Paper Citation Record · LEDGER

Standard Transformers Achieve the Minimax Rate in Nonparametric Regression with $C^{s,\lambda}$ Targets

As of 15 August 2026, this Paper Citation Record lists 62 of 62 outbound references and 1 inbound Pith citation observation for arXiv:2602.20555.

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

pith.paper-citation-record.v1
2602.20555 v2

Coverage vector

measured 62 of 62 reference resolution

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measured 63 of 63 standing notices

One-hop event checks from named stored sources.

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measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-27T05:08:00.711576Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T16:38:40.636427Z

Reference resolution

62 of 62 outbound references displayed

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External citation measurements

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Outbound references

Observation 2e501e7c-54b3-422f-bb71-a1e2a5cf5896 · outbound

This paper cites Pearson, 2 edition, 1974.

Standard Transformers Achieve the Minimax Rate in Nonparametric Regression with $C^{s,\lambda}$ Targets Pearson, 2 edition, 1974

Reference 1

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Observation 2cb93dd9-59ed-4fef-814b-6c73a60efb61 · outbound

This paper cites Low-rank bottleneck in multi-head attention models.

Standard Transformers Achieve the Minimax Rate in Nonparametric Regression with $C^{s,\lambda}$ Targets Low-rank bottleneck in multi-head attention models

Reference 2

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Observation 9dcec2f4-ee2f-438d-8a71-8b2b4c50cf3f · outbound

This paper cites an unresolved cited work.

Standard Transformers Achieve the Minimax Rate in Nonparametric Regression with $C^{s,\lambda}$ Targets Unresolved cited work

Reference 3

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Observation 3a154508-52de-4dab-b3d7-8c63331d65bd · outbound

This paper cites A unified framework for establishing the universal approximation of transformer-type architectures.

Standard Transformers Achieve the Minimax Rate in Nonparametric Regression with $C^{s,\lambda}$ Targets A unified framework for establishing the universal approximation of transformer-type architectures

Reference 4

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Observation 0631de7d-335e-43e3-b836-7243964006c8 · outbound

This paper cites Efficient and Minimax Optimal In-context Nonparametric Regression with Transformers.

Standard Transformers Achieve the Minimax Rate in Nonparametric Regression with $C^{s,\lambda}$ Targets Efficient and Minimax Optimal In-context Nonparametric Regression with Transformers

Reference 5

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Observation 3015893b-4405-4162-b14b-292db3e5d535 · outbound

This paper cites Bert: Pre- training of deep bidirectional transformers for language understanding.

Standard Transformers Achieve the Minimax Rate in Nonparametric Regression with $C^{s,\lambda}$ Targets Bert: Pre- training of deep bidirectional transformers for language understanding

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-02T21:28:28.198176Z

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Observation 74b568fe-6acc-4a49-a4bd-22c86ff064c3 · outbound

This paper cites An image is worth 16x16 words: Transformers for image recognition at scale.

Standard Transformers Achieve the Minimax Rate in Nonparametric Regression with $C^{s,\lambda}$ Targets An image is worth 16x16 words: Transformers for image recognition at scale

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-02T21:28:28.302711Z

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Observation 8460b952-662f-4668-992d-b767a01fc7ef · outbound

This paper cites Inductive biases and variable creation in self-attention mechanisms.

Standard Transformers Achieve the Minimax Rate in Nonparametric Regression with $C^{s,\lambda}$ Targets Inductive biases and variable creation in self-attention mechanisms

Reference 8

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Observation c42b2c03-6729-453e-b86e-560979d2eaeb · outbound

This paper cites How do noise tails impact on deep relu networks?The Annals of Statistics, 52(4):1845–1871, 2024.

Standard Transformers Achieve the Minimax Rate in Nonparametric Regression with $C^{s,\lambda}$ Targets How do noise tails impact on deep relu networks?The Annals of Statistics, 52(4):1845–1871, 2024

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-02T21:28:28.506109Z

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Observation 21fca2ad-42cd-474f-8f6e-442a5c6a24a2 · outbound

This paper cites Deep neural networks for esti- mation and inference.Econometrica, 89(1):181–213, 2021.

Standard Transformers Achieve the Minimax Rate in Nonparametric Regression with $C^{s,\lambda}$ Targets Deep neural networks for esti- mation and inference.Econometrica, 89(1):181–213, 2021

Reference 10

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Observation 52cf793c-4063-4ea4-a189-cc84f8b183f8 · outbound

This paper cites Cambridge university press, 2021.

Standard Transformers Achieve the Minimax Rate in Nonparametric Regression with $C^{s,\lambda}$ Targets Cambridge university press, 2021

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-02T21:28:28.761626Z

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Observation 50bfc956-8029-4e2f-9fa0-23f6530e8275 · outbound

This paper cites Approximation rates for neural networks with encodable weights in smoothness spaces.Neural Networks, 134:107–130, 2021.

Standard Transformers Achieve the Minimax Rate in Nonparametric Regression with $C^{s,\lambda}$ Targets Approximation rates for neural networks with encodable weights in smoothness spaces.Neural Networks, 134:107–130, 2021

Reference 12

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Observation bcfbd33e-b72c-4476-9626-f494cb8692e3 · outbound

This paper cites On the rate of convergence of a classifier based on a transformer encoder.IEEE Transactions on Information Theory, 68(12):8139–8155, 2022.

Standard Transformers Achieve the Minimax Rate in Nonparametric Regression with $C^{s,\lambda}$ Targets On the rate of convergence of a classifier based on a transformer encoder.IEEE Transactions on Information Theory, 68(12):8139–8155, 2022

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-02T21:28:28.932299Z

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Observation 29a4e6c4-1d11-4e20-8767-9cf148ade33f · outbound

This paper cites Understanding scaling laws with statisti- cal and approximation theory for transformer neural networks on intrinsically low- dimensional data.

Standard Transformers Achieve the Minimax Rate in Nonparametric Regression with $C^{s,\lambda}$ Targets Understanding scaling laws with statisti- cal and approximation theory for transformer neural networks on intrinsically low- dimensional data

Reference 14

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Observation 84ba33a6-a5b1-478a-a558-35c34085ea7d · outbound

This paper cites Minimal width for universal property of deep rnn.Journal of Machine Learning Research, 24(121):1–41, 2023.

Standard Transformers Achieve the Minimax Rate in Nonparametric Regression with $C^{s,\lambda}$ Targets Minimal width for universal property of deep rnn.Journal of Machine Learning Research, 24(121):1–41, 2023

Reference 15

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Observation 59978333-edd3-4ecd-a1e5-d7b4ee9e32ca · outbound

This paper cites Universal approximation with softmax attention.arXiv preprint arXiv:2504.15956, 2025.

Standard Transformers Achieve the Minimax Rate in Nonparametric Regression with $C^{s,\lambda}$ Targets Universal approximation with softmax attention.arXiv preprint arXiv:2504.15956, 2025

Reference 16

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Observation a1ac003f-e4f8-457f-a66e-5c90c914d2ce · outbound

This paper cites Approximation rate of the transformer architecture for sequence modeling.

Standard Transformers Achieve the Minimax Rate in Nonparametric Regression with $C^{s,\lambda}$ Targets Approximation rate of the transformer architecture for sequence modeling

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-02T21:28:29.408007Z

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Observation 4ae2c7f0-c677-4dca-b5c1-d7f38b197cb3 · outbound

This paper cites Approximation Bounds for Transformer Networks with Application to Regression.

Standard Transformers Achieve the Minimax Rate in Nonparametric Regression with $C^{s,\lambda}$ Targets Approximation Bounds for Transformer Networks with Application to Regression

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-02T21:28:29.502605Z

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Observation 53c04e93-8632-4176-9f9f-cd0403ff7dca · outbound

This paper cites Transformers Can Overcome the Curse of Dimensionality: A Theoretical Study from an Approximation Perspective.

Standard Transformers Achieve the Minimax Rate in Nonparametric Regression with $C^{s,\lambda}$ Targets Transformers Can Overcome the Curse of Dimensionality: A Theoretical Study from an Approximation Perspective

Reference 19

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Observation edb1e354-523b-40eb-93c1-43e29e926da5 · outbound

This paper cites Deep nonparametric regression on approximate manifolds: Nonasymptotic error bounds with polynomial prefactors.The Annals of Statistics, 51(2):691–716, 2023.

Standard Transformers Achieve the Minimax Rate in Nonparametric Regression with $C^{s,\lambda}$ Targets Deep nonparametric regression on approximate manifolds: Nonasymptotic error bounds with polynomial prefactors.The Annals of Statistics, 51(2):691–716, 2023

Reference 20

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Observation 4a49078c-b132-492e-a8b4-c2e8951e1e92 · outbound

This paper cites Approximation bounds for recurrent neural networks with application to regression.arXiv preprint arXiv:2409.05577, 2024.

Standard Transformers Achieve the Minimax Rate in Nonparametric Regression with $C^{s,\lambda}$ Targets Approximation bounds for recurrent neural networks with application to regression.arXiv preprint arXiv:2409.05577, 2024

Reference 21

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Observation 03d88569-eff9-45f2-9fd9-c2b44b11131b · outbound

This paper cites Are transformers with one layer self-attention using low-rank weight matrices universal approximators? InThe Twelfth International Conference on Learning Representations, 2024.

Standard Transformers Achieve the Minimax Rate in Nonparametric Regression with $C^{s,\lambda}$ Targets Are transformers with one layer self-attention using low-rank weight matrices universal approximators? InThe Twelfth International Conference on Learning Representations, 2024

Reference 22

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Observation 5781e412-70e1-47c4-9cbf-75993ee51e79 · outbound

This paper cites On the optimal memorization capacity of trans- formers.

Standard Transformers Achieve the Minimax Rate in Nonparametric Regression with $C^{s,\lambda}$ Targets On the optimal memorization capacity of trans- formers

Reference 23

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Observation b82921ca-47a1-4901-b929-5422a6d5df4a · outbound

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Standard Transformers Achieve the Minimax Rate in Nonparametric Regression with $C^{s,\lambda}$ Targets The lipschitz constant of self-attention

Reference 24

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Observation 650bce6b-9287-4c58-9aca-20b450104f33 · outbound

This paper cites Provable memorization capac- ity of transformers.

Standard Transformers Achieve the Minimax Rate in Nonparametric Regression with $C^{s,\lambda}$ Targets Provable memorization capac- ity of transformers

Reference 25

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Observation 3e335400-d249-410d-a1f5-27555456dd6b · outbound

This paper cites Transformers are minimax optimal non- parametric in-context learners.

Standard Transformers Achieve the Minimax Rate in Nonparametric Regression with $C^{s,\lambda}$ Targets Transformers are minimax optimal non- parametric in-context learners

Reference 26

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Observation 4f45b3b4-d6e9-4c0e-8bb9-0d60c0de16be · outbound

This paper cites On the rate of convergence of fully connected deep neural network regression estimates.The Annals of Statistics, 49(4):2231–2249, 2021.

Standard Transformers Achieve the Minimax Rate in Nonparametric Regression with $C^{s,\lambda}$ Targets On the rate of convergence of fully connected deep neural network regression estimates.The Annals of Statistics, 49(4):2231–2249, 2021

Reference 27

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Observation b668f606-30f5-4b2e-ae07-52c39ce70c3f · outbound

This paper cites Univer- sal approximation under constraints is possible with transformers.

Standard Transformers Achieve the Minimax Rate in Nonparametric Regression with $C^{s,\lambda}$ Targets Univer- sal approximation under constraints is possible with transformers

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-02T21:28:30.872899Z

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Observation 260ffc38-d2a9-45d3-ae22-ae304c2de87f · outbound

This paper cites Approximation and optimization theory for linear continuous-time recurrent neural networks.Journal of Machine Learning Research, 23(42):1–85, 2022.

Standard Transformers Achieve the Minimax Rate in Nonparametric Regression with $C^{s,\lambda}$ Targets Approximation and optimization theory for linear continuous-time recurrent neural networks.Journal of Machine Learning Research, 23(42):1–85, 2022

Reference 29

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Observation bda47ccb-5703-41b4-b655-844792366c2d · outbound

This paper cites Generalization analysis of transformers in distribu- tion regression.Neural Computation, 37(2):260–293, 2025.

Standard Transformers Achieve the Minimax Rate in Nonparametric Regression with $C^{s,\lambda}$ Targets Generalization analysis of transformers in distribu- tion regression.Neural Computation, 37(2):260–293, 2025

Reference 30

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Observation 108ea5fe-2b55-4c77-9472-2eaa21ea1ebd · outbound

This paper cites Deep network approxi- mation for smooth functions.SIAM Journal on Mathematical Analysis, 53(5):5465– 5506, 2021.

Standard Transformers Achieve the Minimax Rate in Nonparametric Regression with $C^{s,\lambda}$ Targets Deep network approxi- mation for smooth functions.SIAM Journal on Mathematical Analysis, 53(5):5465– 5506, 2021

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Observation 5839742c-0e87-4728-bc4d-4e84d1d4e510 · outbound

This paper cites Upper and lower memory capacity bounds of transformers for next- token prediction.arXiv preprint arXiv:2405.13718, 2024.

Standard Transformers Achieve the Minimax Rate in Nonparametric Regression with $C^{s,\lambda}$ Targets Upper and lower memory capacity bounds of transformers for next- token prediction.arXiv preprint arXiv:2405.13718, 2024

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Observation 251460d0-5f51-4561-9b09-e150acfe6811 · outbound

This paper cites Memorization capacity of multi-head attention in transformers.

Standard Transformers Achieve the Minimax Rate in Nonparametric Regression with $C^{s,\lambda}$ Targets Memorization capacity of multi-head attention in transformers

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-02T21:28:31.589919Z

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This paper cites Rates of approximation by relu shallow neural networks.Journal of Complexity, 79:101784, 2023.

Standard Transformers Achieve the Minimax Rate in Nonparametric Regression with $C^{s,\lambda}$ Targets Rates of approximation by relu shallow neural networks.Journal of Complexity, 79:101784, 2023

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Resolution: unresolved; no resolver link, observed 2026-08-02T21:28:31.739676Z

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This paper cites Adaptive approximation and generalization of deep neural network with intrinsic dimensionality.Journal of Machine Learning Research, 21(174):1–38, 2020.

Standard Transformers Achieve the Minimax Rate in Nonparametric Regression with $C^{s,\lambda}$ Targets Adaptive approximation and generalization of deep neural network with intrinsic dimensionality.Journal of Machine Learning Research, 21(174):1–38, 2020

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Standard Transformers Achieve the Minimax Rate in Nonparametric Regression with $C^{s,\lambda}$ Targets Provable memorization via deep neural networks using sub-linear parameters

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Resolution: unresolved; no resolver link, observed 2026-08-02T21:28:32.126644Z

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Observation 742af754-03ef-4462-9503-40a2cfa02e69 · outbound

This paper cites Equivalence of approximation by convolu- tional neural networks and fully-connected networks.Proceedings of the American Mathematical Society, 148(4):1567–1581, 2020.

Standard Transformers Achieve the Minimax Rate in Nonparametric Regression with $C^{s,\lambda}$ Targets Equivalence of approximation by convolu- tional neural networks and fully-connected networks.Proceedings of the American Mathematical Society, 148(4):1567–1581, 2020

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Resolution: unresolved; no resolver link, observed 2026-08-02T21:28:32.361578Z

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Observation d868eeef-7558-442d-b0dc-6b00fee595bd · outbound

This paper cites Representational strengths and limitations of transformers.

Standard Transformers Achieve the Minimax Rate in Nonparametric Regression with $C^{s,\lambda}$ Targets Representational strengths and limitations of transformers

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Resolution: unresolved; no resolver link, observed 2026-08-02T21:28:32.551226Z

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Observation 7eb5b0ad-7a9a-465e-a2b1-a5decf1adfa0 · outbound

This paper cites Nonparametric regression using deep neural net- works with relu activation function.Annals of statistics, 48(4):1875–1897, 2020.

Standard Transformers Achieve the Minimax Rate in Nonparametric Regression with $C^{s,\lambda}$ Targets Nonparametric regression using deep neural net- works with relu activation function.Annals of statistics, 48(4):1875–1897, 2020

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Resolution: unresolved; no resolver link, observed 2026-08-02T21:28:32.650600Z

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Standard Transformers Achieve the Minimax Rate in Nonparametric Regression with $C^{s,\lambda}$ Targets The kolmogorov–arnold representation theorem revisited

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This paper cites Understanding in- context learning on structured manifolds: Bridging attention to kernel methods.

Standard Transformers Achieve the Minimax Rate in Nonparametric Regression with $C^{s,\lambda}$ Targets Understanding in- context learning on structured manifolds: Bridging attention to kernel methods

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This paper cites Deep network approximation char- acterized by number of neurons.Communications in Computational Physics, 28(5), 2020.

Standard Transformers Achieve the Minimax Rate in Nonparametric Regression with $C^{s,\lambda}$ Targets Deep network approximation char- acterized by number of neurons.Communications in Computational Physics, 28(5), 2020

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Observation d17c58a7-8caf-4b31-9793-6dd6a74c6613 · outbound

This paper cites Optimal approximation rate of relu networks in terms of width and depth.Journal de Math´ ematiques Pures et Appliqu´ ees, 157:101–135, 2022.

Standard Transformers Achieve the Minimax Rate in Nonparametric Regression with $C^{s,\lambda}$ Targets Optimal approximation rate of relu networks in terms of width and depth.Journal de Math´ ematiques Pures et Appliqu´ ees, 157:101–135, 2022

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Observation e4e39434-fbd4-46d0-a726-e7376d1f7765 · outbound

This paper cites Optimal approximation rates for deep relu neural networks on sobolev and besov spaces.Journal of Machine Learning Research, 24(357):1–52, 2023.

Standard Transformers Achieve the Minimax Rate in Nonparametric Regression with $C^{s,\lambda}$ Targets Optimal approximation rates for deep relu neural networks on sobolev and besov spaces.Journal of Machine Learning Research, 24(357):1–52, 2023

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Resolution: unresolved; no resolver link, observed 2026-08-02T21:28:33.378677Z

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Observation 73080394-9a7f-4dec-bd37-0cb148359b01 · outbound

This paper cites Optimal rates of convergence for nonparametric estimators.The annals of Statistics, pages 1348–1360, 1980.

Standard Transformers Achieve the Minimax Rate in Nonparametric Regression with $C^{s,\lambda}$ Targets Optimal rates of convergence for nonparametric estimators.The annals of Statistics, pages 1348–1360, 1980

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Resolution: unresolved; no resolver link, observed 2026-08-02T21:28:33.503352Z

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Observation 0dcf055d-49fd-40e5-87b4-b8361a039fd6 · outbound

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

Standard Transformers Achieve the Minimax Rate in Nonparametric Regression with $C^{s,\lambda}$ Targets Adaptivity of deep reLU network for learning in besov and mixed smooth besov spaces: optimal rate and curse of dimensionality

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Resolution: unresolved; no resolver link, observed 2026-08-02T21:28:33.641883Z

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Observation d7ad0ad1-320c-4f5a-96d9-0591b72e1610 · outbound

This paper cites Approximation and estimation ability of trans- formers for sequence-to-sequence functions with infinite dimensional input.

Standard Transformers Achieve the Minimax Rate in Nonparametric Regression with $C^{s,\lambda}$ Targets Approximation and estimation ability of trans- formers for sequence-to-sequence functions with infinite dimensional input

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Resolution: unresolved; no resolver link, observed 2026-08-02T21:28:33.794727Z

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Observation ec702a1a-f943-401f-8d3b-7efa7b87a55f · outbound

This paper cites Approximation of Permutation Invariant Polynomials by Transformers: Efficient Construction in Column-Size.

Standard Transformers Achieve the Minimax Rate in Nonparametric Regression with $C^{s,\lambda}$ Targets Approximation of Permutation Invariant Polynomials by Transformers: Efficient Construction in Column-Size

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Resolution: unresolved; no resolver link, observed 2026-08-02T21:28:33.955158Z

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Observation 83aa814b-aab6-44a8-9754-a959086b241a · outbound

This paper cites Weak convergence.

Standard Transformers Achieve the Minimax Rate in Nonparametric Regression with $C^{s,\lambda}$ Targets Weak convergence

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Resolution: unresolved; no resolver link, observed 2026-08-02T21:28:34.094329Z

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Observation 99685651-1abc-433d-86a7-2760e342b9b3 · outbound

This paper cites On the optimal memorization power of reLU neural networks.

Standard Transformers Achieve the Minimax Rate in Nonparametric Regression with $C^{s,\lambda}$ Targets On the optimal memorization power of reLU neural networks

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Observation 89b4f471-10f4-4ce3-9e09-92b1fd3deeec · outbound

This paper cites Attention is all you need.Advances in neural information processing systems, 30, 2017.

Standard Transformers Achieve the Minimax Rate in Nonparametric Regression with $C^{s,\lambda}$ Targets Attention is all you need.Advances in neural information processing systems, 30, 2017

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Observation 56d032e3-a846-4c70-a37c-2f027e3cdcb7 · outbound

This paper cites Prompt tuning transformers for data memorization.

Standard Transformers Achieve the Minimax Rate in Nonparametric Regression with $C^{s,\lambda}$ Targets Prompt tuning transformers for data memorization

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Resolution: unresolved; no resolver link, observed 2026-08-02T21:28:34.546484Z

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Observation c22ce1a9-de37-476e-9d5d-3a8213da738f · outbound

This paper cites an unresolved cited work.

Standard Transformers Achieve the Minimax Rate in Nonparametric Regression with $C^{s,\lambda}$ Targets Unresolved cited work

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Resolution: unresolved; no resolver link, observed 2026-08-02T21:28:34.685239Z

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This paper cites Statistically meaningful approximation: a case study on approximating turing machines with transformers.

Standard Transformers Achieve the Minimax Rate in Nonparametric Regression with $C^{s,\lambda}$ Targets Statistically meaningful approximation: a case study on approximating turing machines with transformers

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Observation 986e253b-7c69-4463-8e21-357e00d1a999 · outbound

This paper cites On the optimal approximation of Sobolev and Besov functions using deep ReLU neural networks.

Standard Transformers Achieve the Minimax Rate in Nonparametric Regression with $C^{s,\lambda}$ Targets On the optimal approximation of Sobolev and Besov functions using deep ReLU neural networks

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Observation 14739f9b-4530-47ab-b1cb-451be7a90070 · outbound

This paper cites Nonparametric regression using over- parameterized shallow relu neural networks.Journal of Machine Learning Research, 25(165):1–35, 2024.

Standard Transformers Achieve the Minimax Rate in Nonparametric Regression with $C^{s,\lambda}$ Targets Nonparametric regression using over- parameterized shallow relu neural networks.Journal of Machine Learning Research, 25(165):1–35, 2024

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Observation f319b704-d617-4bd8-85bb-28ec3cf8b13d · outbound

This paper cites Error bounds for approximations with deep relu networks.Neural networks, 94:103–114, 2017.

Standard Transformers Achieve the Minimax Rate in Nonparametric Regression with $C^{s,\lambda}$ Targets Error bounds for approximations with deep relu networks.Neural networks, 94:103–114, 2017

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Resolution: unresolved; no resolver link, observed 2026-08-02T21:28:35.137363Z

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Observation f64b5c00-4260-436c-9af7-3131b2b60151 · outbound

This paper cites Optimal approximation of continuous functions by very deep relu networks.

Standard Transformers Achieve the Minimax Rate in Nonparametric Regression with $C^{s,\lambda}$ Targets Optimal approximation of continuous functions by very deep relu networks

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Observation e94b9401-45af-4c6b-8d36-f462d85f6de9 · outbound

This paper cites Are transformers universal approximators of sequence-to-sequence func- tions? InInternational Conference on Learning Representations, 2020.

Standard Transformers Achieve the Minimax Rate in Nonparametric Regression with $C^{s,\lambda}$ Targets Are transformers universal approximators of sequence-to-sequence func- tions? InInternational Conference on Learning Representations, 2020

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Observation ce2b942a-2dfd-40b5-8197-b5f2af50374d · outbound

This paper cites O (n) connections are expressive enough: Universal approximability of sparse transformers.Advances in Neural Information Processing Systems, 33:13783–13794, 2020.

Standard Transformers Achieve the Minimax Rate in Nonparametric Regression with $C^{s,\lambda}$ Targets O (n) connections are expressive enough: Universal approximability of sparse transformers.Advances in Neural Information Processing Systems, 33:13783–13794, 2020

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Resolution: unresolved; no resolver link, observed 2026-08-02T21:28:35.601184Z

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Observation 4ac02717-490a-42a6-bc5b-d1643ab170dc · outbound

This paper cites Theory of deep convolutional neural networks: Downsampling.

Standard Transformers Achieve the Minimax Rate in Nonparametric Regression with $C^{s,\lambda}$ Targets Theory of deep convolutional neural networks: Downsampling

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Observation 89d356a7-abf0-415b-bbb9-5f6fefffdbca · outbound

This paper cites Universality of deep convolutional neural networks.Applied and computational harmonic analysis, 48(2):787–794, 2020.

Standard Transformers Achieve the Minimax Rate in Nonparametric Regression with $C^{s,\lambda}$ Targets Universality of deep convolutional neural networks.Applied and computational harmonic analysis, 48(2):787–794, 2020

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Resolution: unresolved; no resolver link, observed 2026-08-02T21:28:35.859983Z

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Pith citing papers

Observation 68c41094-657e-4173-97e4-2c0684c89151 · inbound

Generalization Bounds for Transformer-Based Next-Token Prediction in a Language Model cites this paper.

Generalization Bounds for Transformer-Based Next-Token Prediction in a Language Model Standard Transformers Achieve the Minimax Rate in Nonparametric Regression with $C^{s,\lambda}$ Targets

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-07-29T02:24:15.834827Z

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