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Paper Citation Record · LEDGER

Approximation Rates for Metaplectic Neural Networks

As of 17 August 2026, this Paper Citation Record lists 54 of 54 outbound references and 0 inbound Pith citation observations for arXiv:2608.08872.

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pith.paper-citation-record.v1
2608.08872 v1

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Reference resolution

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Outbound references

Observation dea62a38-ce0c-441f-93ac-fffde3477163 · outbound

This paper cites Uniform approximation with quadratic neural networks,.

Approximation Rates for Metaplectic Neural Networks Uniform approximation with quadratic neural networks,

Reference 1

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Approximation Rates for Metaplectic Neural Networks Time-frequency analysis for neuralnetworks,

Reference 2

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This paper cites Space-Time Approximation with Shallow Neural Networks in Fourier Lebesgue spaces.

Approximation Rates for Metaplectic Neural Networks Space-Time Approximation with Shallow Neural Networks in Fourier Lebesgue spaces

Reference 3

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This paper cites Weighted Sobolev Approximation Rates for Neural Networks on Unbounded Domains.

Approximation Rates for Metaplectic Neural Networks Weighted Sobolev Approximation Rates for Neural Networks on Unbounded Domains

Reference 4

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This paper cites Approximations with deep neural networks in Sobolev time-space,.

Approximation Rates for Metaplectic Neural Networks Approximations with deep neural networks in Sobolev time-space,

Reference 5

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Approximation Rates for Metaplectic Neural Networks The fractional fourier transform and time-frequency representations,

Reference 6

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This paper cites Breaking the Curse of Dimensionality with Convex Neural Networks.

Approximation Rates for Metaplectic Neural Networks Breaking the Curse of Dimensionality with Convex Neural Networks

Reference 7

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This paper cites Universal approximation bounds for superpositions of a sigmoidal func- tion,.

Approximation Rates for Metaplectic Neural Networks Universal approximation bounds for superpositions of a sigmoidal func- tion,

Reference 8

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This paper cites Approximation and learning by greedy algorithms,.

Approximation Rates for Metaplectic Neural Networks Approximation and learning by greedy algorithms,

Reference 9

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Reference 10

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Approximation Rates for Metaplectic Neural Networks Stochastic partial differential equations in M-type 2 Banach spaces,

Reference 11

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Approximation Rates for Metaplectic Neural Networks The discrete fractional fourier trans- form,

Reference 12

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This paper cites Universal approximation to nonlinear operators by neural networkswitharbitraryactivationfunctionsanditsapplicationtodynamicalsystems,.

Approximation Rates for Metaplectic Neural Networks Universal approximation to nonlinear operators by neural networkswitharbitraryactivationfunctionsanditsapplicationtodynamicalsystems,

Reference 13

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This paper cites A Regularity Theory for Static Schrödinger Equa- tions on{R} d in Spectral Barron Spaces,.

Approximation Rates for Metaplectic Neural Networks A Regularity Theory for Static Schrödinger Equa- tions on{R} d in Spectral Barron Spaces,

Reference 14

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Approximation Rates for Metaplectic Neural Networks Cordero and L

Reference 15

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Approximation Rates for Metaplectic Neural Networks Approximation by Superpositions of a Sigmoidal Function,

Reference 16

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Approximation Rates for Metaplectic Neural Networks On the approximation of functions by tanh neural networks,

Reference 17

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Approximation Rates for Metaplectic Neural Networks Nonlinear Approximation,

Reference 18

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Approximation Rates for Metaplectic Neural Networks A metaplectic perspective of uncertainty principles in the linear canonical transform domain,

Reference 19

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Reference 20

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Approximation Rates for Metaplectic Neural Networks The Barron Space and the Flow-Induced Function Spaces for Neural Network Models,

Reference 21

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Approximation Rates for Metaplectic Neural Networks Deep Neural Network Approximation Theory,

Reference 22

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Approximation Rates for Metaplectic Neural Networks The metaplectic action on modulation spaces,

Reference 24

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Approximation Rates for Metaplectic Neural Networks Boundedness of metaplectic operators withinL p spaces, applications to pseudodifferential calculus, and time–frequency representations,

Reference 25

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Approximation Rates for Metaplectic Neural Networks Gröchenig,Foundations of Time-Frequency Analysis(Applied and Numerical Har- monic Analysis), J

Reference 30

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Approximation Rates for Metaplectic Neural Networks Approximation capabilities of multilayer feedforward networks,

Reference 31

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Approximation Rates for Metaplectic Neural Networks Multilayer feedforward networks are uni- versal approximators,

Reference 32

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Approximation Rates for Metaplectic Neural Networks A simple lemma on greedy approximation in Hilbert space and conver- gence rates for projection pursuit regression and neural network training,

Reference 33

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Approximation Rates for Metaplectic Neural Networks On universal approximation and error bounds for fourier neural operators,

Reference 34

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Approximation Rates for Metaplectic Neural Networks A Theoretical Analysis of Deep Neural Networks and Parametric PDEs,

Reference 35

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Approximation Rates for Metaplectic Neural Networks Error estimates for deeponets: A deep learning framework in infinite dimensions,

Reference 36

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Approximation Rates for Metaplectic Neural Networks The expressive power of neural networks: A view from the width,

Reference 37

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Approximation Rates for Metaplectic Neural Networks The fractional order fourier transform and its application to quantum me- chanics,

Reference 38

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verified fuzzy
raw_fallback, observed 2026-08-14T04:39:33.769326Z

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=2026-08-14T04:39:32.555269Z digest=sha256:fe7dabb54970589a0645d9c478448f986de235cea6ed4d290c5af15f805502c6

Observation c5332c36-7c9a-4a2e-8653-68c50f3cabb1 · outbound

This paper cites Fractional fourier transforms and their optical implementation,.

Approximation Rates for Metaplectic Neural Networks Fractional fourier transforms and their optical implementation,

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T04:39:33.753696Z

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=2026-08-14T04:39:32.559983Z digest=sha256:3cb448c0284394ea4cf48351a9074d0cd8acc093c25cd04bbea5024ab2643d34

Observation ec956e18-7c3b-456f-8b95-d6ec21b60e01 · outbound

This paper cites an unresolved cited work.

Approximation Rates for Metaplectic Neural Networks Unresolved cited work

Reference 40

Resolution
unresolved
raw_fallback, observed 2026-08-14T04:39:33.735650Z

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=2026-08-14T04:39:32.564786Z digest=sha256:4675e33dc8705f02c936b9c3a9f89c964ee3c1cbc0775eeb8321893d690d3e0a

Observation 8a97c7b6-b8be-4b14-b496-3b13e6ff38ef · outbound

This paper cites Modulation Spaces and the Curse of Dimensionality,.

Approximation Rates for Metaplectic Neural Networks Modulation Spaces and the Curse of Dimensionality,

Reference 41

Resolution
unresolved
no resolver link, observed 2026-08-14T04:39:32.569560Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-14T04:39:32.569560Z digest=sha256:fa1416e56f743eadc979da0b725ebddf8c6abef601a88cb28a171e53af603535

Observation c04554f5-13ed-49ae-ac34-48bf4f1166cc · outbound

This paper cites Remarques sur un résultat non publié de b. maurey,.

Approximation Rates for Metaplectic Neural Networks Remarques sur un résultat non publié de b. maurey,

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T04:39:33.709773Z

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=2026-08-14T04:39:32.574283Z digest=sha256:9b30a1904a4341927307e28bfc33a1a62fc6964fc8ed3f9e978a7839080d0970

Observation 1c32be1f-466a-495a-8660-5805d1aa9a8f · outbound

This paper cites Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations,.

Approximation Rates for Metaplectic Neural Networks Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations,

Reference 43

Resolution
unresolved
no resolver link, observed 2026-08-14T04:39:32.578553Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-14T04:39:32.578553Z digest=sha256:b9641b6711b434e29127707990aa76294ad4a0fa2667d969a1fbebb0b54c2d64

Observation a009cbbc-e9fc-44ff-9462-72d4d1bab8fd · outbound

This paper cites Rudin,Real and Complex Analysis, 3rd ed.

Approximation Rates for Metaplectic Neural Networks Rudin,Real and Complex Analysis, 3rd ed

Reference 44

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T04:39:33.689366Z

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=2026-08-14T04:39:32.583084Z digest=sha256:7efc1d7d401d7e1101fe5de35e38c987a5f52dbc66f10ddf4855a30ef20de470

Observation 66dd9506-42f2-438f-a935-ce0af0cd92e3 · outbound

This paper cites Approximation Rates for Neural Networks With General Activation Functions,.

Approximation Rates for Metaplectic Neural Networks Approximation Rates for Neural Networks With General Activation Functions,

Reference 45

Resolution
unresolved
no resolver link, observed 2026-08-14T04:39:32.587335Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-14T04:39:32.587335Z digest=sha256:e563d32770bc9e2c52f8ff0f6e5838599c3fc94e4d5fab662e39fb02a52f1dee

Observation 54744e26-08c3-4af0-b57d-09464eb7ed99 · outbound

This paper cites Characterization of the Variation Spaces Corresponding to Shallow Neural Networks,.

Approximation Rates for Metaplectic Neural Networks Characterization of the Variation Spaces Corresponding to Shallow Neural Networks,

Reference 46

Resolution
unresolved
no resolver link, observed 2026-08-14T04:39:32.592261Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-14T04:39:32.592261Z digest=sha256:6025c97977c66efb8001ced189669f84e3fdc573e9b2be946bbdff424bf4355c

Observation c24626c0-d85a-434d-a21f-c6afda2aea8f · outbound

This paper cites Sharp Bounds on the Approximation Rates, Metric Entropy, and n-Widths of Shallow Neural Networks,.

Approximation Rates for Metaplectic Neural Networks Sharp Bounds on the Approximation Rates, Metric Entropy, and n-Widths of Shallow Neural Networks,

Reference 47

Resolution
unresolved
no resolver link, observed 2026-08-14T04:39:32.596941Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-14T04:39:32.596941Z digest=sha256:c5c99edfc95b73106351248a0eee0b324f1df46ac905d9a88a1fbd6a63cde96f

Observation e34b85d7-4b36-47d1-97e9-40adb0e07cf0 · outbound

This paper cites Tartar,An Introduction to Sobolev Spaces and Interpolation Spaces(Lecture Notes of the Unione Matematica Italiana).

Approximation Rates for Metaplectic Neural Networks Tartar,An Introduction to Sobolev Spaces and Interpolation Spaces(Lecture Notes of the Unione Matematica Italiana)

Reference 48

Resolution
unresolved
no resolver link, observed 2026-08-14T04:39:32.610260Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-14T04:39:32.610260Z digest=sha256:a3c2f86124ed592e4e02a54456168bdfd01b210f13a6c1c99e0248016578c9f0

Observation b1fc3328-5da1-4694-8cf9-9c2ca17b5465 · outbound

This paper cites an unresolved cited work.

Approximation Rates for Metaplectic Neural Networks Unresolved cited work

Reference 49

Resolution
unresolved
raw_fallback, observed 2026-08-14T04:39:33.647762Z

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=2026-08-14T04:39:32.615713Z digest=sha256:a5d36577775792259549514048b5eb8bf98bc79f93a74b1f55698a3767f37368

Observation 145f14fb-bc0d-4dce-9913-25e281043e31 · outbound

This paper cites Terdik,Multivariate Statistical Methods: Going Beyond the Linear(Frontiers in Probability and the Statistical Sciences).

Approximation Rates for Metaplectic Neural Networks Terdik,Multivariate Statistical Methods: Going Beyond the Linear(Frontiers in Probability and the Statistical Sciences)

Reference 50

Resolution
verified exact
doi, observed 2026-08-14T04:39:32.698103Z

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=2026-08-14T04:39:32.620912Z digest=sha256:67a9a315b0aafc1b53a45713b22b1437cabfd59539a0552eb486e08e2e9e348a

Observation 2ce68538-e22e-434d-957e-0ab4a9ec961d · outbound

This paper cites American Mathematical Soci- ety, 2014, vol.

Approximation Rates for Metaplectic Neural Networks American Mathematical Soci- ety, 2014, vol

Reference 51

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T04:39:33.617960Z

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=2026-08-14T04:39:32.641884Z digest=sha256:b509fea772c212c48cc0b39048a65a214c2b7c38c8b8212db5c9280b2cde00eb

Observation 5088463a-e27f-446a-8101-c80636cbbc47 · outbound

This paper cites Thangavelu,Lectures on Hermite and Laguerre Expansions(Mathematical Notes).

Approximation Rates for Metaplectic Neural Networks Thangavelu,Lectures on Hermite and Laguerre Expansions(Mathematical Notes)

Reference 52

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T04:39:33.591518Z

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=2026-08-14T04:39:32.648045Z digest=sha256:6570fd5c8fd5570d3166bdfc423802787aac708d427558412afe5485cf20fba0

Observation 01142346-22b0-408d-b622-1a78dddf3f02 · outbound

This paper cites $L^p$ sampling numbers for the Fourier-analytic Barron space.

Approximation Rates for Metaplectic Neural Networks $L^p$ sampling numbers for the Fourier-analytic Barron space

Reference 53

Resolution
unresolved
no resolver link, observed 2026-08-14T04:39:32.652993Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-14T04:39:32.652993Z digest=sha256:52372a12bb438ad0e2e8b4dba2d43d8b26d594081947a947906b37afe404deaa

Observation 8fcd5945-1aca-44ba-96df-ddde14f68be5 · outbound

This paper cites Some observations on high-dimensional partial differ- ential equations with barron data,.

Approximation Rates for Metaplectic Neural Networks Some observations on high-dimensional partial differ- ential equations with barron data,

Reference 54

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T04:39:33.573659Z

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=2026-08-14T04:39:32.658161Z digest=sha256:24318c9a555a889428ac85534a128e22dee877a8ed5ba845d1f3e470e3a4ea16

Pith citing papers

No inbound Pith citation observations are available.