Paper Citation Record · LEDGER

Learning Spectral Methods by Transformers

As of 23 August 2026, this Paper Citation Record lists 38 of 38 outbound references and 3 inbound Pith citation observations for arXiv:2501.01312.

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

pith.paper-citation-record.v1
2501.01312 v3

Coverage vector

measured 38 of 38 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T22:36:51.343701Z

measured 41 of 41 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T05:01:32.338962Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-11T19:16:09.617446Z

Reference resolution

38 of 38 outbound references displayed

verified exact0
verified fuzzy21
unresolved17
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation ceee7ea6-7334-409f-9c17-e3a2b3664dae · outbound

This paper cites http://yann.

Learning Spectral Methods by Transformers http://yann

Reference 1

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Source-reported events for the cited work

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Observation ae920c5a-2aae-43ee-821d-8219a595fd4e · outbound

This paper cites What learning algorithm is in-context learning? Investigations with linear models.

Learning Spectral Methods by Transformers What learning algorithm is in-context learning? Investigations with linear models

Reference 2

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Source-reported events for the cited work

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Observation 796ae448-7600-401b-aead-9e38feea4933 · outbound

This paper cites Breaking the curse of dimensionality with convex neural networks.

Learning Spectral Methods by Transformers Breaking the curse of dimensionality with convex neural networks

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:36:51.973167Z

Source-reported events for the cited work

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Observation 3eda86c8-e73a-40c8-a926-0d60b76c3fb8 · outbound

This paper cites Transformers as Statisticians: Provable In-Context Learning with In-Context Algorithm Selection.

Learning Spectral Methods by Transformers Transformers as Statisticians: Provable In-Context Learning with In-Context Algorithm Selection

Reference 4

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Source-reported events for the cited work

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Observation 87371e1d-0693-4136-a30d-2da0d5b1ecb9 · outbound

This paper cites Transformers as statis- ticians: Provable in-context learning with in-context algorithm selection.

Learning Spectral Methods by Transformers Transformers as statis- ticians: Provable in-context learning with in-context algorithm selection

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:36:51.956214Z

Source-reported events for the cited work

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Observation ac852a9f-75a6-44c7-8c5b-523174cb7e6b · outbound

This paper cites Universal approximation bounds for superpositions of a sigmoidal function.

Learning Spectral Methods by Transformers Universal approximation bounds for superpositions of a sigmoidal function

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:36:51.938978Z

Source-reported events for the cited work

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Observation 4750f14a-ce43-44d6-863a-bc30b1a5f2d7 · outbound

This paper cites On the Computational Power of Transformers and its Implications in Sequence Modeling.

Learning Spectral Methods by Transformers On the Computational Power of Transformers and its Implications in Sequence Modeling

Reference 7

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Source-reported events for the cited work

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Observation 3a5ee2ad-1003-48ef-9990-3a69a60749b0 · outbound

This paper cites Comparative accuracies of artificial neural net- works and discriminant analysis in predicting forest cover types from cartographic variables.

Learning Spectral Methods by Transformers Comparative accuracies of artificial neural net- works and discriminant analysis in predicting forest cover types from cartographic variables

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:36:51.920635Z

Source-reported events for the cited work

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Observation a4a11e38-5e6c-4266-9a3a-faff961ab650 · outbound

This paper cites Foundations of data science.

Learning Spectral Methods by Transformers Foundations of data science

Reference 9

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Source-reported events for the cited work

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Observation 724d3bd4-09d7-4e24-bb75-58682ef8f14b · outbound

This paper cites A Survey on In-context Learning.

Learning Spectral Methods by Transformers A Survey on In-context Learning

Reference 10

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Source-reported events for the cited work

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Observation b8f324d5-643f-4c5c-81ce-75140bd9461f · outbound

This paper cites What can trans- formers learn in-context? a case study of simple function classes.

Learning Spectral Methods by Transformers What can trans- formers learn in-context? a case study of simple function classes

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:36:51.887259Z

Source-reported events for the cited work

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Observation 50ffeb68-60b6-4040-94a9-9137884dfa82 · outbound

This paper cites Mathematical foundations of infinite-dimensional statistical models, volume 40.

Learning Spectral Methods by Transformers Mathematical foundations of infinite-dimensional statistical models, volume 40

Reference 12

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Source-reported events for the cited work

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Observation e69b6d89-90ad-4c2d-96f6-cec065761143 · outbound

This paper cites Matrix computations.

Learning Spectral Methods by Transformers Matrix computations

Reference 13

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8b874352-e60d-4b76-8e48-030db1f3fb15 · outbound

This paper cites Infinite at- tention: Nngp and ntk for deep attention networks.

Learning Spectral Methods by Transformers Infinite at- tention: Nngp and ntk for deep attention networks

Reference 14

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Source-reported events for the cited work

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Observation ca8c64c5-9edb-4e4d-b30b-00ec9402f2fb · outbound

This paper cites In-Context Convergence of Transformers.

Learning Spectral Methods by Transformers In-Context Convergence of Transformers

Reference 15

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Source-reported events for the cited work

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Observation 8fce8855-71a1-49c7-9c60-2faa52e75ef9 · outbound

This paper cites Partially view-aligned clustering.

Learning Spectral Methods by Transformers Partially view-aligned clustering

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:36:51.823203Z

Source-reported events for the cited work

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Observation daecd661-3314-4c41-b1fe-998c20931936 · outbound

This paper cites Spectral algorithms.

Learning Spectral Methods by Transformers Spectral algorithms

Reference 17

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Source-reported events for the cited work

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Observation 92f9e86b-10ca-4f66-8ba5-f94d922bb373 · outbound

This paper cites Transformers in vision: A survey.

Learning Spectral Methods by Transformers Transformers in vision: A survey

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:36:51.789881Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation b2b37e6a-27ef-427d-91b0-14f73b984f13 · outbound

This paper cites Adaptive estimation of a quadratic functional by model selection.

Learning Spectral Methods by Transformers Adaptive estimation of a quadratic functional by model selection

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:36:51.774243Z

Source-reported events for the cited work

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Observation 906da664-de99-4c0f-9895-a84daac54ef3 · outbound

This paper cites Image clustering with external guidance.

Learning Spectral Methods by Transformers Image clustering with external guidance

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:36:51.756148Z

Source-reported events for the cited work

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Observation f4c809e4-c01c-4e6a-b896-a020c5cd77aa · outbound

This paper cites Transformers Learn Shortcuts to Automata.

Learning Spectral Methods by Transformers Transformers Learn Shortcuts to Automata

Reference 21

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Observation fd6f2a9a-7b19-4d60-a26d-5524189974ad · outbound

This paper cites Learning representations for time series clustering.

Learning Spectral Methods by Transformers Learning representations for time series clustering

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:36:51.738464Z

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Observation 2bbc9d73-b137-44df-9c9f-6f11a20bf734 · outbound

This paper cites Deep transformation-invariant clustering.

Learning Spectral Methods by Transformers Deep transformation-invariant clustering

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:36:51.722279Z

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Observation 10fad760-e531-47ce-a565-34484fc9baba · outbound

This paper cites Pytorch: An imperative style, high-performance deep learning library.

Learning Spectral Methods by Transformers Pytorch: An imperative style, high-performance deep learning library

Reference 24

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Source-reported events for the cited work

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Observation 3eadb097-cc88-431d-8413-4423a53604e3 · outbound

This paper cites Scikit-learn: Machine learning in python.

Learning Spectral Methods by Transformers Scikit-learn: Machine learning in python

Reference 25

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Source-reported events for the cited work

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Observation ed431c5d-78f3-4997-b4cd-9213fe862b0e · outbound

This paper cites Attention is turing-complete.

Learning Spectral Methods by Transformers Attention is turing-complete

Reference 26

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Observation 1a51274d-145d-499f-9c32-61fdd16af409 · outbound

This paper cites Language models are unsupervised multitask learners.

Learning Spectral Methods by Transformers Language models are unsupervised multitask learners

Reference 27

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Observation cd3a35f4-7912-4c1f-9107-9eca3e22e658 · outbound

This paper cites A Study on ReLU and Softmax in Transformer.

Learning Spectral Methods by Transformers A Study on ReLU and Softmax in Transformer

Reference 28

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Observation 00a2355e-2877-435d-880f-949cbf698d45 · outbound

This paper cites LSEnet: Lorentz Structural Entropy Neural Network for Deep Graph Clustering.

Learning Spectral Methods by Transformers LSEnet: Lorentz Structural Entropy Neural Network for Deep Graph Clustering

Reference 29

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This paper cites Attention is all you need.

Learning Spectral Methods by Transformers Attention is all you need

Reference 30

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Observation cc66db0d-a9bc-414b-be08-9bf6eb649a48 · outbound

This paper cites Transformers learn in- context by gradient descent.

Learning Spectral Methods by Transformers Transformers learn in- context by gradient descent

Reference 31

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Source-reported events for the cited work

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Observation 04acfb6c-5721-4db7-99ea-62e0f1b4debd · outbound

This paper cites High-dimensional statistics: A non-asymptotic viewpoint , vol- ume 48.

Learning Spectral Methods by Transformers High-dimensional statistics: A non-asymptotic viewpoint , vol- ume 48

Reference 32

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Source-reported events for the cited work

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Observation 262ad51b-1627-40f0-a7a1-7d3a97745113 · outbound

This paper cites Trans- formers: State-of-the-art natural language processing.

Learning Spectral Methods by Transformers Trans- formers: State-of-the-art natural language processing

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:36:51.595358Z

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Observation 7fc552c3-865e-4166-9230-35a99cff304d · outbound

This paper cites Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms.

Learning Spectral Methods by Transformers Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms

Reference 34

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This paper cites Self-Attention Networks Can Process Bounded Hierarchical Languages.

Learning Spectral Methods by Transformers Self-Attention Networks Can Process Bounded Hierarchical Languages

Reference 35

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Observation f1ff1e78-352c-441b-91b5-b60963743113 · outbound

This paper cites A useful variant of the davis–kahan theorem for statisticians.

Learning Spectral Methods by Transformers A useful variant of the davis–kahan theorem for statisticians

Reference 36

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Observation 49785f9d-c8a3-4613-90e8-314de27e1ca1 · outbound

This paper cites Are Transformers universal approximators of sequence-to-sequence functions?.

Learning Spectral Methods by Transformers Are Transformers universal approximators of sequence-to-sequence functions?

Reference 37

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Observation e5f7a132-c7d9-46cc-92a8-cc959f2088df · outbound

This paper cites Trained Transformers Learn Linear Models In-Context.

Learning Spectral Methods by Transformers Trained Transformers Learn Linear Models In-Context

Reference 38

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Pith citing papers

Observation 1f2c86cb-6f51-4f1a-9d1e-2723d05b16e4 · inbound

Transformers and Their Roles as Time Series Foundation Models cites this paper.

Transformers and Their Roles as Time Series Foundation Models Learning Spectral Methods by Transformers

Reference 21

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Observation 6ed2557f-9bf8-4aeb-9b7e-50038db5f801 · inbound

Transformers versus the EM Algorithm in Multi-class Clustering cites this paper.

Transformers versus the EM Algorithm in Multi-class Clustering Learning Spectral Methods by Transformers

Reference 13

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Observation a8193d0d-06c2-4a52-88ee-dd373f3e2861 · inbound

Transformers Efficiently Perform In-Context Logistic Regression via Normalized Gradient Descent cites this paper.

Transformers Efficiently Perform In-Context Logistic Regression via Normalized Gradient Descent Learning Spectral Methods by Transformers

Reference 19

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Source-reported events for the cited work

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