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Paper Citation Record · LEDGER

Kernel $k$-Medoids as General Vector Quantization

As of 22 August 2026, this Paper Citation Record lists 40 of 40 outbound references and 0 inbound Pith citation observations for arXiv:2506.04786.

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

pith.paper-citation-record.v1
2506.04786 v2

Coverage vector

measured 40 of 40 reference resolution

Typed states for the displayed outbound observations.

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measured 40 of 40 standing notices

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measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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Source: cited_works

Reference resolution

40 of 40 outbound references displayed

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External citation measurements

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Outbound references

Observation b0819eda-49e8-4d68-8d7b-851aba49451d · outbound

This paper cites Vector quantization and signal compression.

Kernel $k$-Medoids as General Vector Quantization Vector quantization and signal compression

Reference 1

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Observation 7afaaa5d-5546-4570-9026-cb77338de818 · outbound

This paper cites Learning vec- tor quantization.

Kernel $k$-Medoids as General Vector Quantization Learning vec- tor quantization

Reference 2

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This paper cites The coming of age of inter- pretable and explainable machine learning models.

Kernel $k$-Medoids as General Vector Quantization The coming of age of inter- pretable and explainable machine learning models

Reference 3

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Observation a49fadce-ee92-4310-b653-15cdca0f67fb · outbound

This paper cites The importance of interpretability and visualization in machine learning for applications in medicine and health care.

Kernel $k$-Medoids as General Vector Quantization The importance of interpretability and visualization in machine learning for applications in medicine and health care

Reference 4

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Observation 02b44718-dd6b-4daa-bd97-998a4ae40756 · outbound

This paper cites Interpretable learning for self-driving cars by visualizing causal attention.

Kernel $k$-Medoids as General Vector Quantization Interpretable learning for self-driving cars by visualizing causal attention

Reference 5

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Observation 520dcd7e-f1fb-4db3-ad3f-74b0ae184604 · outbound

This paper cites Unsupervised word embeddings capture latent knowledge from materials science litera- ture.

Kernel $k$-Medoids as General Vector Quantization Unsupervised word embeddings capture latent knowledge from materials science litera- ture

Reference 6

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Observation ceed10f7-2030-49a0-a49b-dc916733d229 · outbound

This paper cites Mean shift, mode seeking, and clus- tering.

Kernel $k$-Medoids as General Vector Quantization Mean shift, mode seeking, and clus- tering

Reference 7

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Observation 986ff466-d5de-41e6-adb8-c421085b07dd · outbound

This paper cites Some methods for classification and analysis of multivariate observations.

Kernel $k$-Medoids as General Vector Quantization Some methods for classification and analysis of multivariate observations

Reference 8

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Source-reported events for the cited work

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Observation b5120fe0-38be-4243-8c94-e7ef8dd41b00 · outbound

This paper cites Least squares quantization in PCM.

Kernel $k$-Medoids as General Vector Quantization Least squares quantization in PCM

Reference 9

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Observation 5c0f0c72-954b-4922-b890-efeee4afa271 · outbound

This paper cites Clustering by means of medoids.

Kernel $k$-Medoids as General Vector Quantization Clustering by means of medoids

Reference 10

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Observation ce8b61cb-2b1f-4846-93b5-4811592b4df6 · outbound

This paper cites Infor- mation theoretic vector quantization with fixed point updates.

Kernel $k$-Medoids as General Vector Quantization Infor- mation theoretic vector quantization with fixed point updates

Reference 11

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Observation bb48c341-9790-4cfa-bdb3-79441b0d88a5 · outbound

This paper cites A reproducing kernel Hilbert space framework for information-theoretic learning.

Kernel $k$-Medoids as General Vector Quantization A reproducing kernel Hilbert space framework for information-theoretic learning

Reference 12

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Observation dbf2d65b-610f-4968-9dd3-991c7b358354 · outbound

This paper cites NP-hardness of Euclidean sum- of-squares clustering.

Kernel $k$-Medoids as General Vector Quantization NP-hardness of Euclidean sum- of-squares clustering

Reference 13

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Observation 48e0c2df-e189-44ad-bf86-67496235f6c7 · outbound

This paper cites The quadratic unconstrained bi- nary optimization problem.

Kernel $k$-Medoids as General Vector Quantization The quadratic unconstrained bi- nary optimization problem

Reference 14

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Observation ae988a80-590d-4916-9857-d0bea977daae · outbound

This paper cites Ising machines as hardware solvers of combinatorial optimization problems.

Kernel $k$-Medoids as General Vector Quantization Ising machines as hardware solvers of combinatorial optimization problems

Reference 15

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Observation af0d2025-861a-44f6-9622-835dbe07af83 · outbound

This paper cites Quantum computation and quantum information.

Kernel $k$-Medoids as General Vector Quantization Quantum computation and quantum information

Reference 16

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Observation 33d5efb5-e0ea-4c8e-a0d3-7462a3e83469 · outbound

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Kernel $k$-Medoids as General Vector Quantization Quantum annealing in the transverse Ising model

Reference 17

Resolution
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Observation 618c26ad-7a58-42f7-b0d5-76772440f3ae · outbound

This paper cites Adiabatic quan- tum computation.

Kernel $k$-Medoids as General Vector Quantization Adiabatic quan- tum computation

Reference 18

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Kernel $k$-Medoids as General Vector Quantization Quantum computing in the NISQ era and beyond

Reference 19

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Observation 9470dc2a-f948-41d7-a722-011a50200bf7 · outbound

This paper cites Towards bundle adjustment for satellite imaging via quantum machine learning.

Kernel $k$-Medoids as General Vector Quantization Towards bundle adjustment for satellite imaging via quantum machine learning

Reference 20

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Observation fa1dbee3-70cf-4c0e-9f19-101822fc2da4 · outbound

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Kernel $k$-Medoids as General Vector Quantization Quantum optimization for FPGA- placement

Reference 21

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Kernel $k$-Medoids as General Vector Quantization Optimum-preserving QUBO parameter compression

Reference 22

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Observation 906c55c7-4ec8-4937-8bcd-09510970222f · outbound

This paper cites Hybrid quantum-classical multi- agent pathfinding.

Kernel $k$-Medoids as General Vector Quantization Hybrid quantum-classical multi- agent pathfinding

Reference 23

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Observation d2e7f0a2-e88b-4134-8512-f2dc6b5aee97 · outbound

This paper cites Dynamic range reduction via branch-and-bound.

Kernel $k$-Medoids as General Vector Quantization Dynamic range reduction via branch-and-bound

Reference 24

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Observation e633b65c-a27a-4f0a-ad6c-df60267277bd · outbound

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Kernel $k$-Medoids as General Vector Quantization A QUBO formulation of the k-medoids problem

Reference 25

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This paper cites Hopfield networks for vector quantization.

Kernel $k$-Medoids as General Vector Quantization Hopfield networks for vector quantization

Reference 26

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Kernel $k$-Medoids as General Vector Quantization Quadratic binary programming with application to capital-budgeting problems

Reference 27

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This paper cites Supervised learning with quantum-enhanced feature spaces.

Kernel $k$-Medoids as General Vector Quantization Supervised learning with quantum-enhanced feature spaces

Reference 28

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation 3642202a-4154-41e3-9e2e-bd7cb42c4dd7 · outbound

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Kernel $k$-Medoids as General Vector Quantization Quantum-hybrid neural vector quantization–a mathematical approach

Reference 29

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Kernel $k$-Medoids as General Vector Quantization Steps forward to quantum learning vector quantization for classification learning on a theoretical quantum computer

Reference 30

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation e8874b61-9456-415b-8a5f-b98e52aa97b3 · outbound

This paper cites Quantum-Inspired Learning Vector Quantization for Classification Learning.

Kernel $k$-Medoids as General Vector Quantization Quantum-Inspired Learning Vector Quantization for Classification Learning

Reference 31

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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This paper cites Quantum ap- proaches for medoid clustering.

Kernel $k$-Medoids as General Vector Quantization Quantum ap- proaches for medoid clustering

Reference 32

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation 5b348129-3adc-4cb6-bbe6-ee6a9dc22222 · outbound

This paper cites Quantum-ready vector quantization: Prototype learning as a binary optimization problem.

Kernel $k$-Medoids as General Vector Quantization Quantum-ready vector quantization: Prototype learning as a binary optimization problem

Reference 33

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation 6ba07b48-08c7-42a2-bf5d-3eac877a1001 · outbound

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Kernel $k$-Medoids as General Vector Quantization On the” probable error

Reference 34

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation 91497bee-2a4d-4023-a4bf-8de42b9222c1 · outbound

This paper cites Alleviating the quantum Big-$M$ problem.

Kernel $k$-Medoids as General Vector Quantization Alleviating the quantum Big-$M$ problem

Reference 35

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local_arxiv, observed 2026-08-07T10:40:20.041682Z

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation 998f79e0-7077-4b51-a232-a06ca650f4f9 · outbound

This paper cites The Cauchy–Schwarz divergence and Parzen windowing: Connections to graph theory and Mercer kernels.

Kernel $k$-Medoids as General Vector Quantization The Cauchy–Schwarz divergence and Parzen windowing: Connections to graph theory and Mercer kernels

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T10:40:20.203795Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-07T10:40:19.938596Z digest=sha256:bde42ae6a1c0f5b31d2cca806f2642741fdbec5afe6b39b1b688df86b0a6d096

Observation 3f0e5650-bbab-4bd1-9d30-25af3d954efd · outbound

This paper cites On informa- tion and sufficiency.

Kernel $k$-Medoids as General Vector Quantization On informa- tion and sufficiency

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T10:40:20.161896Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-07T10:40:19.943950Z digest=sha256:d77f278ca6d4b5a6138ba5c22e87f0ef622ae627bef3e59c2a7270f18378f9e2

Observation fed95792-4176-49a6-81ed-fff916546bd6 · outbound

This paper cites Vector quantization using information theoretic concepts.

Kernel $k$-Medoids as General Vector Quantization Vector quantization using information theoretic concepts

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T10:40:20.126466Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-07T10:40:19.949326Z digest=sha256:1f2a825ece20c02b0c8fbedfbf8b1412351207f54374da4f40a53a5eb0497097

Observation b453efed-472e-4ce0-80f8-b6ed02d174d7 · outbound

This paper cites Functions of positive and negative type, and their connection the theory of integral equations.

Kernel $k$-Medoids as General Vector Quantization Functions of positive and negative type, and their connection the theory of integral equations

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T10:40:20.095588Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-07T10:40:19.959427Z digest=sha256:371483ee85458d99b6009915bb9def6878571ed20b668cf5a266084dd89d8046

Observation 28848596-947f-4178-bb1b-1928d11a97a4 · outbound

This paper cites A kernel two-sample test.

Kernel $k$-Medoids as General Vector Quantization A kernel two-sample test

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T10:40:20.070067Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-07T10:40:19.965986Z digest=sha256:11a44338a2f31a550fd13e89664ecad77453ec3d3d1ea0ed5fa5d476e741cd25

Pith citing papers

No inbound Pith citation observations are available.