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Paper Citation Record · LEDGER

Approximation Rates in Besov Norms and Sample-Complexity of Kolmogorov-Arnold Networks with Residual Connections

As of 17 August 2026, this Paper Citation Record lists 54 of 54 outbound references and 7 inbound Pith citation observations for arXiv:2504.15110.

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

pith.paper-citation-record.v1
2504.15110 v3

Coverage vector

measured 54 of 54 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T11:38:34.553098Z

measured 61 of 61 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 7 of 7 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T00:51:48.433653Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

54 of 54 outbound references displayed

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External citation measurements

0
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

Observation 35145108-54b8-4e48-a3f3-fd3ce5d1fe4e · outbound

This paper cites Mathematical Finance 34 , 2 (2024), 671– 735.

Approximation Rates in Besov Norms and Sample-Complexity of Kolmogorov-Arnold Networks with Residual Connections Mathematical Finance 34 , 2 (2024), 671– 735

Reference 1

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Observation c9207fcd-e055-4b1b-b4b0-63818623f8dc · outbound

This paper cites Scale-sensitive dimensions, uniform convergence, and learnability.

Approximation Rates in Besov Norms and Sample-Complexity of Kolmogorov-Arnold Networks with Residual Connections Scale-sensitive dimensions, uniform convergence, and learnability

Reference 2

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Approximation Rates in Besov Norms and Sample-Complexity of Kolmogorov-Arnold Networks with Residual Connections Unresolved cited work

Reference 3

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Observation e5308633-4d80-48b0-bc54-e9d5a4ccdd19 · outbound

This paper cites Almost linear vc dimension bounds for piecewise polynomial networks.

Approximation Rates in Besov Norms and Sample-Complexity of Kolmogorov-Arnold Networks with Residual Connections Almost linear vc dimension bounds for piecewise polynomial networks

Reference 4

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Observation 32fd85b7-3dbc-440f-8c02-d07c6c1c6a19 · outbound

This paper cites L., Kulkarni, S.

Approximation Rates in Besov Norms and Sample-Complexity of Kolmogorov-Arnold Networks with Residual Connections L., Kulkarni, S

Reference 5

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Observation 352b1b27-4e89-4257-b9c3-e1fc44f30f59 · outbound

This paper cites Simultaneous approximation of a smooth function and its derivatives by deep neural networks with piecewise-polynomial activations.

Approximation Rates in Besov Norms and Sample-Complexity of Kolmogorov-Arnold Networks with Residual Connections Simultaneous approximation of a smooth function and its derivatives by deep neural networks with piecewise-polynomial activations

Reference 6

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Observation c57aaa4f-cbd3-4213-b360-e74231e7aaf0 · outbound

This paper cites an unresolved cited work.

Approximation Rates in Besov Norms and Sample-Complexity of Kolmogorov-Arnold Networks with Residual Connections Unresolved cited work

Reference 7

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Observation b2275d5b-9322-4b60-a77f-dfcaceccc146 · outbound

This paper cites Extending sobolev func- tions with partially vanishing traces from locally (ε,δ)-domains and applications to mixed boundary problems.

Approximation Rates in Besov Norms and Sample-Complexity of Kolmogorov-Arnold Networks with Residual Connections Extending sobolev func- tions with partially vanishing traces from locally (ε,δ)-domains and applications to mixed boundary problems

Reference 8

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Observation ccfc780b-4e7d-4a7f-8a58-e5ab5ac4c9b8 · outbound

This paper cites Efficient approximation of high- dimensional functions with neural networks.

Approximation Rates in Besov Norms and Sample-Complexity of Kolmogorov-Arnold Networks with Residual Connections Efficient approximation of high- dimensional functions with neural networks

Reference 9

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Observation 17673cbe-8d96-411d-95ca-82d609b24beb · outbound

This paper cites K., and Wang, J.-Z.

Approximation Rates in Besov Norms and Sample-Complexity of Kolmogorov-Arnold Networks with Residual Connections K., and Wang, J.-Z

Reference 10

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Observation 41e8aabb-d9e2-4069-ae29-ba91de2083ad · outbound

This paper cites A practical guide to splines , revised ed., vol.

Approximation Rates in Besov Norms and Sample-Complexity of Kolmogorov-Arnold Networks with Residual Connections A practical guide to splines , revised ed., vol

Reference 11

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Observation 5bda752b-80f7-47db-ac91-6628728967a0 · outbound

This paper cites On the approximation of functions by tanh neural networks.

Approximation Rates in Besov Norms and Sample-Complexity of Kolmogorov-Arnold Networks with Residual Connections On the approximation of functions by tanh neural networks

Reference 12

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Observation d9d83be8-44fa-4bca-8789-fc6db5e7e607 · outbound

This paper cites A., and Popov, V.

Approximation Rates in Besov Norms and Sample-Complexity of Kolmogorov-Arnold Networks with Residual Connections A., and Popov, V

Reference 13

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Observation 1e6ffee6-f95a-493d-9869-5e2b4e3cbf72 · outbound

This paper cites A., and Sharpley, R.

Approximation Rates in Besov Norms and Sample-Complexity of Kolmogorov-Arnold Networks with Residual Connections A., and Sharpley, R

Reference 14

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Observation e6124d21-1816-44f4-8d56-cd9f600062b9 · outbound

This paper cites Simultaneously solving fbsdes with neural operators of logarithmic depth, constant width, and sub-linear rank.

Approximation Rates in Besov Norms and Sample-Complexity of Kolmogorov-Arnold Networks with Residual Connections Simultaneously solving fbsdes with neural operators of logarithmic depth, constant width, and sub-linear rank

Reference 15

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 113f79d4-c5de-4997-99bb-bc3dc6fd0008 · outbound

This paper cites Approxima- tion spaces of deep neural networks.

Approximation Rates in Besov Norms and Sample-Complexity of Kolmogorov-Arnold Networks with Residual Connections Approxima- tion spaces of deep neural networks

Reference 16

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Observation dff3950e-fc10-4c8f-8d65-1499e96f10a2 · outbound

This paper cites Deep residual learning for image recognition.

Approximation Rates in Besov Norms and Sample-Complexity of Kolmogorov-Arnold Networks with Residual Connections Deep residual learning for image recognition

Reference 17

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Observation a405ba5a-2fdb-42e6-a8f7-9b955fa2a6ca · outbound

This paper cites Minimum Width for Deep, Narrow MLP: A Diffeomorphism Approach.

Approximation Rates in Besov Norms and Sample-Complexity of Kolmogorov-Arnold Networks with Residual Connections Minimum Width for Deep, Narrow MLP: A Diffeomorphism Approach

Reference 18

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Observation f6fe8c3e-eb13-43bf-a7db-2aba8dcc4099 · outbound

This paper cites Smoothness spaces of higher order on lower dimen- sional subsets of the Euclidean space.

Approximation Rates in Besov Norms and Sample-Complexity of Kolmogorov-Arnold Networks with Residual Connections Smoothness spaces of higher order on lower dimen- sional subsets of the Euclidean space

Reference 19

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Observation 9e574aa1-5b12-45a0-a209-3ebd9198f1b2 · outbound

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Approximation Rates in Besov Norms and Sample-Complexity of Kolmogorov-Arnold Networks with Residual Connections Unresolved cited work

Reference 20

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Observation 29f8e6df-de03-4a7b-9659-954479660820 · outbound

This paper cites Z., and Yang, Y.

Approximation Rates in Besov Norms and Sample-Complexity of Kolmogorov-Arnold Networks with Residual Connections Z., and Yang, Y

Reference 21

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Observation e3009bd6-b034-4b07-86be-9824cc8be844 · outbound

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Approximation Rates in Besov Norms and Sample-Complexity of Kolmogorov-Arnold Networks with Residual Connections Unresolved cited work

Reference 22

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Observation f66b033a-15f0-455c-92c8-7ae36bbd144e · outbound

This paper cites Sur le th´ eor` eme de superposition de Kolmogorov.

Approximation Rates in Besov Norms and Sample-Complexity of Kolmogorov-Arnold Networks with Residual Connections Sur le th´ eor` eme de superposition de Kolmogorov

Reference 23

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Observation be0a87c2-969d-4077-83e2-59ef55b1aeb3 · outbound

This paper cites Polynomial bounds for vc dimension of sigmoidal and general pfaffian neural networks.

Approximation Rates in Besov Norms and Sample-Complexity of Kolmogorov-Arnold Networks with Residual Connections Polynomial bounds for vc dimension of sigmoidal and general pfaffian neural networks

Reference 24

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This paper cites Universal approximation with deep narrow networks.

Approximation Rates in Besov Norms and Sample-Complexity of Kolmogorov-Arnold Networks with Residual Connections Universal approximation with deep narrow networks

Reference 25

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This paper cites Transformers are minimax optimal nonpara- metric in-context learners.

Approximation Rates in Besov Norms and Sample-Complexity of Kolmogorov-Arnold Networks with Residual Connections Transformers are minimax optimal nonpara- metric in-context learners

Reference 26

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This paper cites Universal approximation theorems for differentiable geometric deep learning.

Approximation Rates in Besov Norms and Sample-Complexity of Kolmogorov-Arnold Networks with Residual Connections Universal approximation theorems for differentiable geometric deep learning

Reference 27

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation e659a5c7-feca-4787-ac75-40e26b11844c · outbound

This paper cites Resnet with one-neuron hidden layers is a universal approx- imator.

Approximation Rates in Besov Norms and Sample-Complexity of Kolmogorov-Arnold Networks with Residual Connections Resnet with one-neuron hidden layers is a universal approx- imator

Reference 28

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 176cea7a-e45f-494b-8c4e-729890776214 · outbound

This paper cites Y., and Tegmark, M.

Approximation Rates in Besov Norms and Sample-Complexity of Kolmogorov-Arnold Networks with Residual Connections Y., and Tegmark, M

Reference 29

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation d21ff679-0d35-4ea5-9028-e68f26bc87e4 · outbound

This paper cites an unresolved cited work.

Approximation Rates in Besov Norms and Sample-Complexity of Kolmogorov-Arnold Networks with Residual Connections Unresolved cited work

Reference 30

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Observation c69fb825-1b8c-4b6d-9f04-d691b21e39cd · outbound

This paper cites W., and Xu, J.

Approximation Rates in Besov Norms and Sample-Complexity of Kolmogorov-Arnold Networks with Residual Connections W., and Xu, J

Reference 31

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Source-reported events for the cited work

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Observation f1fc2c69-1cbe-4c88-aea8-fe02b9bdddce · outbound

This paper cites N., and Micchelli, C.

Approximation Rates in Besov Norms and Sample-Complexity of Kolmogorov-Arnold Networks with Residual Connections N., and Micchelli, C

Reference 32

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation d08632c4-0208-4525-ad31-8697e708fde5 · outbound

This paper cites N., and Poggio, T.

Approximation Rates in Besov Norms and Sample-Complexity of Kolmogorov-Arnold Networks with Residual Connections N., and Poggio, T

Reference 33

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 56a8403e-4bba-4cdc-bffa-3a299134b2db · outbound

This paper cites In International Conference on Learning Representations (2021).

Approximation Rates in Besov Norms and Sample-Complexity of Kolmogorov-Arnold Networks with Residual Connections In International Conference on Learning Representations (2021)

Reference 34

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source=pdf_text observed=2026-08-16T11:38:34.259166Z digest=sha256:555f143044a6f64e06ee826a512886020c5b8a4216eb57ad2d7a4405f6f88eab

Observation 9e56a1a6-8fd8-4ce0-9f8b-0141c29b82c3 · outbound

This paper cites H., Balestriero, R., and Baraniuk, R.

Approximation Rates in Besov Norms and Sample-Complexity of Kolmogorov-Arnold Networks with Residual Connections H., Balestriero, R., and Baraniuk, R

Reference 35

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Observation 710c5431-0979-44be-952b-61702e2f9066 · outbound

This paper cites an unresolved cited work.

Approximation Rates in Besov Norms and Sample-Complexity of Kolmogorov-Arnold Networks with Residual Connections Unresolved cited work

Reference 36

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raw_fallback, observed 2026-08-16T11:38:35.947154Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation cbb084bd-bc6b-4d49-b40f-ac78d2c1c220 · outbound

This paper cites Recurrent fourier-kolmogorov arnold networks for photovoltaic power forecasting.

Approximation Rates in Besov Norms and Sample-Complexity of Kolmogorov-Arnold Networks with Residual Connections Recurrent fourier-kolmogorov arnold networks for photovoltaic power forecasting

Reference 37

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 0f078491-2329-4ff8-9d77-e7f9e6a6a27d · outbound

This paper cites an unresolved cited work.

Approximation Rates in Besov Norms and Sample-Complexity of Kolmogorov-Arnold Networks with Residual Connections Unresolved cited work

Reference 38

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raw_fallback, observed 2026-08-16T11:38:35.917008Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 682d11e1-0906-4970-b967-ce828af693b4 · outbound

This paper cites GLU Variants Improve Transformer.

Approximation Rates in Besov Norms and Sample-Complexity of Kolmogorov-Arnold Networks with Residual Connections GLU Variants Improve Transformer

Reference 39

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source=pdf_text observed=2026-08-16T11:38:34.281022Z digest=sha256:7b85e0d7bf551701e8a58943600a9e0e94796bbd0f0de505baffb0c7ab9021b0

Observation a5ed3e46-065b-4799-b863-f5841d5e4186 · outbound

This paper cites New estimates of Rychkov’s universal extension operator for Lipschitz domains and some applications.

Approximation Rates in Besov Norms and Sample-Complexity of Kolmogorov-Arnold Networks with Residual Connections New estimates of Rychkov’s universal extension operator for Lipschitz domains and some applications

Reference 40

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-16T11:38:34.386646Z digest=sha256:dce7ae93b542648989866326a13bfff4ba1e7f8339abbd7567ca938385a8960c

Observation a428cd5d-85d3-4997-b27e-e85bcb8648b2 · outbound

This paper cites Adaptivity of deep relu network for learning in besov and mixed smooth besov spaces: optimal rate and curse of dimensionality.

Approximation Rates in Besov Norms and Sample-Complexity of Kolmogorov-Arnold Networks with Residual Connections Adaptivity of deep relu network for learning in besov and mixed smooth besov spaces: optimal rate and curse of dimensionality

Reference 41

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raw_fallback, observed 2026-08-16T11:38:35.705157Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-16T11:38:34.460668Z digest=sha256:8793c805b7ae3483ecde0c26d4ca8800679c6cf10349cdc5d5e4b5f08fc9e305

Observation 7e29ca99-9c47-402a-a67d-500a0c1d5512 · outbound

This paper cites Theory of function spaces.

Approximation Rates in Besov Norms and Sample-Complexity of Kolmogorov-Arnold Networks with Residual Connections Theory of function spaces

Reference 42

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 3787832e-013e-4a30-9434-a7aca562ae29 · outbound

This paper cites W., Wellner, J.

Approximation Rates in Besov Norms and Sample-Complexity of Kolmogorov-Arnold Networks with Residual Connections W., Wellner, J

Reference 43

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no resolver link, observed 2026-08-16T11:38:34.506543Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T11:38:34.506543Z digest=sha256:81c77b94a9d56ca4a3f59787dd0acb494f61267867efa7aac02b5e276c7c12d7

Observation b52823a9-c968-4fd1-8774-8155b63d80e4 · outbound

This paper cites N., Kaiser, L., and Polosukhin, I.

Approximation Rates in Besov Norms and Sample-Complexity of Kolmogorov-Arnold Networks with Residual Connections N., Kaiser, L., and Polosukhin, I

Reference 44

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-16T11:38:34.510747Z digest=sha256:1e68ed48cd0a94bd639d14d855b462630ead019bdfbb877101fc798dd561e296

Observation f6616e1a-6fe1-46e0-97c5-163d816dadde · outbound

This paper cites Don’t fear peculiar activation functions: Euaf and beyond.

Approximation Rates in Besov Norms and Sample-Complexity of Kolmogorov-Arnold Networks with Residual Connections Don’t fear peculiar activation functions: Euaf and beyond

Reference 45

Resolution
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source=pdf_text observed=2026-08-16T11:38:34.514967Z digest=sha256:ecfae21927869c5e390d891c41a3edc5c82226ee992289ae958d1f4c544062cd

Observation 8cce19b6-92f4-4c37-8202-5d93549853d4 · outbound

This paper cites W., Liu, Z., and Hou, T.

Approximation Rates in Besov Norms and Sample-Complexity of Kolmogorov-Arnold Networks with Residual Connections W., Liu, Z., and Hou, T

Reference 46

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation b5df0aa1-e7e5-490b-affb-dc5c9328d31a · outbound

This paper cites Graph attention and kolmogorov–arnold network based smart grids intrusion detection.

Approximation Rates in Besov Norms and Sample-Complexity of Kolmogorov-Arnold Networks with Residual Connections Graph attention and kolmogorov–arnold network based smart grids intrusion detection

Reference 47

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 5dc36fcd-0b86-4f15-ae0c-2ca5bb7f4ce3 · outbound

This paper cites Optimal rates of approximation by shallow ReLU k neu- ral networks and applications to nonparametric regression.

Approximation Rates in Besov Norms and Sample-Complexity of Kolmogorov-Arnold Networks with Residual Connections Optimal rates of approximation by shallow ReLU k neu- ral networks and applications to nonparametric regression

Reference 48

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 504d3caf-6019-495d-a523-5184ab9a1a3c · outbound

This paper cites Optimal approximation of continuous functions by very deep relu net- works.

Approximation Rates in Besov Norms and Sample-Complexity of Kolmogorov-Arnold Networks with Residual Connections Optimal approximation of continuous functions by very deep relu net- works

Reference 49

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Observation 56fcd50a-3635-43bc-abe7-cb102db21cc7 · outbound

This paper cites Elementary superexpressive activations.

Approximation Rates in Besov Norms and Sample-Complexity of Kolmogorov-Arnold Networks with Residual Connections Elementary superexpressive activations

Reference 50

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raw_fallback, observed 2026-08-16T11:38:35.230612Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 7490b671-b317-4c1e-bc84-7d9486d1dfa9 · outbound

This paper cites Deep network approximation: Beyond relu to diverse activation functions.

Approximation Rates in Besov Norms and Sample-Complexity of Kolmogorov-Arnold Networks with Residual Connections Deep network approximation: Beyond relu to diverse activation functions

Reference 51

Resolution
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raw_fallback, observed 2026-08-16T11:38:35.214470Z

Source-reported events for the cited work

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source=pdf_text observed=2026-08-16T11:38:34.540140Z digest=sha256:d62673ecedcd6e13c4f9a13923cea4c65e03a4927a080256e9a4385b6949bf01

Observation ac14f46e-b154-41b9-a268-613404d3455a · outbound

This paper cites Deep network approximation: Achieving arbitrary accuracy with fixed number of neurons.

Approximation Rates in Besov Norms and Sample-Complexity of Kolmogorov-Arnold Networks with Residual Connections Deep network approximation: Achieving arbitrary accuracy with fixed number of neurons

Reference 52

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raw_fallback, observed 2026-08-16T11:38:35.199578Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-16T11:38:34.544565Z digest=sha256:302a47bf0d7228384b30f17179a6a4a720282bce3869b61fb066a15e7bf348fb

Observation bfb515a4-9b17-48ee-a624-7e4cfe2a3bc7 · outbound

This paper cites Neural network architecture beyond width and depth.

Approximation Rates in Besov Norms and Sample-Complexity of Kolmogorov-Arnold Networks with Residual Connections Neural network architecture beyond width and depth

Reference 53

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-16T11:38:34.548779Z digest=sha256:d5fe67a300e4b7920d7ec66557ee68801368e80f033bcf38a828a980abc7f950

Observation 24ad74cd-db87-40aa-96fa-bd11e62e14d0 · outbound

This paper cites Physics-informed neu- ral networks with hybrid kolmogorov-arnold network and augmented lagrangian function for solving partial differential equations.

Approximation Rates in Besov Norms and Sample-Complexity of Kolmogorov-Arnold Networks with Residual Connections Physics-informed neu- ral networks with hybrid kolmogorov-arnold network and augmented lagrangian function for solving partial differential equations

Reference 54

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-16T11:38:34.553098Z digest=sha256:7b31a622cee7c1f2befa8475ce8c2c5f7ccc0c7c4e3b3f8476d880fdd91af89d

Pith citing papers

Observation 8b318f53-9e8b-4d2b-878f-22d201137ca9 · inbound

Model Risk in Machine-Learning Distributional IV Estimation cites this paper.

Model Risk in Machine-Learning Distributional IV Estimation Approximation Rates in Besov Norms and Sample-Complexity of Kolmogorov-Arnold Networks with Residual Connections

Reference 19

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no resolver link, observed 2026-08-07T00:51:48.433653Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:51:48.433653Z digest=sha256:2b053cb7aeec4a211b02d536616ecb6550e744d35476b46926834a4107214047

Observation aabbda40-d59b-4241-ab4c-f902a58e3daf · inbound

A Practitioner's Guide to Kolmogorov-Arnold Networks cites this paper.

A Practitioner's Guide to Kolmogorov-Arnold Networks Approximation Rates in Besov Norms and Sample-Complexity of Kolmogorov-Arnold Networks with Residual Connections

Reference 281

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arxiv_id, observed 2026-05-18T03:30:50.383571Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-05-18T03:29:15.570760Z digest=sha256:bed402e7eba0e26464c1853a6d2ca7f245bebaa1154668241531e9a6a0ae6927

Observation e6c1b0b8-92ba-4a42-acb0-1fae90988fdd · inbound

Necessary and sufficient conditions for universality of Kolmogorov-Arnold networks cites this paper.

Necessary and sufficient conditions for universality of Kolmogorov-Arnold networks Approximation Rates in Besov Norms and Sample-Complexity of Kolmogorov-Arnold Networks with Residual Connections

Reference 14

Resolution
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arxiv_id, observed 2026-05-11T21:11:19.529105Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-05-08T06:27:26.340361Z digest=sha256:2afd35e9846813603c93b995c651aaead85872b709d69cd76e5ffad2ebea514b

Observation 7971fb3d-efd0-4825-96b6-8ced52bd1aac · inbound

Necessary and sufficient conditions for universality of Kolmogorov-Arnold networks cites this paper.

Necessary and sufficient conditions for universality of Kolmogorov-Arnold networks Approximation Rates in Besov Norms and Sample-Complexity of Kolmogorov-Arnold Networks with Residual Connections

Reference 14

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verified exact
arxiv_id, observed 2026-05-20T23:59:15.296038Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-05-20T23:56:01.280838Z digest=sha256:9afb9ecc574f5f2e281569892824ebb0479785e5d9128b0f936b2cb548120afd

Observation 2c9836ed-2a7e-42b2-b623-504d9dc62304 · inbound

Necessary and sufficient conditions for universality of Kolmogorov-Arnold networks cites this paper.

Necessary and sufficient conditions for universality of Kolmogorov-Arnold networks Approximation Rates in Besov Norms and Sample-Complexity of Kolmogorov-Arnold Networks with Residual Connections

Reference 14

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verified exact
arxiv_id, observed 2026-07-01T09:35:39.317155Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-07-01T09:27:26.548718Z digest=sha256:2ee90cedd3f1e94a846e456cec8ac6589e2623b0f5172afd04454bf42efdca56

Observation 5f2ce4fa-ebfe-49b9-b41b-1a2bb72680d8 · inbound

KAPLAN: Kolmogorov-Arnold Prognostic Learnable Activation Networks for Survival Analysis cites this paper.

KAPLAN: Kolmogorov-Arnold Prognostic Learnable Activation Networks for Survival Analysis Approximation Rates in Besov Norms and Sample-Complexity of Kolmogorov-Arnold Networks with Residual Connections

Reference 28

Resolution
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arxiv_id, observed 2026-05-25T05:00:20.571989Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 455c183e-12a9-40ce-a065-217c32e0460f · inbound

KAPLAN: Kolmogorov-Arnold Prognostic Learnable Activation Networks for Survival Analysis cites this paper.

KAPLAN: Kolmogorov-Arnold Prognostic Learnable Activation Networks for Survival Analysis Approximation Rates in Besov Norms and Sample-Complexity of Kolmogorov-Arnold Networks with Residual Connections

Reference 28

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arxiv_id, observed 2026-06-30T15:44:48.131603Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-06-30T15:37:46.259398Z digest=sha256:2e7704706cd35720b57f59d4e455de4342dbea7da34e96fee45605de6056ff28