Paper Citation Record · LEDGER

Learning Soft Sparse Shapes for Efficient Time-Series Classification

As of 22 August 2026, this Paper Citation Record lists 19 of 19 outbound references and 3 inbound Pith citation observations for arXiv:2505.06892.

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

pith.paper-citation-record.v1
2505.06892 v2

Coverage vector

measured 19 of 19 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T22:35:50.277568Z

measured 22 of 22 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-02T16:45:46.207051Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T16:47:08.968442Z

Reference resolution

19 of 19 outbound references displayed

verified exact1
verified fuzzy10
unresolved7
parse uncertain0
malformed identifier1
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 0f0190e8-11dc-483a-8c2d-d76e70052d29 · outbound

This paper cites To address this, Grabocka et al.

Learning Soft Sparse Shapes for Efficient Time-Series Classification To address this, Grabocka et al

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:35:50.521284Z

Source-reported events for the cited work

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Observation 70e50f06-1a71-4072-b01d-fe44114aecb5 · outbound

This paper cites Crafting papers on machine learning.

Learning Soft Sparse Shapes for Efficient Time-Series Classification Crafting papers on machine learning

Reference 3

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Source-reported events for the cited work

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Observation 9c4f6d5a-ff2f-4ba9-bb48-6f8a1d55b57e · outbound

This paper cites The rateη in Equation (5) is set to 50%.

Learning Soft Sparse Shapes for Efficient Time-Series Classification The rateη in Equation (5) is set to 50%

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:35:50.505190Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation b235d76b-422f-4d67-9996-e41e42c84f8f · outbound

This paper cites and Keogh, E.

Learning Soft Sparse Shapes for Efficient Time-Series Classification and Keogh, E

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:35:50.638736Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 2c81bf25-bebc-49b2-83ea-59b71b02542a · outbound

This paper cites Time series classification from scratch with deep neural networks: A strong base- line.

Learning Soft Sparse Shapes for Efficient Time-Series Classification Time series classification from scratch with deep neural networks: A strong base- line

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:35:50.626317Z

Source-reported events for the cited work

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Observation ac9ce8bb-f50f-4af8-aced-a4cd3b925f85 · outbound

This paper cites Experimental Setup A.1.

Learning Soft Sparse Shapes for Efficient Time-Series Classification Experimental Setup A.1

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:35:50.612777Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 01e67087-294c-4a26-859f-3f1e40849179 · outbound

This paper cites an unresolved cited work.

Learning Soft Sparse Shapes for Efficient Time-Series Classification Unresolved cited work

Reference 9

Resolution: unresolved; raw_fallback, observed 2026-08-15T22:35:50.600888Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 0a5501b2-8f72-49e4-9629-304bc5427f71 · outbound

This paper cites Train” represents the count of samples within the raw training set. “Test.

Learning Soft Sparse Shapes for Efficient Time-Series Classification Train” represents the count of samples within the raw training set. “Test

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:35:50.587232Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 40c890dc-ff17-4b10-a409-e114c53fd079 · outbound

This paper cites an unresolved cited work.

Learning Soft Sparse Shapes for Efficient Time-Series Classification Unresolved cited work

Reference 12

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation e0c32f84-9391-4e47-bba2-a9ce10a7d5f2 · outbound

This paper cites Figure 6 illustrates the critical difference diagram and significance analysis results for SoftShape and the 17 baseline methods on the UCR 128 time series dataset.

Learning Soft Sparse Shapes for Efficient Time-Series Classification Figure 6 illustrates the critical difference diagram and significance analysis results for SoftShape and the 17 baseline methods on the UCR 128 time series dataset

Reference 16

Resolution: malformed identifier; raw_fallback, observed 2026-08-15T22:35:50.490915Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation cd4f24db-130e-4932-b736-7fb1454308b2 · outbound

This paper cites an unresolved cited work.

Learning Soft Sparse Shapes for Efficient Time-Series Classification Unresolved cited work

Reference 21

Resolution: unresolved; raw_fallback, observed 2026-08-15T22:35:50.475579Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation c6b2b03d-d6d0-4af2-bea9-1af052f396c7 · outbound

This paper cites The results demonstrate that SoftShape outperforms TS2Vec and TimesNet, highlighting its potential for time series forecasting tasks.

Learning Soft Sparse Shapes for Efficient Time-Series Classification The results demonstrate that SoftShape outperforms TS2Vec and TimesNet, highlighting its potential for time series forecasting tasks

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:35:50.453697Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 74025487-0704-476e-84d8-ca4e8d1026a7 · outbound

This paper cites Among these, w/o II refers to the w/o Intra & Inter method.

Learning Soft Sparse Shapes for Efficient Time-Series Classification Among these, w/o II refers to the w/o Intra & Inter method

Reference 25

Resolution: verified exact; raw_fallback, observed 2026-08-15T22:35:50.386523Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 24911b9e-2432-4483-87dd-f92fbc0a3adb · outbound

This paper cites For all UCR datasets, we apply a uniform normalization strategy to standardize each time series within the dataset (Ismail Fawaz et al., 2019).

Learning Soft Sparse Shapes for Efficient Time-Series Classification For all UCR datasets, we apply a uniform normalization strategy to standardize each time series within the dataset (Ismail Fawaz et al., 2019)

Reference 500

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:35:50.538425Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 93cf84dc-794c-403d-91ce-106e9262ec3c · outbound

This paper cites F., Weber, J., Webb, G.

Learning Soft Sparse Shapes for Efficient Time-Series Classification F., Weber, J., Webb, G

Reference 2019

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:35:50.661493Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation be0837b0-367e-41c9-9fa3-390c07db4ada · outbound

This paper cites Moirai-MoE: Empowering Time Series Foundation Models with Sparse Mixture of Experts.

Learning Soft Sparse Shapes for Efficient Time-Series Classification Moirai-MoE: Empowering Time Series Foundation Models with Sparse Mixture of Experts

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-15T22:35:50.205726Z

Source-reported events for the cited work

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Observation f8c28014-47f0-4bcb-8585-5dfb90f6257e · outbound

This paper cites ST-MoE: Designing Stable and Transferable Sparse Expert Models.

Learning Soft Sparse Shapes for Efficient Time-Series Classification ST-MoE: Designing Stable and Transferable Sparse Expert Models

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-15T22:35:50.221056Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3998a186-6f5a-4b32-a11d-57fbc2746bc7 · outbound

This paper cites Furthermore, Middlehurst et al.

Learning Soft Sparse Shapes for Efficient Time-Series Classification Furthermore, Middlehurst et al

Reference 2023

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:35:50.569435Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 8ded6d7d-eaad-4e3c-81bd-ade3b6614bd1 · outbound

This paper cites Self-Supervised Time Series Representation Learning by Inter-Intra Relational Reasoning.

Learning Soft Sparse Shapes for Efficient Time-Series Classification Self-Supervised Time Series Representation Learning by Inter-Intra Relational Reasoning

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-15T22:35:50.192096Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation 3b8d9b7c-5965-49cc-9d84-bfb7cbd7d411 · inbound

VTBench: A Multimodal Framework for Time-Series Classification with Chart-Based Representations cites this paper.

VTBench: A Multimodal Framework for Time-Series Classification with Chart-Based Representations Learning Soft Sparse Shapes for Efficient Time-Series Classification

Reference 41

Resolution: verified exact; arxiv_id, observed 2026-05-12T09:31:26.948606Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation c011edac-2e6f-4813-b04b-4bf7383335bc · inbound

Prototype-Guided Classification Sub-Task Decoupling Framework: Enhancing Generalization and Interpretability for Multivariate Time Series cites this paper.

Prototype-Guided Classification Sub-Task Decoupling Framework: Enhancing Generalization and Interpretability for Multivariate Time Series Learning Soft Sparse Shapes for Efficient Time-Series Classification

Reference 7

Resolution: verified exact; arxiv_id, observed 2026-05-22T08:26:16.613487Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation d0076976-7e6f-4b31-b63f-2b090f35ab81 · inbound

PRISM: Prioritized Channel Importance with Semi-supervised Domain Adaptation for Cross-Subject EEG Emotion Recognition cites this paper.

PRISM: Prioritized Channel Importance with Semi-supervised Domain Adaptation for Cross-Subject EEG Emotion Recognition Learning Soft Sparse Shapes for Efficient Time-Series Classification

Reference 30

Resolution: verified exact; arxiv_id, observed 2026-07-02T16:47:08.970152Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.