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Paper Citation Record · LEDGER

Tensor-to-Tensor Models with Fast Iterated Sum Features

As of 19 August 2026, this Paper Citation Record lists 54 of 54 outbound references and 0 inbound Pith citation observations for arXiv:2506.06041.

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

pith.paper-citation-record.v1
2506.06041 v1

Coverage vector

measured 54 of 54 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T06:06:09.171982Z

measured 54 of 54 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

54 of 54 outbound references displayed

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  • verified fuzzy33
  • unresolved14
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 4fe1a603-e83f-4335-a826-ff0d094e6d4e · outbound

This paper cites Sequence to sequence learning with neural networks.

Tensor-to-Tensor Models with Fast Iterated Sum Features Sequence to sequence learning with neural networks

Reference 1

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Observation 15eb5069-c58a-4854-a45c-0f3867296cd7 · outbound

This paper cites Improving language under- standing by generative pre-training.

Tensor-to-Tensor Models with Fast Iterated Sum Features Improving language under- standing by generative pre-training

Reference 2

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Observation d03f7476-b04c-4b08-8039-a268c3b2ad8d · outbound

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

Tensor-to-Tensor Models with Fast Iterated Sum Features Bert: Pre-training of deep bidirectional transformers for language understanding

Reference 3

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Observation f0949df1-bb04-45b5-ae43-8d6c94c35110 · outbound

This paper cites Attention is all you need.

Tensor-to-Tensor Models with Fast Iterated Sum Features Attention is all you need

Reference 4

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Observation 9c2ff43c-f11e-42bd-afbb-c4df59c3e0ed · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

Tensor-to-Tensor Models with Fast Iterated Sum Features LLaMA: Open and Efficient Foundation Language Models

Reference 5

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Observation 52032947-f830-43d4-84ee-59019899d1f5 · outbound

This paper cites U-net: Convolutional networks for biomedical image segmentation.

Tensor-to-Tensor Models with Fast Iterated Sum Features U-net: Convolutional networks for biomedical image segmentation

Reference 6

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Observation b0479a24-88c8-4ad2-b101-5b4c580627cf · outbound

This paper cites 3d u-net: learning dense volumetric segmentation from sparse annotation.

Tensor-to-Tensor Models with Fast Iterated Sum Features 3d u-net: learning dense volumetric segmentation from sparse annotation

Reference 7

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Observation 7db3fe68-e674-478f-b3a1-f82ce48f8789 · outbound

This paper cites Image-to-image translation with conditional adversarial networks.

Tensor-to-Tensor Models with Fast Iterated Sum Features Image-to-image translation with conditional adversarial networks

Reference 8

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Observation 52aa83d3-d273-4733-90aa-0218a582a7c6 · outbound

This paper cites Video-to-video synthesis.

Tensor-to-Tensor Models with Fast Iterated Sum Features Video-to-video synthesis

Reference 9

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Source-reported events for the cited work

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Observation 8a9eccbc-0b46-4dfa-bcba-e80955db0f89 · outbound

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

Tensor-to-Tensor Models with Fast Iterated Sum Features An image is worth 16x16 words: Transformers for image recognition at scale

Reference 10

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Observation 34665fc1-5b55-44eb-af52-da67b0996b12 · outbound

This paper cites Differential equations driven by rough signals (i): An extension of an inequality of lc young.

Tensor-to-Tensor Models with Fast Iterated Sum Features Differential equations driven by rough signals (i): An extension of an inequality of lc young

Reference 11

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Observation f2a179eb-e918-4741-8e29-5b1d052f44aa · outbound

This paper cites Efficiently Modeling Long Sequences with Structured State Spaces.

Tensor-to-Tensor Models with Fast Iterated Sum Features Efficiently Modeling Long Sequences with Structured State Spaces

Reference 12

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Observation 5c3af193-c2d5-4d85-a1f3-00e29fb70c2e · outbound

This paper cites Mamba: Linear-Time Sequence Modeling with Selective State Spaces.

Tensor-to-Tensor Models with Fast Iterated Sum Features Mamba: Linear-Time Sequence Modeling with Selective State Spaces

Reference 13

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Observation 04be0b4a-d7a0-446a-84be-4e1d7d8b927f · outbound

This paper cites A discrete state-space model for linear image processing.

Tensor-to-Tensor Models with Fast Iterated Sum Features A discrete state-space model for linear image processing

Reference 14

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Source-reported events for the cited work

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Observation 94d7e556-6296-4dac-a7e3-4e3ef868dbab · outbound

This paper cites State space representations of the roesser type for convolutional layers.

Tensor-to-Tensor Models with Fast Iterated Sum Features State space representations of the roesser type for convolutional layers

Reference 15

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Source-reported events for the cited work

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Observation 605aa1d9-9782-47da-a9b8-210bc8798c10 · outbound

This paper cites Vmamba: Visual state space model.

Tensor-to-Tensor Models with Fast Iterated Sum Features Vmamba: Visual state space model

Reference 16

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 18d62166-44da-4068-b081-dea20b481b19 · outbound

This paper cites Theo- retical foundations of deep selective state-space models.

Tensor-to-Tensor Models with Fast Iterated Sum Features Theo- retical foundations of deep selective state-space models

Reference 17

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Observation 878da380-6a3e-499f-b65b-28993b80827c · outbound

This paper cites Integration of paths, geometric invariants and a generalized baker-hausdorff formula.

Tensor-to-Tensor Models with Fast Iterated Sum Features Integration of paths, geometric invariants and a generalized baker-hausdorff formula

Reference 18

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Observation 4084de29-24a5-4d53-aceb-c57ae7c3d3aa · outbound

This paper cites Neural rough differential equa- tions for long time series.

Tensor-to-Tensor Models with Fast Iterated Sum Features Neural rough differential equa- tions for long time series

Reference 19

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Observation b4a9df4a-d6d6-442b-9848-d1cfab42422b · outbound

This paper cites Early prediction of lithium-ion cell degradation trajectories using signatures of voltage curves up to 4-minute sub-sampling rates.

Tensor-to-Tensor Models with Fast Iterated Sum Features Early prediction of lithium-ion cell degradation trajectories using signatures of voltage curves up to 4-minute sub-sampling rates

Reference 20

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Observation cc6567a7-6e5e-4b4e-bd74-e71f923a9476 · outbound

This paper cites FRUITS: Feature extraction using iterated sums for time series classification.

Tensor-to-Tensor Models with Fast Iterated Sum Features FRUITS: Feature extraction using iterated sums for time series classification

Reference 21

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Observation 52e3ebf8-ea0c-4d9f-81c7-1d850d2e1b47 · outbound

This paper cites Signature moments to characterize laws of stochastic pro- cesses.

Tensor-to-Tensor Models with Fast Iterated Sum Features Signature moments to characterize laws of stochastic pro- cesses

Reference 22

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 35882633-5aa7-4ba1-ac33-19f19e4869f3 · outbound

This paper cites Rough transformers: Lightweight and continuous time series modelling through signature patching.

Tensor-to-Tensor Models with Fast Iterated Sum Features Rough transformers: Lightweight and continuous time series modelling through signature patching

Reference 23

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 4780e446-b25f-45f8-b884-005ec214993f · outbound

This paper cites Two-parameter sums signatures and corresponding quasisymmetric functions.

Tensor-to-Tensor Models with Fast Iterated Sum Features Two-parameter sums signatures and corresponding quasisymmetric functions

Reference 24

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 1b2f5ac2-b99c-4550-b5fc-f338b7cd25f6 · outbound

This paper cites Harang, and Samy Tindel.

Tensor-to-Tensor Models with Fast Iterated Sum Features Harang, and Samy Tindel

Reference 25

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Observation 3b4f8005-1fd4-48a7-8508-383b342b13f9 · outbound

This paper cites Two-dimensional signature of images and texture classification.

Tensor-to-Tensor Models with Fast Iterated Sum Features Two-dimensional signature of images and texture classification

Reference 26

Resolution
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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation fc02e8b5-36b3-4c43-8f5b-4c6abbc065f7 · outbound

This paper cites A topological approach to mapping space signatures.

Tensor-to-Tensor Models with Fast Iterated Sum Features A topological approach to mapping space signatures

Reference 27

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 6f6ddfdf-9796-476c-9953-8ec097181059 · outbound

This paper cites Signature matrices of membranes, 2024.

Tensor-to-Tensor Models with Fast Iterated Sum Features Signature matrices of membranes, 2024

Reference 28

Resolution
verified exact
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation b38361e6-6976-414c-be41-1a05f7322199 · outbound

This paper cites Two-dimensional signature of images and texture classification.

Tensor-to-Tensor Models with Fast Iterated Sum Features Two-dimensional signature of images and texture classification

Reference 29

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 09822f78-69bd-4453-afd1-985f52799196 · outbound

This paper cites 2dsig-detect: a semi-supervised framework for anomaly detection on image data using 2d-signatures, 2025.

Tensor-to-Tensor Models with Fast Iterated Sum Features 2dsig-detect: a semi-supervised framework for anomaly detection on image data using 2d-signatures, 2025

Reference 30

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 3bde4698-eb54-4cd5-9320-d3d2cdf0d0da · outbound

This paper cites A multiplicative surface signature through its Magnus expansion.

Tensor-to-Tensor Models with Fast Iterated Sum Features A multiplicative surface signature through its Magnus expansion

Reference 31

Resolution
verified exact
local_arxiv, observed 2026-08-07T06:06:10.503492Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation c5f31dd6-a453-4483-b1da-e0ae55e67b3f · outbound

This paper cites The Surface Signature and Rough Surfaces.

Tensor-to-Tensor Models with Fast Iterated Sum Features The Surface Signature and Rough Surfaces

Reference 32

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unresolved
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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation c3a4b5b1-8741-436b-92b7-95ecf6c7265e · outbound

This paper cites Random surfaces and higher algebra.

Tensor-to-Tensor Models with Fast Iterated Sum Features Random surfaces and higher algebra

Reference 33

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T06:06:07.116733Z digest=sha256:1aedd970c8781d6536c70a04aa4f0734ea6549857909be4927d5178ebd0c0a16

Observation 8158d923-b9fe-43e3-a1e4-935ac6d23c32 · outbound

This paper cites Counting small permutation patterns.

Tensor-to-Tensor Models with Fast Iterated Sum Features Counting small permutation patterns

Reference 34

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raw_fallback, observed 2026-08-07T06:06:11.477588Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation de5d6fcd-a1a0-43ca-ba5f-009ac21ba8fb · outbound

This paper cites Mvtec ad–a comprehensive real-world dataset for unsupervised anomaly detection.

Tensor-to-Tensor Models with Fast Iterated Sum Features Mvtec ad–a comprehensive real-world dataset for unsupervised anomaly detection

Reference 35

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 02281fdd-ca5d-49bf-9843-782e315cdba9 · outbound

This paper cites Time-warping invariants of multidimen- sional time series.

Tensor-to-Tensor Models with Fast Iterated Sum Features Time-warping invariants of multidimen- sional time series

Reference 36

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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This paper cites Tropical time series, iterated-sums signa- tures, and quasisymmetric functions.

Tensor-to-Tensor Models with Fast Iterated Sum Features Tropical time series, iterated-sums signa- tures, and quasisymmetric functions

Reference 37

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Tensor-to-Tensor Models with Fast Iterated Sum Features Prefix sums and their applications

Reference 38

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Observation 835a6ec0-6d00-4192-90ff-d8d87669a876 · outbound

This paper cites Seq2tens: An efficient representation of se- quences by low-rank tensor projections.

Tensor-to-Tensor Models with Fast Iterated Sum Features Seq2tens: An efficient representation of se- quences by low-rank tensor projections

Reference 39

Resolution
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Observation 58650312-7c92-47f7-8f52-5a4a0b2fe7ed · outbound

This paper cites Towards a unifying sequence-to-sequence deep learning layer based on iterated sums.

Tensor-to-Tensor Models with Fast Iterated Sum Features Towards a unifying sequence-to-sequence deep learning layer based on iterated sums

Reference 40

Resolution
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Observation 692de633-52dd-41cc-a654-d495563ee106 · outbound

This paper cites Efficient counting of permutation patterns via double posets.

Tensor-to-Tensor Models with Fast Iterated Sum Features Efficient counting of permutation patterns via double posets

Reference 41

Resolution
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Observation 1a460c88-9447-4973-8f90-70f350e15986 · outbound

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Tensor-to-Tensor Models with Fast Iterated Sum Features Counting Permutation Patterns with Multidimensional Trees

Reference 42

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Observation 28758274-52d9-4c18-865c-6c5895a51d2f · outbound

This paper cites Semirings and their Applications.

Tensor-to-Tensor Models with Fast Iterated Sum Features Semirings and their Applications

Reference 43

Resolution
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Observation b6c63766-da43-49c2-a654-0b3c76931e82 · outbound

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Tensor-to-Tensor Models with Fast Iterated Sum Features Tropical geometry of deep neural networks

Reference 44

Resolution
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Observation ffcc8092-14cf-4399-8ee1-6666cc3a3874 · outbound

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Tensor-to-Tensor Models with Fast Iterated Sum Features Deep residual learning for image recog- nition

Reference 45

Resolution
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Tensor-to-Tensor Models with Fast Iterated Sum Features Image classification codebase

Reference 46

Resolution
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Observation f99e3ff9-7988-4b75-8063-6e81bad6119b · outbound

This paper cites Learning multiple layers of features from tiny images.

Tensor-to-Tensor Models with Fast Iterated Sum Features Learning multiple layers of features from tiny images

Reference 47

Resolution
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Observation 432c680b-9fda-4b1e-be6b-a3c5ba252268 · outbound

This paper cites Texture image analysis and texture classification methods - A review.

Tensor-to-Tensor Models with Fast Iterated Sum Features Texture image analysis and texture classification methods - A review

Reference 48

Resolution
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Observation c037df80-89e4-4884-8265-71968df1dd0e · outbound

This paper cites Anomaly detection in medical imaging-a mini review.

Tensor-to-Tensor Models with Fast Iterated Sum Features Anomaly detection in medical imaging-a mini review

Reference 49

Resolution
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This paper cites Zero-shot versus many-shot: Un- supervised texture anomaly detection.

Tensor-to-Tensor Models with Fast Iterated Sum Features Zero-shot versus many-shot: Un- supervised texture anomaly detection

Reference 50

Resolution
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Observation 342b0293-0f4f-4a30-8c53-ea3902a94453 · outbound

This paper cites Towards total recall in industrial anomaly detection.

Tensor-to-Tensor Models with Fast Iterated Sum Features Towards total recall in industrial anomaly detection

Reference 51

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation b96a9569-fc05-46e6-a29b-f1707e5eb4a2 · outbound

This paper cites Unsupervised Anomaly Detection for X-Ray Images.

Tensor-to-Tensor Models with Fast Iterated Sum Features Unsupervised Anomaly Detection for X-Ray Images

Reference 52

Resolution
verified exact
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Observation 58957b5c-d13e-4fe3-83f6-b022bd638cf9 · outbound

This paper cites Anomaly detection using autoencoders with nonlinear dimen- sionality reduction.

Tensor-to-Tensor Models with Fast Iterated Sum Features Anomaly detection using autoencoders with nonlinear dimen- sionality reduction

Reference 53

Resolution
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Source-reported events for the cited work

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Observation 343af0ea-2f7f-4fd8-828f-9622692bc14c · outbound

This paper cites Attribute Restoration Framework for Anomaly Detection.

Tensor-to-Tensor Models with Fast Iterated Sum Features Attribute Restoration Framework for Anomaly Detection

Reference 54

Resolution
verified exact
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Pith citing papers

No inbound Pith citation observations are available.