Paper Citation Record · LEDGER

Improving KAN with CDF normalization to quantiles

As of 13 August 2026, this Paper Citation Record lists 12 of 12 outbound references and 1 inbound Pith citation observation for arXiv:2507.13393.

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

pith.paper-citation-record.v1
2507.13393 v1

Coverage vector

measured 12 of 12 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T16:57:45.110988Z

measured 13 of 13 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-24T00:56:54.913435Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-05-24T00:58:40.460650Z

Reference resolution

12 of 12 outbound references displayed

verified exact0
verified fuzzy4
unresolved8
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 50a6a32c-0e6f-4b32-8149-04b54b85f3da · outbound

This paper cites Copula theory: an introduction,.

Improving KAN with CDF normalization to quantiles Copula theory: an introduction,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:57:45.569017Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation d78233a0-e5fd-4593-b7c8-2906f9cc50f2 · outbound

This paper cites Biology-inspired joint distribution neurons based on Hierarchical Correlation Reconstruction allowing for multidirectional propagation of values and densities.

Improving KAN with CDF normalization to quantiles Biology-inspired joint distribution neurons based on Hierarchical Correlation Reconstruction allowing for multidirectional propagation of values and densities

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-06T16:57:44.305400Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 34bd6c4d-d542-4f09-9adf-eacd0f304ce4 · outbound

This paper cites Legendre-kan: High accuracy ka network based on legendre polynomials.

Improving KAN with CDF normalization to quantiles Legendre-kan: High accuracy ka network based on legendre polynomials

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:57:45.499581Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 8e06de40-8304-4a1d-ba10-a2481d9a3051 · outbound

This paper cites Batch normalization: Accelerating deep network training by reducing internal covariate shift,.

Improving KAN with CDF normalization to quantiles Batch normalization: Accelerating deep network training by reducing internal covariate shift,

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-06T16:57:44.491302Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 302f1b0e-8cfd-4927-bff1-269dcf829083 · outbound

This paper cites Hierarchical correlation reconstruction with missing data, for example for biology-inspired neuron.

Improving KAN with CDF normalization to quantiles Hierarchical correlation reconstruction with missing data, for example for biology-inspired neuron

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-06T16:57:44.583588Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a699732a-f6d5-4676-b7e0-a194e7df7103 · outbound

This paper cites KAN: Kolmogorov-Arnold Networks.

Improving KAN with CDF normalization to quantiles KAN: Kolmogorov-Arnold Networks

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-06T16:57:44.673069Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fc87f5bc-9dee-48cd-a4a9-4a7f2e9ea4e0 · outbound

This paper cites Kolmogorov, On the representation of continuous functions of several variables by superpositions of continuous functions of a smaller number of variables.

Improving KAN with CDF normalization to quantiles Kolmogorov, On the representation of continuous functions of several variables by superpositions of continuous functions of a smaller number of variables

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:57:45.417299Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation fa4e3036-a4e3-4a31-b70d-521e0740226d · outbound

This paper cites The kolmogorov–arnold representation theorem revis- ited,.

Improving KAN with CDF normalization to quantiles The kolmogorov–arnold representation theorem revis- ited,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:57:45.307667Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 2eed890c-a0c4-4d44-b145-04bf96f0ad67 · outbound

This paper cites Chebyshev Polynomial-Based Kolmogorov-Arnold Networks: An Efficient Architecture for Nonlinear Function Approximation.

Improving KAN with CDF normalization to quantiles Chebyshev Polynomial-Based Kolmogorov-Arnold Networks: An Efficient Architecture for Nonlinear Function Approximation

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-06T16:57:44.909306Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ff69a007-4d9c-46e3-9103-e8e9715255ce · outbound

This paper cites Exploring the Potential of Polynomial Basis Functions in Kolmogorov-Arnold Networks: A Comparative Study of Different Groups of Polynomials.

Improving KAN with CDF normalization to quantiles Exploring the Potential of Polynomial Basis Functions in Kolmogorov-Arnold Networks: A Comparative Study of Different Groups of Polynomials

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-06T16:57:45.003762Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2c06c64b-54d1-4ce0-a0e5-6c1dd2f33e9f · outbound

This paper cites Rapid parametric density estimation.

Improving KAN with CDF normalization to quantiles Rapid parametric density estimation

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-06T16:57:45.064130Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2f27ddd4-455b-4b6d-aab4-3814d9a020e8 · outbound

This paper cites The information bottleneck method.

Improving KAN with CDF normalization to quantiles The information bottleneck method

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-06T16:57:45.110988Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation 21b93300-1049-4e39-ac81-a3caef104144 · inbound

Biology-inspired joint distribution neurons based on Hierarchical Correlation Reconstruction allowing for multidirectional propagation of values and densities cites this paper.

Biology-inspired joint distribution neurons based on Hierarchical Correlation Reconstruction allowing for multidirectional propagation of values and densities Improving KAN with CDF normalization to quantiles

Reference 21

Resolution: verified exact; arxiv_id, observed 2026-05-24T00:58:40.463984Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.