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Source: paper_references, paper_reference_links, observed 2026-05-24T10:58:21.620110Z
Paper Citation Record · LEDGER
Universal hidden monotonic trend estimation with contrastive learning
As of 22 July 2026, this Paper Citation Record lists 49 of 49 outbound references and 0 inbound Pith citation observations for arXiv:2210.09817.
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Reference resolution
49 of 49 outbound references displayed
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- verified fuzzy45
- unresolved0
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Outbound references
Observation 6a59b7bc-e814-47ae-a6fd-9b4df7d28017 · outbound
This paper cites Development of a new method of wavelet aided trend detection and estimation.
Universal hidden monotonic trend estimation with contrastive learning Development of a new method of wavelet aided trend detection and estimation
Reference 1
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Observation 21add529-2c6b-40a9-855f-412445ae0431 · outbound
This paper cites and Poveda Germ´ an.
Universal hidden monotonic trend estimation with contrastive learning and Poveda Germ´ an
Reference 2
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No event found in the named queried sources as of 2026-07-22T06:31:00.163083+00:00.
Observation dd4f8ee9-09e7-441b-a951-ad24c08c36e6 · outbound
This paper cites Self-supervised representation learning from electroencephalography signals.
Universal hidden monotonic trend estimation with contrastive learning Self-supervised representation learning from electroencephalography signals
Reference 3
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-22T06:31:00.163083+00:00.
Observation 5305ffdf-6593-4f03-9f01-2b9807cd6f16 · outbound
This paper cites Representation learning: A review and new perspectives.
Universal hidden monotonic trend estimation with contrastive learning Representation learning: A review and new perspectives
Reference 4
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-22T06:31:00.163083+00:00.
Observation 18f1a6ad-9f3f-4da0-b483-ca5f64f40ac2 · outbound
This paper cites Analysis of survival data by the proportional odds model.
Universal hidden monotonic trend estimation with contrastive learning Analysis of survival data by the proportional odds model
Reference 5
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-22T06:31:00.163083+00:00.
Observation 931dc2e0-8d50-475f-a427-a49ade3e095e · outbound
This paper cites Independent slow feature analysis and nonlinear blind source separation.
Universal hidden monotonic trend estimation with contrastive learning Independent slow feature analysis and nonlinear blind source separation
Reference 6
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-22T06:31:00.163083+00:00.
Observation ac3d3712-c918-46c0-9b46-1b4f2c6e5fd9 · outbound
This paper cites Design of multivariate alarm systems based on online calculation of variational directions.
Universal hidden monotonic trend estimation with contrastive learning Design of multivariate alarm systems based on online calculation of variational directions
Reference 7
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-22T06:31:00.163083+00:00.
Observation 4fe37fcd-021f-40c7-b9d9-abbcf1be0713 · outbound
This paper cites Blind source separation and independent component analysis: A review.
Universal hidden monotonic trend estimation with contrastive learning Blind source separation and independent component analysis: A review
Reference 8
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-22T06:31:00.163083+00:00.
Observation f879d590-aab1-4859-acc2-4819f0f0c0f5 · outbound
This paper cites Regression models and life-tables.
Universal hidden monotonic trend estimation with contrastive learning Regression models and life-tables
Reference 9
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-22T06:31:00.163083+00:00.
Observation 0fb453ab-21f0-47a0-9368-bee7492d22e9 · outbound
This paper cites PySurvival: Open source package for survival analysis modeling, 2019–.
Universal hidden monotonic trend estimation with contrastive learning PySurvival: Open source package for survival analysis modeling, 2019–
Reference 10
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-22T06:31:00.163083+00:00.
Observation 5becc34d-b6e4-4cc2-b64a-a7e6a34ccef0 · outbound
This paper cites Unsupervised scalable representation learning for multivariate time series.
Universal hidden monotonic trend estimation with contrastive learning Unsupervised scalable representation learning for multivariate time series
Reference 11
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- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-22T06:31:00.163083+00:00.
Observation 1ee9c58d-4ec7-4373-be5d-815035b0e3b2 · outbound
This paper cites Robust parameter estimation with a small bias against heavy contamination.
Universal hidden monotonic trend estimation with contrastive learning Robust parameter estimation with a small bias against heavy contamination
Reference 12
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-22T06:31:00.163083+00:00.
Observation 110a5703-ba7a-499c-b345-083d0c1d2a56 · outbound
This paper cites Robust Loss Functions under Label Noise for Deep Neural Networks.
Universal hidden monotonic trend estimation with contrastive learning Robust Loss Functions under Label Noise for Deep Neural Networks
Reference 13
- Resolution
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- local_arxiv, observed 2026-05-24T10:59:21.517572Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-22T06:31:00.163083+00:00.
Observation c6862f0f-14f3-4145-8519-90ac1ee12f64 · outbound
This paper cites an unresolved cited work.
Universal hidden monotonic trend estimation with contrastive learning Unresolved cited work
Reference 14
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-22T06:31:00.163083+00:00.
Observation 0008cb21-d453-414e-90e0-9448a0f6fdcb · outbound
This paper cites Econ 2 0a: Sufficiency, minimal sufficiency and the exponential family of distributions.
Universal hidden monotonic trend estimation with contrastive learning Econ 2 0a: Sufficiency, minimal sufficiency and the exponential family of distributions
Reference 15
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-22T06:31:00.163083+00:00.
Observation a0aef040-11b7-42d6-9b43-f70fe3f87e58 · outbound
This paper cites Monitoring for conservation and ecology , volume 3.
Universal hidden monotonic trend estimation with contrastive learning Monitoring for conservation and ecology , volume 3
Reference 16
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-22T06:31:00.163083+00:00.
Observation 6c385065-fae9-4617-af70-6a6c7c69784f · outbound
This paper cites Comparison of trend detection methods.
Universal hidden monotonic trend estimation with contrastive learning Comparison of trend detection methods
Reference 17
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-22T06:31:00.163083+00:00.
Observation cc6abaed-a196-4627-82fb-7765282d0089 · outbound
This paper cites Likelihood- free inference via classification.
Universal hidden monotonic trend estimation with contrastive learning Likelihood- free inference via classification
Reference 18
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-24T10:59:22.161952Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-22T06:31:00.163083+00:00.
Observation ca492908-9ee3-4968-a3fb-3ee70cd8987d · outbound
This paper cites A Survey of Label-noise Representation Learning: Past, Present and Future.
Universal hidden monotonic trend estimation with contrastive learning A Survey of Label-noise Representation Learning: Past, Present and Future
Reference 19
- Resolution
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- arxiv_id, observed 2026-05-24T10:59:21.523701Z
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No event found in the named queried sources as of 2026-07-22T06:31:00.163083+00:00.
Observation aaf5f3e6-a4f3-41a7-9913-949d08a30cf0 · outbound
This paper cites Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors.
Universal hidden monotonic trend estimation with contrastive learning Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors
Reference 20
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-24T10:59:22.151921Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-22T06:31:00.163083+00:00.
Observation 38b6e26a-959f-48a9-9011-10bacb94bcec · outbound
This paper cites 10 structural time series models.
Universal hidden monotonic trend estimation with contrastive learning 10 structural time series models
Reference 21
- Resolution
- verified fuzzy
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Source-reported events for the cited work
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Observation 086483c4-9643-4f47-975a-af75f0635ee7 · outbound
This paper cites The empirical mode decomposition and the hilbert spectrum for nonlinear and non-stationary time series analysis.
Universal hidden monotonic trend estimation with contrastive learning The empirical mode decomposition and the hilbert spectrum for nonlinear and non-stationary time series analysis
Reference 22
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-24T10:59:22.180322Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-22T06:31:00.163083+00:00.
Observation 28a22f11-38c4-4a7e-9f84-87137fd783e7 · outbound
This paper cites Meaningful trend in climate time series: A discussion based on linear and smoothing techniques for drought analysis in taiwan.
Universal hidden monotonic trend estimation with contrastive learning Meaningful trend in climate time series: A discussion based on linear and smoothing techniques for drought analysis in taiwan
Reference 23
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-24T10:59:22.148935Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-22T06:31:00.163083+00:00.
Observation cadd38f9-efa1-486f-ba12-d6de299fb8d9 · outbound
This paper cites Unsupervised feature extraction by time- contrastive learning and nonlinear ica.
Universal hidden monotonic trend estimation with contrastive learning Unsupervised feature extraction by time- contrastive learning and nonlinear ica
Reference 24
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-24T10:59:22.189985Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-22T06:31:00.163083+00:00.
Observation 6a93718e-10d3-4bdd-b626-cea6e757b407 · outbound
This paper cites Independent component analysis: algorithms and applications.
Universal hidden monotonic trend estimation with contrastive learning Independent component analysis: algorithms and applications
Reference 25
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-24T10:59:22.168089Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-22T06:31:00.163083+00:00.
Observation 076d04af-4382-425e-8b6f-49377074af31 · outbound
This paper cites Nonlinear ica using auxiliary variables and generalized contrastive learning.
Universal hidden monotonic trend estimation with contrastive learning Nonlinear ica using auxiliary variables and generalized contrastive learning
Reference 26
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-24T10:59:22.183529Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-22T06:31:00.163083+00:00.
Observation e6281294-6a37-4243-88d5-13d403fbf559 · outbound
This paper cites Random survival forests.
Universal hidden monotonic trend estimation with contrastive learning Random survival forests
Reference 27
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-24T10:59:22.186380Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-22T06:31:00.163083+00:00.
Observation 71fd5060-f66b-4c04-9a1c-4ce8c8be9af9 · outbound
This paper cites A deep survival analysis method based on ranking.
Universal hidden monotonic trend estimation with contrastive learning A deep survival analysis method based on ranking
Reference 28
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-22T06:31:00.163083+00:00.
Observation b89bf86d-1857-44f1-860c-f320860b52ab · outbound
This paper cites Deepsurv: personalized treatment recommender system using a cox proportional hazards deep neural network.
Universal hidden monotonic trend estimation with contrastive learning Deepsurv: personalized treatment recommender system using a cox proportional hazards deep neural network
Reference 29
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-24T10:59:22.173740Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-22T06:31:00.163083+00:00.
Observation 3b46b1ae-cc6e-4965-974f-8300a212f8d7 · outbound
This paper cites an unresolved cited work.
Universal hidden monotonic trend estimation with contrastive learning Unresolved cited work
Reference 30
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-22T06:31:00.163083+00:00.
Observation feb878ef-097e-4582-bc38-c7334a8ccb5e · outbound
This paper cites Adam: A Method for Stochastic Optimization.
Universal hidden monotonic trend estimation with contrastive learning Adam: A Method for Stochastic Optimization
Reference 31
- Resolution
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No event found in the named queried sources as of 2026-07-22T06:31:00.163083+00:00.
Observation c3c23e2b-2999-4a4b-9632-31d722d3ecee · outbound
This paper cites Analysis of soil moisture trends in europe using rank-based and empirical decomposition approaches.
Universal hidden monotonic trend estimation with contrastive learning Analysis of soil moisture trends in europe using rank-based and empirical decomposition approaches
Reference 32
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-22T06:31:00.163083+00:00.
Observation 478fcd8c-232e-4f88-9445-58924e181e2d · outbound
This paper cites Contrastive representation learning: A framework and review.
Universal hidden monotonic trend estimation with contrastive learning Contrastive representation learning: A framework and review
Reference 33
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-22T06:31:00.163083+00:00.
Observation ba41fbd1-773f-4add-a14c-19a74d113087 · outbound
This paper cites an unresolved cited work.
Universal hidden monotonic trend estimation with contrastive learning Unresolved cited work
Reference 34
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-24T10:59:22.139289Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-22T06:31:00.163083+00:00.
Observation b78a836f-4c0c-4887-bafc-ed90dce9d359 · outbound
This paper cites Survival analysis.
Universal hidden monotonic trend estimation with contrastive learning Survival analysis
Reference 35
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-22T06:31:00.163083+00:00.
Observation d3ff66af-de3e-498e-aef0-2bc153489e3f · outbound
This paper cites Techniques of trend analysis in degradation-based prognostics.
Universal hidden monotonic trend estimation with contrastive learning Techniques of trend analysis in degradation-based prognostics
Reference 36
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-24T10:59:22.204760Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-22T06:31:00.163083+00:00.
Observation 191f5124-d0ed-46a0-94bf-1eb8eae7f85f · outbound
This paper cites Time series source sep- aration with slow flows.
Universal hidden monotonic trend estimation with contrastive learning Time series source sep- aration with slow flows
Reference 37
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-24T10:59:22.103958Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-22T06:31:00.163083+00:00.
Observation 50f1cc9a-9e27-44e6-97db-064e8be44e96 · outbound
This paper cites Unsupervised ageing detection of mechanical systems on a causality graph.
Universal hidden monotonic trend estimation with contrastive learning Unsupervised ageing detection of mechanical systems on a causality graph
Reference 38
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-22T06:31:00.163083+00:00.
Observation 71d95acc-73f4-40cf-8bf0-eaeae71f10f3 · outbound
This paper cites Robust contrastive learning and nonlinear ica in the presence of outliers.
Universal hidden monotonic trend estimation with contrastive learning Robust contrastive learning and nonlinear ica in the presence of outliers
Reference 39
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-24T10:59:22.107116Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-22T06:31:00.163083+00:00.
Observation 4184576f-1dc0-423b-bd8d-a6c435a3cad3 · outbound
This paper cites Turbofan engine degradation simulation data set.
Universal hidden monotonic trend estimation with contrastive learning Turbofan engine degradation simulation data set
Reference 40
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-24T10:59:22.126306Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-22T06:31:00.163083+00:00.
Observation 482c370a-8aa6-4eff-97d6-553d812d29d3 · outbound
This paper cites Hlynsson, and Laurenz Wiskott.
Universal hidden monotonic trend estimation with contrastive learning Hlynsson, and Laurenz Wiskott
Reference 41
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-24T10:59:22.129470Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-22T06:31:00.163083+00:00.
Observation cc9634e1-09d5-4cd1-8f21-9f5d370c28b1 · outbound
This paper cites Randomized 2 x 2 trial evaluating hormonal treatment and the duration of chemotherapy in node-positive breast cancer patients.
Universal hidden monotonic trend estimation with contrastive learning Randomized 2 x 2 trial evaluating hormonal treatment and the duration of chemotherapy in node-positive breast cancer patients
Reference 42
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-24T10:59:22.133068Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-22T06:31:00.163083+00:00.
Observation 5217d6bc-c3c3-49c2-bf21-bf39dc81ed55 · outbound
This paper cites On ranking in survival analysis: Bounds on the concordance index.
Universal hidden monotonic trend estimation with contrastive learning On ranking in survival analysis: Bounds on the concordance index
Reference 43
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-24T10:59:22.201330Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-22T06:31:00.163083+00:00.
Observation 4fafcc0d-5a5d-426d-bc9f-f0cdd0ca3c50 · outbound
This paper cites Likelihood-free inference by ratio estimation.
Universal hidden monotonic trend estimation with contrastive learning Likelihood-free inference by ratio estimation
Reference 44
- Resolution
- verified exact
- arxiv_id, observed 2026-05-24T10:59:21.529523Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-22T06:31:00.163083+00:00.
Observation d049a13f-ba1d-41b0-beb9-cfa2a5984af9 · outbound
This paper cites Unsupervised learning of visual representations using videos.
Universal hidden monotonic trend estimation with contrastive learning Unsupervised learning of visual representations using videos
Reference 45
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-24T10:59:22.143023Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-22T06:31:00.163083+00:00.
Observation 50f69644-70ed-4125-adda-165003dc3cb7 · outbound
This paper cites Vegetation dynamic trends and the main drivers detected using the en- semble empirical mode decomposition method in east africa.
Universal hidden monotonic trend estimation with contrastive learning Vegetation dynamic trends and the main drivers detected using the en- semble empirical mode decomposition method in east africa
Reference 46
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-24T10:59:22.146074Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-22T06:31:00.163083+00:00.
Observation 2692575a-7ed6-4e5d-88c5-e60e18dbe3b3 · outbound
This paper cites Slow feature analysis: Unsupervised learn- ing of invariances.
Universal hidden monotonic trend estimation with contrastive learning Slow feature analysis: Unsupervised learn- ing of invariances
Reference 47
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-24T10:59:22.119632Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-22T06:31:00.163083+00:00.
Observation be4790fd-ee4a-4b23-be4b-28bb4e73f579 · outbound
This paper cites Learning patient- specific cancer survival distributions as a sequence of dependent regressors.
Universal hidden monotonic trend estimation with contrastive learning Learning patient- specific cancer survival distributions as a sequence of dependent regressors
Reference 48
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-24T10:59:22.123270Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-22T06:31:00.163083+00:00.
Observation 6950a2de-5bd2-4fca-8c55-f0e3c3529c42 · outbound
This paper cites Serial-emd: Fast empirical mode decomposition method for multi- dimensional signals based on serialization.
Universal hidden monotonic trend estimation with contrastive learning Serial-emd: Fast empirical mode decomposition method for multi- dimensional signals based on serialization
Reference 49
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-24T10:59:22.113258Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-22T06:31:00.163083+00:00.
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