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Paper Citation Record · LEDGER

Neural Phase Correlation

As of 9 August 2026, this Paper Citation Record lists 40 of 40 outbound references and 0 inbound Pith citation observations for arXiv:2606.18496.

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

pith.paper-citation-record.v1
2606.18496 v1

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measured 40 of 40 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-06-27T00:48:04.518476Z

measured 40 of 40 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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Reference resolution

40 of 40 outbound references displayed

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  • verified fuzzy0
  • unresolved39
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

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Outbound references

Observation b873f566-abe4-4a51-aab8-cb45dcf55958 · outbound

This paper cites an unresolved cited work.

Neural Phase Correlation Unresolved cited work

Reference 1

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Observation a357efe5-4b6d-4ca9-a5f5-9e6b91c9d4e0 · outbound

This paper cites R2D2: Reliable and repeatable detector and descriptor.

Neural Phase Correlation R2D2: Reliable and repeatable detector and descriptor

Reference 2

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Observation 35a2d209-fc5b-4426-b311-05fc16224fcc · outbound

This paper cites SuperGlue: Learning feature matching with graph neural networks.

Neural Phase Correlation SuperGlue: Learning feature matching with graph neural networks

Reference 3

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Observation b7d37c5e-99d8-41c4-be04-42a392dfdb72 · outbound

This paper cites RoMa: Robust dense feature matching.

Neural Phase Correlation RoMa: Robust dense feature matching

Reference 4

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Observation c133685a-be77-44bd-8304-0f6557fa56d8 · outbound

This paper cites FlowNet: Learning optical flow with convolutional networks.

Neural Phase Correlation FlowNet: Learning optical flow with convolutional networks

Reference 5

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source=pdf_text observed=2026-06-27T00:48:04.518476Z digest=sha256:30dd02ef1c6c9f4f685eebfc2fd8553f5eb95f7a52e4b3edbb1b577f96b4c823

Observation febde964-394b-42f3-a173-c298a8457323 · outbound

This paper cites PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume.

Neural Phase Correlation PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume

Reference 6

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source=pdf_text observed=2026-06-27T00:48:04.518476Z digest=sha256:9b02ba493e7daeb635422527f5448232951e392b95a813564cb5b54527b7716f

Observation bc043399-09c0-4231-8bcd-4cb155d7d0bb · outbound

This paper cites RAFT: Recurrent all-pairs field transforms for optical flow.

Neural Phase Correlation RAFT: Recurrent all-pairs field transforms for optical flow

Reference 7

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source=pdf_text observed=2026-06-27T00:48:04.518476Z digest=sha256:0f0d20aa3d35da24f779ab0c8950010b55092333eaa63f6ba301d99923c9191c

Observation e285b3df-2887-442e-b83f-d07ad5ed59fe · outbound

This paper cites LoFTR: Detector-free local feature matching with transformers.

Neural Phase Correlation LoFTR: Detector-free local feature matching with transformers

Reference 8

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source=pdf_text observed=2026-06-27T00:48:04.518476Z digest=sha256:571264c0b1c574705c14c40904a114d00c3115ad80802fd0e7513ac87ab4379e

Observation bcefd37f-344f-45fb-afcb-43d8bbcfd37b · outbound

This paper cites Sabuncu, John Guttag, and Adrian V.

Neural Phase Correlation Sabuncu, John Guttag, and Adrian V

Reference 9

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source=pdf_text observed=2026-06-27T00:48:04.518476Z digest=sha256:b00dd49991669b6ba9d2fab849f23311f6a02fd3c85f54a0a764d431b9aead70

Observation 1631db01-18ca-404b-8eda-e81a6128a0dd · outbound

This paper cites Segars, Ye Li, and Eric C.

Neural Phase Correlation Segars, Ye Li, and Eric C

Reference 10

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source=pdf_text observed=2026-06-27T00:48:04.518476Z digest=sha256:5c3c6c94a1d6ec2cbd4875493551b6362be54c4d79d66a6b8c4a152ddfc23522

Observation 5f1dc6c1-5d54-4a78-a26c-c6cd37c24274 · outbound

This paper cites Kuglin and Donald C.

Neural Phase Correlation Kuglin and Donald C

Reference 11

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Observation eba3d3bd-f8d2-4fca-a517-f8f2d71c17d3 · outbound

This paper cites Cohen and Max Welling.

Neural Phase Correlation Cohen and Max Welling

Reference 12

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source=pdf_text observed=2026-06-27T00:48:04.518476Z digest=sha256:b36d3b94cce06c486df6da7de72f3e4b452e9ffbabe7270dce7983199f0eae9c

Observation 219afe1f-d84f-4ca5-9f0b-b841ac0904ac · outbound

This paper cites Worrall, Stephan J.

Neural Phase Correlation Worrall, Stephan J

Reference 13

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source=pdf_text observed=2026-06-27T00:48:04.518476Z digest=sha256:1c974ae250154ef897c00693ec588d11614f2ebcf0ed41efca6aa77fc496a964

Observation 4b4c7ea6-dfbc-45b6-939a-900ecd173ff4 · outbound

This paper cites General E(2)-equivariant steerable CNNs.

Neural Phase Correlation General E(2)-equivariant steerable CNNs

Reference 14

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source=pdf_text observed=2026-06-27T00:48:04.518476Z digest=sha256:7d80c3ab71ead39cd7f2bcafb23ddcf69f378dcdfe444783db1d48b13d9da3e3

Observation cbfdcaa6-d698-47a3-a570-b0bba5ef5802 · outbound

This paper cites Cohen, Mario Geiger, and Maurice Weiler.

Neural Phase Correlation Cohen, Mario Geiger, and Maurice Weiler

Reference 15

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Observation 3b7de31c-71db-40c5-9ed5-e7af3b50195c · outbound

This paper cites de V os, Floris F.

Neural Phase Correlation de V os, Floris F

Reference 16

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Observation d469c216-3e6d-4cf9-80d7-8a40497ffa49 · outbound

This paper cites Stendahl, Lawrence H.

Neural Phase Correlation Stendahl, Lawrence H

Reference 17

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source=pdf_text observed=2026-06-27T00:48:04.518476Z digest=sha256:caabea030119c1fc3c1c7db41efb6c6f9f932c0debccffd5d0454b4264c7a30a

Observation e7eaad24-c448-4939-bf06-31c5b6bae66e · outbound

This paper cites U-Net vs transformer: Is U-Net outdated in medical image registration? InMedical Image Computing and Computer-Assisted Intervention (MICCAI), 2022.

Neural Phase Correlation U-Net vs transformer: Is U-Net outdated in medical image registration? InMedical Image Computing and Computer-Assisted Intervention (MICCAI), 2022

Reference 18

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Observation 61b47396-0d52-47d7-a047-4c8dc9d92519 · outbound

This paper cites MambaMorph: a Mamba-based Framework for Medical MR-CT Deformable Registration.

Neural Phase Correlation MambaMorph: a Mamba-based Framework for Medical MR-CT Deformable Registration

Reference 19

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Observation 471b1b1a-6ebe-4221-9877-2be553d4482b · outbound

This paper cites Decoder-only image registration.IEEE Transactions on Medical Imaging, 2024.

Neural Phase Correlation Decoder-only image registration.IEEE Transactions on Medical Imaging, 2024

Reference 20

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Observation 89066ba7-a9e8-4e22-9f9f-90636ee6065e · outbound

This paper cites and Steven L.

Neural Phase Correlation and Steven L

Reference 21

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Observation 5abfd0cf-cda5-4f89-a263-9afe8e2f934f · outbound

This paper cites Learning deep neural network representations for Koopman operators of nonlinear dynamical systems, 2017.

Neural Phase Correlation Learning deep neural network representations for Koopman operators of nonlinear dynamical systems, 2017

Reference 22

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Observation 10e21fbc-a8bc-4d58-b890-2a8befe119e8 · outbound

This paper cites Solving the quantum many-body problem with artificial neural networks.Science, 355(6325):602–606, 2017.

Neural Phase Correlation Solving the quantum many-body problem with artificial neural networks.Science, 355(6325):602–606, 2017

Reference 23

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Observation 8a409c5a-5915-4638-9998-7152e925417a · outbound

This paper cites Physics-informed neural networks for quantum eigenvalue problems, 2022.

Neural Phase Correlation Physics-informed neural networks for quantum eigenvalue problems, 2022

Reference 24

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Observation 636a26e0-63b9-45aa-9ce7-675ce8e34ab6 · outbound

This paper cites Solving the quantum many-body Hamiltonian learning problem with neural differential equations, 2024.

Neural Phase Correlation Solving the quantum many-body Hamiltonian learning problem with neural differential equations, 2024

Reference 25

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Observation 6ee81207-d207-4a4a-8982-53a3937d34f8 · outbound

This paper cites an unresolved cited work.

Neural Phase Correlation Unresolved cited work

Reference 26

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Observation dc27784c-28f2-4654-b9ff-6b87e9223cc6 · outbound

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Neural Phase Correlation Unresolved cited work

Reference 27

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Observation 0e123675-de1a-4eb5-8262-18d01b137257 · outbound

This paper cites On multi-view feature learning.

Neural Phase Correlation On multi-view feature learning

Reference 28

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Observation 4eb43202-2e82-48c9-8eda-6e6151f0af32 · outbound

This paper cites Learning to relate images.IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(8):1829–1846, 2013.

Neural Phase Correlation Learning to relate images.IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(8):1829–1846, 2013

Reference 29

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Observation ad6f8670-bfe7-4267-9143-248897ed7912 · outbound

This paper cites Differentiable top-k with optimal transport.

Neural Phase Correlation Differentiable top-k with optimal transport

Reference 30

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Observation 86ca5b78-51da-4107-b45c-da7c1c246e4a · outbound

This paper cites A log-Euclidean framework for statistics on diffeomorphisms.

Neural Phase Correlation A log-Euclidean framework for statistics on diffeomorphisms

Reference 31

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Observation 31f5b043-60d9-4b39-8b83-18a47764f447 · outbound

This paper cites Deep learning techniques for automatic MRI cardiac multi-structures segmentation and diagnosis: Is the problem solved?IEEE Transactions on Medical Imaging, 37(11):2514–2525, 2018.

Neural Phase Correlation Deep learning techniques for automatic MRI cardiac multi-structures segmentation and diagnosis: Is the problem solved?IEEE Transactions on Medical Imaging, 37(11):2514–2525, 2018

Reference 32

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Observation c312e85c-3f88-4a5d-acc0-74a3633971ab · outbound

This paper cites Disentangling progress in medical image registration: Beyond trend- driven architectures towards domain-specific strategies.Medical Image Analysis, 2025.

Neural Phase Correlation Disentangling progress in medical image registration: Beyond trend- driven architectures towards domain-specific strategies.Medical Image Analysis, 2025

Reference 33

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Observation ab4c9443-35c2-4309-951d-af87e3724fa3 · outbound

This paper cites Deep learning for segmentation using an open large-scale dataset in 2D echocardiography.IEEE Transactions on Medical Imaging, 38(9):2198–2210, 2019.

Neural Phase Correlation Deep learning for segmentation using an open large-scale dataset in 2D echocardiography.IEEE Transactions on Medical Imaging, 38(9):2198–2210, 2019

Reference 34

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Observation 9f7c4c55-460d-4e3a-b231-982fa21fb376 · outbound

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Neural Phase Correlation Unresolved cited work

Reference 35

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Observation 21af047c-6314-4a43-baca-62ebd58862e9 · outbound

This paper cites Srinivasan, Matthew Tancik, Jonathan T.

Neural Phase Correlation Srinivasan, Matthew Tancik, Jonathan T

Reference 36

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Observation 590249db-7119-471f-8e2b-5b5bdc1ecb51 · outbound

This paper cites Marcus, Tracy H.

Neural Phase Correlation Marcus, Tracy H

Reference 37

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Observation 71fc1bdc-6e4f-4378-b3f0-7a4ee1ad613f · outbound

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Neural Phase Correlation Unresolved cited work

Reference 38

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Observation 5a36c8ee-c5cc-4f61-87ab-8b775bc63c07 · outbound

This paper cites permutation matrices are orthogonal.

Neural Phase Correlation permutation matrices are orthogonal

Reference 39

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source=pdf_text observed=2026-06-27T00:48:04.518476Z digest=sha256:87c088e5104cfc4d8c348cd2eaa8929efab778cd57d87192c21511fa6ac847bc

Observation e342eab3-56b1-45b6-9bc5-36ba27ccc915 · outbound

This paper cites C.2 Stage 2: Bilinear interaction on filter responses Classical phase correlation forms the cross-power spectrum ˆy(k)·ˆx(k)by multiplying per-frequency Fourier coefficients.

Neural Phase Correlation C.2 Stage 2: Bilinear interaction on filter responses Classical phase correlation forms the cross-power spectrum ˆy(k)·ˆx(k)by multiplying per-frequency Fourier coefficients

Reference 40

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Pith citing papers

No inbound Pith citation observations are available.