving neural networks in lattice field theories Theoretical Aspects of Group Equivariant Neural Networks

Reference 71

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local_arxiv, observed 2026-08-07T00:58:49.286596Z

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source=pdf_text observed=2026-08-07T00:58:37.523954Z digest=sha256:5ee28893890d490c2bed1a49c806c18f1bb142eadf08999b5924fb69742da4fa

Observation 3c89c460-bbbe-4788-9c83-15a3fa9306a4 · outbound

This paper cites Boosting Deep Neural Networks with Geometrical Prior Knowledge: A Survey.

Symmetry-preserving neural networks in lattice field theories Boosting Deep Neural Networks with Geometrical Prior Knowledge: A Survey

Reference 72

Resolution
verified exact
local_arxiv, observed 2026-08-07T00:58:49.158611Z

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source=pdf_text observed=2026-08-07T00:58:37.651999Z digest=sha256:902ad7828d44606ef1199ce20612fb1451898f43f3a596acc7793a8f38c946ab

Observation 5bb847ee-aff7-44e7-8101-60b8b8ab65d2 · outbound

This paper cites Geometric Deep Learning and Equivariant Neural Networks.

Symmetry-preserving neural networks in lattice field theories Geometric Deep Learning and Equivariant Neural Networks

Reference 73

Resolution
verified exact
local_arxiv, observed 2026-08-07T00:58:48.945466Z

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source=pdf_text observed=2026-08-07T00:58:37.737413Z digest=sha256:f5b3f460f10e8d3b4796fd6598a74033133cbe72b2db35943fe2dc27d5cdc97f

Observation fdb902a9-dfd8-4dc9-9151-632c96718464 · outbound

This paper cites Celledoni, M.J.

Symmetry-preserving neural networks in lattice field theories Celledoni, M.J

Reference 74

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source=pdf_text observed=2026-08-07T00:58:37.830157Z digest=sha256:30783dd4e74003bde9237d901c55f9482e3cf9f077253f12f8e12737cab4680e

Observation 874723e4-83ff-4f9f-b68a-3491c1525023 · outbound

This paper cites Aronsson, Homogeneous vector bundles and G-equivariant convolutional neural networks , Sampling Theory, Signal Processing, and Data Analysis 20 (2022) 10.

Symmetry-preserving neural networks in lattice field theories Aronsson, Homogeneous vector bundles and G-equivariant convolutional neural networks , Sampling Theory, Signal Processing, and Data Analysis 20 (2022) 10

Reference 75

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source=pdf_text observed=2026-08-07T00:58:37.974704Z digest=sha256:23c2abb4364a38f84779c5a9366a97cd43a8516591b5b4e475c598ff31dddf8b

Observation 2c1e9e36-9237-425c-980e-82b4ba703c4c · outbound

This paper cites Implicit Convolutional Kernels for Steerable CNNs.

Symmetry-preserving neural networks in lattice field theories Implicit Convolutional Kernels for Steerable CNNs

Reference 76

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local_arxiv, observed 2026-08-07T00:58:48.749597Z

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source=pdf_text observed=2026-08-07T00:58:38.082000Z digest=sha256:144f8e1408c447e76a931ec10ec59232c56a487fc15f4c156406a4088a0f149c

Observation a9d22d46-24bb-443a-91cb-050fef95cd04 · outbound

This paper cites Hossain, S.

Symmetry-preserving neural networks in lattice field theories Hossain, S

Reference 77

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Observation c5167c25-72ff-4665-b17b-a5b812c7f66b · outbound

This paper cites Edixhoven, A.

Symmetry-preserving neural networks in lattice field theories Edixhoven, A

Reference 78

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Observation 4a37f704-b90f-478a-932a-a6cca03ffb7b · outbound

This paper cites Gauge Equivariant Convolutional Networks and the Icosahedral CNN.

Symmetry-preserving neural networks in lattice field theories Gauge Equivariant Convolutional Networks and the Icosahedral CNN

Reference 79

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Observation 8fd83f4f-0234-462c-8143-2198470cd760 · outbound

This paper cites Gauge equivariant neural networks for quantum lattice gauge theories.

Symmetry-preserving neural networks in lattice field theories Gauge equivariant neural networks for quantum lattice gauge theories

Reference 80

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Observation d5564258-a6d8-453f-b747-83a7863a8c00 · outbound

This paper cites Generalizing Convolutional Neural Networks for Equivariance to Lie Groups on Arbitrary Continuous Data.

Symmetry-preserving neural networks in lattice field theories Generalizing Convolutional Neural Networks for Equivariance to Lie Groups on Arbitrary Continuous Data

Reference 81

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Observation 54b21998-f4ee-46f9-a468-272ce22d9648 · outbound

This paper cites Gauge covariant neural network for quarks and gluons.

Symmetry-preserving neural networks in lattice field theories Gauge covariant neural network for quarks and gluons

Reference 82

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source=pdf_text observed=2026-08-07T00:58:38.742479Z digest=sha256:c9e4494d7929c38da721fa72d3669a5f583e86e4b605563447cbc10b2a325921

Observation fe9fcd2d-e8b2-4b80-b215-6e34b64cd9bf · outbound

This paper cites Geometrical aspects of lattice gauge equivariant convolutional neural networks.

Symmetry-preserving neural networks in lattice field theories Geometrical aspects of lattice gauge equivariant convolutional neural networks

Reference 83

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Observation 2f35d0df-3e3a-4361-83b3-6b128bd7ca97 · outbound

This paper cites Gauge-equivariant neural networks as preconditioners in lattice QCD.

Symmetry-preserving neural networks in lattice field theories Gauge-equivariant neural networks as preconditioners in lattice QCD

Reference 84

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source=pdf_text observed=2026-08-07T00:58:38.966083Z digest=sha256:e1316a72735511218549564622a688d8369175cbf6266373ef9149a941194ca4

Observation 6687f949-d2c8-45e9-9818-d912e7c8efca · outbound

This paper cites Gauge-equivariant pooling layers for preconditioners in lattice QCD.

Symmetry-preserving neural networks in lattice field theories Gauge-equivariant pooling layers for preconditioners in lattice QCD

Reference 85

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source=pdf_text observed=2026-08-07T00:58:39.066099Z digest=sha256:958f0c5ffbc680fa1fdbe75ba968fd3a470fb531399a21bff492bd68048392ba

Observation ad41b40e-5761-496d-8c4e-76d2402bf201 · outbound

This paper cites Machine learning a fixed point action for SU(3) gauge theory with a gauge equivariant convolutional neural network.

Symmetry-preserving neural networks in lattice field theories Machine learning a fixed point action for SU(3) gauge theory with a gauge equivariant convolutional neural network

Reference 86

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source=pdf_text observed=2026-08-07T00:58:39.161490Z digest=sha256:bf0685c12e651e22896001e1cd51792a05ca7cd2fc132990b0e47cf4f0139c61

Observation 61d0d7e6-eb6b-40d1-afcc-37543ed99ffe · outbound

This paper cites Self-learning Monte Carlo with equivariant Transformer.

Symmetry-preserving neural networks in lattice field theories Self-learning Monte Carlo with equivariant Transformer

Reference 87

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Observation e78611ab-c050-450a-83dc-983e11c069ff · outbound

This paper cites Equivariant Transformer is all you need.

Symmetry-preserving neural networks in lattice field theories Equivariant Transformer is all you need

Reference 88

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Observation 2dccc692-8348-4d20-bb71-ebb0a971e76a · outbound

This paper cites Equivariant flow-based sampling for lattice gauge theory.

Symmetry-preserving neural networks in lattice field theories Equivariant flow-based sampling for lattice gauge theory

Reference 89

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Observation 22bbe7c8-951a-414d-83c6-4527af939172 · outbound

This paper cites Sampling using $SU(N)$ gauge equivariant flows.

Symmetry-preserving neural networks in lattice field theories Sampling using $SU(N)$ gauge equivariant flows

Reference 90

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source=pdf_text observed=2026-08-07T00:58:39.563726Z digest=sha256:c083128847189894e52d87021456e05e3ad0a67d9c8a736ca2e3fcb59fd26a43

Observation bee0ecda-749c-45f8-bc96-c439fa356d33 · outbound

This paper cites Flow-based sampling for fermionic lattice field theories.

Symmetry-preserving neural networks in lattice field theories Flow-based sampling for fermionic lattice field theories

Reference 91

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source=pdf_text observed=2026-08-07T00:58:39.653396Z digest=sha256:0ce72894ff111f7eb7bc5a414b84c70448200c5a83d654290d6f8b82c5cea9a9

Observation 8ca5e41b-85de-490f-91ef-67269462b63e · outbound

This paper cites Abbott, M.S.

Symmetry-preserving neural networks in lattice field theories Abbott, M.S

Reference 92

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source=pdf_text observed=2026-08-07T00:58:39.778568Z digest=sha256:5a03e7aefe97c69bf50b8e874f68af75821ce2a89125639dc03ab042a5190590

Observation f29e81e7-1bbe-4dd9-a07f-1fc3ac3c69fc · outbound

This paper cites Detecting and Mitigating Mode-Collapse for Flow-based Sampling of Lattice Field Theories.

Symmetry-preserving neural networks in lattice field theories Detecting and Mitigating Mode-Collapse for Flow-based Sampling of Lattice Field Theories

Reference 93

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Observation 484cbad4-9966-4769-9dd8-f424d441b0b8 · outbound

This paper cites Bacchio, P.

Symmetry-preserving neural networks in lattice field theories Bacchio, P

Reference 94

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source=pdf_text observed=2026-08-07T00:58:39.999839Z digest=sha256:fc383d271810f0bc2ed54c0265a0458a3a88023f62488b41c12c7854ea221adf

Observation 7e7602ed-126a-443b-8a39-8b85856f6efe · outbound

This paper cites Remarks on relativistic scalar models with chemical potential.

Symmetry-preserving neural networks in lattice field theories Remarks on relativistic scalar models with chemical potential

Reference 95

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Observation 482383b5-09ab-4a8b-aa41-a39155afff75 · outbound

This paper cites Lattice study of the Silver Blaze phenomenon for a charged scalar phi-4 field.

Symmetry-preserving neural networks in lattice field theories Lattice study of the Silver Blaze phenomenon for a charged scalar phi-4 field

Reference 96

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Observation bd37881e-4c23-493e-809b-dbfc42af8789 · outbound

This paper cites Angulu, J.R.

Symmetry-preserving neural networks in lattice field theories Angulu, J.R

Reference 97

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Observation 7e14b243-91c0-4347-b549-4bc8651d8b25 · outbound

This paper cites PyTorch: An Imperative Style, High-Performance Deep Learning Library.

Symmetry-preserving neural networks in lattice field theories PyTorch: An Imperative Style, High-Performance Deep Learning Library

Reference 98

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source=pdf_text observed=2026-08-07T00:58:40.485614Z digest=sha256:4f72f4253c0e4871c881f958746a1865a858f2e8c1929e59583d131234f04af8

Observation f9b45110-26b5-4486-a192-81e633426773 · outbound

This paper cites Bozinovski, Reminder of the First Paper on Transfer Learning in Neural Networks, 1976 , Informatica (Slovenia) 44 (2020) 291.

Symmetry-preserving neural networks in lattice field theories Bozinovski, Reminder of the First Paper on Transfer Learning in Neural Networks, 1976 , Informatica (Slovenia) 44 (2020) 291

Reference 99

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source=pdf_text observed=2026-08-07T00:58:40.608054Z digest=sha256:1358ed1d73d9c7dd53a38c66994ca2891acf63f2c1a32a75ee09abe3e36db2ef

Observation 3a66b026-ef51-4c1b-8b54-7abbd4a02adb · outbound

This paper cites Multi-Scale Context Aggregation by Dilated Convolutions.

Symmetry-preserving neural networks in lattice field theories Multi-Scale Context Aggregation by Dilated Convolutions

Reference 100

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source=pdf_text observed=2026-08-07T00:58:40.750257Z digest=sha256:7897125b938f9b47226cca4f35f392daaf3238145549ad553c56f29fbbb2a7ff

Pith citing papers

Observation 0717c9e9-9b8f-402b-958b-ab9354c56281 · inbound

Quantum-Inspired Vision: Leveraging Wave-Particle Duality for Low-Illumination Enhancement cites this paper.

Quantum-Inspired Vision: Leveraging Wave-Particle Duality for Low-Illumination Enhancement Symmetry-preserving neural networks in lattice field theories

Reference 11

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arxiv_id, observed 2026-07-03T04:27:36.145348Z

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