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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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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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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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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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Observation 00c5ca2c-aba6-4941-944d-9911ab15e268 · outbound

This paper cites The fractional order fourier transform and its application to quantum me- chanics,.

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

Reference 38

Resolution
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=sha256:2e125c280f9e2f82ad34b7993a58c6a267151d9200fdbdec84c4a0461047288f

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:f7a245dce5cd8bef48fd5677fb449cb70a4d8de36bcf79c94235acb72d60926b

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=sha256:482671f54e4223c35611fed9fcf86d5e3f68c9ecb23693f3314ae149a4e1e0f3

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:d6dafcb244d8a805565435ddd72c280071307c4a9ca0055197e184f62d48e9cf

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:a4df3b2b24183ac986644642af7b0cf3cf49d0ee8c193fece0bc255fbff8d7db

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=sha256:49849817db82235bb9d7c8216a4b7aa79271e75a4fd1f84e0a3f92b724051ceb

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=sha256:34235e5e60ff2da771570c1d6f107ebaba18cd37608c9f6077d24dfd50183b93

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=sha256:9cc204b64d9d4bde334db3e5e2073c1a1b576304aae009822891ebcb3629f0bd

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:fedc84adb341c29818730f891415e0061b060fdaf3f1cdbadf96cd8a52376f46

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:f8b374fcb869a538551d0cb13444b12d439a8c5f5187ceeed968575083c55177

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:fe388d4286149aa0f019bca06c50ac4f3f0bda76361c57f29fefbceb36532e15

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:e97c187a863aac874bce51464f1d192b2968a1fb3989f56f93717b104fc0aae3

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=sha256:14c401963783b25f2ff92d2a75d4b49b77038b32c38818a7875dac1ee62596c4

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:ef1a58f4017b3635585d1d9c79bb53047039d02cc794569040d51d7aca5ab685

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=sha256:581746bf22dda485c18e854027b080389570c5e4c4a6dee1a73e16346743491c

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:fc28edb423e7f64c23bad615cf5866791892583aa451b57f9217e6de20d84f1a

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=sha256:912edfe01d559c86d2fb04ec9bc7bda5ef3439c4aa1645b7184655b4dd5d2fcd

Pith citing papers

No inbound Pith citation observations are available.