Learning to Predict Global Atrial Fibrillation Dynamics from Sparse Measurements | Paper Citation Record

Observation e217b78c-44e5-4fef-b735-5e3dfd3fdb97 · outbound

This paper cites Lifetime risk of atrial fibrillation by race and socioeconomic status: ARIC study (Atherosclerosis Risk in Communities).

Learning to Predict Global Atrial Fibrillation Dynamics from Sparse Measurements Lifetime risk of atrial fibrillation by race and socioeconomic status: ARIC study (Atherosclerosis Risk in Communities)

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T21:26:18.192201Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 4d747394-6b7d-432d-9f3a-206e361eebea · outbound

This paper cites Global epidemiology of atrial fibrillation: An increasing epidemic and public health challenge.

Learning to Predict Global Atrial Fibrillation Dynamics from Sparse Measurements Global epidemiology of atrial fibrillation: An increasing epidemic and public health challenge

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T21:26:18.176907Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation b581d9a8-3878-48f3-8c57-5a751591663c · outbound

This paper cites Stroke prevention in atrial fibrillation: Looking forward.

Learning to Predict Global Atrial Fibrillation Dynamics from Sparse Measurements Stroke prevention in atrial fibrillation: Looking forward

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T21:26:18.161377Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 89b30c6e-e891-4b58-a80d-1e8d5d39f0cc · outbound

This paper cites Atrial fibrillation in heart failure: Epidemiology, pathophysiology, and rationale for therapy.

Learning to Predict Global Atrial Fibrillation Dynamics from Sparse Measurements Atrial fibrillation in heart failure: Epidemiology, pathophysiology, and rationale for therapy

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T21:26:18.145286Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation cd3a1696-8e75-4c47-a277-3b54e22a93fa · outbound

This paper cites Impact of atrial fibrillation on mortality, stroke, and medical costs.

Learning to Predict Global Atrial Fibrillation Dynamics from Sparse Measurements Impact of atrial fibrillation on mortality, stroke, and medical costs

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T21:26:18.129154Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 951afd73-9169-417a-8c0e-e56fd35564cc · outbound

This paper cites Cost of an emerging epidemic: An economic analysis of atrial fibrillation in the UK.

Learning to Predict Global Atrial Fibrillation Dynamics from Sparse Measurements Cost of an emerging epidemic: An economic analysis of atrial fibrillation in the UK

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T21:26:18.114209Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation d501f69a-a866-45de-bf86-9ef0dd6c6fb5 · outbound

This paper cites Atrial fibrillation burden and clinical outcomes in heart failure: The CASTLE-AF trial.

Learning to Predict Global Atrial Fibrillation Dynamics from Sparse Measurements Atrial fibrillation burden and clinical outcomes in heart failure: The CASTLE-AF trial

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T21:26:18.098145Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 9bc04046-418a-47a5-975d-b4fb79783df4 · outbound

This paper cites an unresolved cited work.

Learning to Predict Global Atrial Fibrillation Dynamics from Sparse Measurements Unresolved cited work

Reference 8

Resolution: unresolved; raw_fallback, observed 2026-08-07T21:26:18.082920Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 04149d2d-a2df-4056-96f7-f2fd3effd32f · outbound

This paper cites Cryoballoon or radiofrequency ablation for paroxysmal atrial fibrillation.

Learning to Predict Global Atrial Fibrillation Dynamics from Sparse Measurements Cryoballoon or radiofrequency ablation for paroxysmal atrial fibrillation

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T21:26:18.068013Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 52a69be1-7b17-4cd7-8781-a60899afbe74 · outbound

This paper cites Five-year outcome of catheter ablation of persistent atrial fibrillation using termination of atrial fibrillation as a procedural endpoint.

Learning to Predict Global Atrial Fibrillation Dynamics from Sparse Measurements Five-year outcome of catheter ablation of persistent atrial fibrillation using termination of atrial fibrillation as a procedural endpoint

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T21:26:18.048799Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 10639a52-a38f-4b97-ae79-c99efe11ec79 · outbound

This paper cites The electrical isolation of the left atrial posterior wall in catheter ablation of persistent atrial fibrillation.

Learning to Predict Global Atrial Fibrillation Dynamics from Sparse Measurements The electrical isolation of the left atrial posterior wall in catheter ablation of persistent atrial fibrillation

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T21:26:18.033320Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation de6d4a6d-72f9-42fd-aa4e-d7bff5d3fb4c · outbound

This paper cites Approaches to catheter ablation for persistent atrial fibrillation.

Learning to Predict Global Atrial Fibrillation Dynamics from Sparse Measurements Approaches to catheter ablation for persistent atrial fibrillation

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T21:26:18.016052Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 02345e7c-2f21-4aae-9be2-a919c25d33f4 · outbound

This paper cites Pulmonary vein isolation versus defragmentation: The CHASE-AF clinical trial.

Learning to Predict Global Atrial Fibrillation Dynamics from Sparse Measurements Pulmonary vein isolation versus defragmentation: The CHASE-AF clinical trial

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T21:26:17.997004Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation b43d98a2-3752-4e2c-b4ba-7165e5bc2172 · outbound

This paper cites Narayan et al.

Learning to Predict Global Atrial Fibrillation Dynamics from Sparse Measurements Narayan et al

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T21:26:17.981395Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 3529a1b2-5b13-44cc-8920-c07c739c4901 · outbound

This paper cites No benefit of complex fractionated atrial electrogram ablation in addition to circumferential pulmonary vein ablation and linear ablation: Benefit of complex ablation study.

Learning to Predict Global Atrial Fibrillation Dynamics from Sparse Measurements No benefit of complex fractionated atrial electrogram ablation in addition to circumferential pulmonary vein ablation and linear ablation: Benefit of complex ablation study

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T21:26:17.966272Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 28b708fb-28f8-4112-9ef5-e1aab4fce9a5 · outbound

This paper cites Toward mechanism-directed electrophenotype-based treatments for atrial fibrillation.

Learning to Predict Global Atrial Fibrillation Dynamics from Sparse Measurements Toward mechanism-directed electrophenotype-based treatments for atrial fibrillation

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T21:26:17.950344Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation abf42888-dd5d-404f-9486-d5a7cea2df1a · outbound

This paper cites High-density and high coverage composite mapping of repetitive atrial activation patterns.

Learning to Predict Global Atrial Fibrillation Dynamics from Sparse Measurements High-density and high coverage composite mapping of repetitive atrial activation patterns

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T21:26:17.934998Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation ca6774f0-97c4-4118-a0ef-cd172a036bff · outbound

This paper cites Long-term clinical outcomes of focal impulse and rotor modulation for treatment of atrial fibrillation: A multicenter experience.

Learning to Predict Global Atrial Fibrillation Dynamics from Sparse Measurements Long-term clinical outcomes of focal impulse and rotor modulation for treatment of atrial fibrillation: A multicenter experience

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T21:26:17.919623Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 0232edb9-7e51-4f64-b2c5-c5c95151a10d · outbound

This paper cites Noninvasive electrocardiographic imaging.

Learning to Predict Global Atrial Fibrillation Dynamics from Sparse Measurements Noninvasive electrocardiographic imaging

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T21:26:17.904405Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation d7d3a64a-d5a4-42cb-984b-7ad00f09871b · outbound

This paper cites Spatial resolution requirements for accurate identification of drivers of atrial fibrillation.

Learning to Predict Global Atrial Fibrillation Dynamics from Sparse Measurements Spatial resolution requirements for accurate identification of drivers of atrial fibrillation

Reference 20

Resolution: malformed identifier; raw_fallback, observed 2026-08-07T21:26:17.889893Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 0a8f6f91-07b2-481b-85a4-7ba6374ca794 · outbound

This paper cites Synchronization of pulse-coupled biological oscillators.

Learning to Predict Global Atrial Fibrillation Dynamics from Sparse Measurements Synchronization of pulse-coupled biological oscillators

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T21:26:17.873561Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 0c5c32ab-b55b-4956-a1cd-b04ed7eb4126 · outbound

This paper cites The fundamental organization of cardiac mitochondria as a network of coupled oscillators.

Learning to Predict Global Atrial Fibrillation Dynamics from Sparse Measurements The fundamental organization of cardiac mitochondria as a network of coupled oscillators

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T21:26:17.857779Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation ee4f8d50-03d8-4664-8623-52e48617f8c7 · outbound

This paper cites Nonlinear and stochastic dynamics in the heart.

Learning to Predict Global Atrial Fibrillation Dynamics from Sparse Measurements Nonlinear and stochastic dynamics in the heart

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T21:26:17.841803Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation f609b744-6e6e-4072-862b-b4280fb2899b · outbound

This paper cites The graph neural network model.

Learning to Predict Global Atrial Fibrillation Dynamics from Sparse Measurements The graph neural network model

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T21:26:17.826688Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 46d8cc3d-d637-4c89-b510-75aec8551450 · outbound

This paper cites Geometric deep learning: Going beyond Euclidean data.

Learning to Predict Global Atrial Fibrillation Dynamics from Sparse Measurements Geometric deep learning: Going beyond Euclidean data

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T21:26:17.812087Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 7148f6c7-5bf0-4cea-8b18-a1df20a5ab29 · outbound

This paper cites A gentle introduction to deep learning for graphs.

Learning to Predict Global Atrial Fibrillation Dynamics from Sparse Measurements A gentle introduction to deep learning for graphs

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T21:26:17.797665Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 0571e587-e2c9-4ee3-9708-52ad9267c036 · outbound

This paper cites Structured sequence modeling with graph convolutional recurrent networks.

Learning to Predict Global Atrial Fibrillation Dynamics from Sparse Measurements Structured sequence modeling with graph convolutional recurrent networks

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T21:26:17.782404Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 018e5b30-6d63-4aae-a8e1-1dd2719ea327 · outbound

This paper cites Filling the g_ap_s: Multivariate time series imputation by graph neural networks.

Learning to Predict Global Atrial Fibrillation Dynamics from Sparse Measurements Filling the g_ap_s: Multivariate time series imputation by graph neural networks

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T21:26:17.766752Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 44d57024-f847-4e75-839e-9063a725a2ab · outbound

This paper cites Word embedding for understanding natural language: A survey.

Learning to Predict Global Atrial Fibrillation Dynamics from Sparse Measurements Word embedding for understanding natural language: A survey

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T21:26:17.750913Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 60cfe5b8-3211-43ec-9050-e2f102e61666 · outbound

This paper cites an unresolved cited work.

Learning to Predict Global Atrial Fibrillation Dynamics from Sparse Measurements Unresolved cited work

Reference 30

Resolution: unresolved; raw_fallback, observed 2026-08-07T21:26:17.735174Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 92444e3c-aab6-4da5-be28-e0cc27666b21 · outbound

This paper cites Validation of dipole density mapping during atrial fibrillation and sinus rhythm in human left atrium.

Learning to Predict Global Atrial Fibrillation Dynamics from Sparse Measurements Validation of dipole density mapping during atrial fibrillation and sinus rhythm in human left atrium

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T21:26:17.720422Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 3faed7d9-c8cb-49d5-8b82-d26c172fb237 · outbound

This paper cites Standardised framework for quantitative analysis of fibrillation dynamics.

Learning to Predict Global Atrial Fibrillation Dynamics from Sparse Measurements Standardised framework for quantitative analysis of fibrillation dynamics

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T21:26:17.704677Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 34767c37-4f46-4e5e-a795-1f0903ab1cee · outbound

This paper cites Visualizing data using t-SNE.

Learning to Predict Global Atrial Fibrillation Dynamics from Sparse Measurements Visualizing data using t-SNE

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T21:26:17.689490Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 4d6a8147-9a06-49ae-ab80-b1abaca7b7e9 · outbound

This paper cites Recurrence plots for the analysis of complex systems.

Learning to Predict Global Atrial Fibrillation Dynamics from Sparse Measurements Recurrence plots for the analysis of complex systems

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T21:26:17.673720Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 62c0b627-e587-45c1-b5b7-9df785834289 · outbound

This paper cites V ortex dynamics in three-dimensional continuous myocardium with fiber rotation: Filament instability and fibrillation.

Learning to Predict Global Atrial Fibrillation Dynamics from Sparse Measurements V ortex dynamics in three-dimensional continuous myocardium with fiber rotation: Filament instability and fibrillation

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T21:26:17.657095Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 3556f9a9-ef4d-422c-a312-2eadc9f96477 · outbound

This paper cites Models of cardiac tissue electrophysiology: Progress, challenges and open questions.

Learning to Predict Global Atrial Fibrillation Dynamics from Sparse Measurements Models of cardiac tissue electrophysiology: Progress, challenges and open questions

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T21:26:17.641244Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 8dab83ec-0d21-4daa-b198-ca83cf4debc9 · outbound

This paper cites Graph-based time series clustering for end-to-end hierarchical forecasting.

Learning to Predict Global Atrial Fibrillation Dynamics from Sparse Measurements Graph-based time series clustering for end-to-end hierarchical forecasting

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T21:26:17.624639Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 7d6b6672-5dc3-4485-abbe-a2d74c2b732c · outbound

This paper cites Learning to reconstruct missing data from spatiotemporal graphs with sparse observations.

Learning to Predict Global Atrial Fibrillation Dynamics from Sparse Measurements Learning to reconstruct missing data from spatiotemporal graphs with sparse observations

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T21:26:17.608994Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 140f0832-1485-42ad-8f08-8a4500598c09 · outbound

This paper cites Graph signal processing: Overview, challenges, and applications.

Learning to Predict Global Atrial Fibrillation Dynamics from Sparse Measurements Graph signal processing: Overview, challenges, and applications

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T21:26:17.592984Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation fe881b69-35e2-4ae2-90d5-2a6c7bebf29f · outbound

This paper cites Data analytics on graphs. Part II: Signals on graphs.

Learning to Predict Global Atrial Fibrillation Dynamics from Sparse Measurements Data analytics on graphs. Part II: Signals on graphs

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T21:26:17.528705Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 2c9c9811-50b3-4dc9-b211-27a8b1cda53c · outbound

This paper cites Data analytics on graphs. Part III: Machine learning on graphs, from graph topology to applications.

Learning to Predict Global Atrial Fibrillation Dynamics from Sparse Measurements Data analytics on graphs. Part III: Machine learning on graphs, from graph topology to applications

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T21:26:17.488192Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 938523c3-2179-4e26-a5bf-62185b2b0734 · outbound

This paper cites Semi-supervised classification with graph convolutional networks.

Learning to Predict Global Atrial Fibrillation Dynamics from Sparse Measurements Semi-supervised classification with graph convolutional networks

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T21:26:17.472579Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation a7d9179b-b47f-4270-abe0-374d4fcd8ada · outbound

This paper cites Deep learning on graphs: A survey.

Learning to Predict Global Atrial Fibrillation Dynamics from Sparse Measurements Deep learning on graphs: A survey

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T21:26:17.457279Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 9f53eb21-5b8c-4fae-b961-c9bb5c4187d5 · outbound

This paper cites Graph Deep Learning for Time Series Forecasting.

Learning to Predict Global Atrial Fibrillation Dynamics from Sparse Measurements Graph Deep Learning for Time Series Forecasting

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-07T21:26:16.767377Z

Source-reported events for the cited work

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Observation 6b4803ce-a26a-4bba-a03a-e4ce20e47558 · outbound

This paper cites A survey on graph neural networks for time series: Forecasting, classification, imputation, and anomaly detection.

Learning to Predict Global Atrial Fibrillation Dynamics from Sparse Measurements A survey on graph neural networks for time series: Forecasting, classification, imputation, and anomaly detection

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T21:26:17.440899Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation ff47e89a-28d9-4558-a8f4-2f05267ddfb6 · outbound

This paper cites On the equivalence between temporal and static equivariant graph representations.

Learning to Predict Global Atrial Fibrillation Dynamics from Sparse Measurements On the equivalence between temporal and static equivariant graph representations

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T21:26:17.424946Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 03b43292-9d5b-4823-8536-c22795f64aee · outbound

This paper cites Scalable spatiotemporal graph neural networks.

Learning to Predict Global Atrial Fibrillation Dynamics from Sparse Measurements Scalable spatiotemporal graph neural networks

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T21:26:17.401722Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 67798dd0-e3f5-452a-8b9c-bd4bd991d58b · outbound

This paper cites Diffusion convolutional recurrent neural network: Data-driven traffic forecasting.

Learning to Predict Global Atrial Fibrillation Dynamics from Sparse Measurements Diffusion convolutional recurrent neural network: Data-driven traffic forecasting

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T21:26:17.385526Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 9d465f91-c511-4669-918c-680442ed3b32 · outbound

This paper cites Graph neural network for traffic forecasting: A survey.

Learning to Predict Global Atrial Fibrillation Dynamics from Sparse Measurements Graph neural network for traffic forecasting: A survey

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T21:26:17.369254Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 20568478-cb3e-4dbd-995b-d08e8d7bd00c · outbound

This paper cites Learning skillful medium-range global weather forecasting.

Learning to Predict Global Atrial Fibrillation Dynamics from Sparse Measurements Learning skillful medium-range global weather forecasting

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T21:26:17.354071Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation cb073b49-1f8c-4397-9a00-5057d0143c5e · outbound

This paper cites Taming local effects in graph-based spatiotemporal forecasting.

Learning to Predict Global Atrial Fibrillation Dynamics from Sparse Measurements Taming local effects in graph-based spatiotemporal forecasting

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T21:26:17.337820Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 430ddccb-e6ef-4d23-b5db-bc0d996cacf2 · outbound

This paper cites NodeTrans: A Graph Transfer Learning Approach for Traffic Prediction.

Learning to Predict Global Atrial Fibrillation Dynamics from Sparse Measurements NodeTrans: A Graph Transfer Learning Approach for Traffic Prediction

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-07T21:26:16.809356Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 90e22ec2-1c3c-4e9b-a268-3cf2ffb633a2 · outbound

This paper cites Bayesian probabilistic matrix factorization using Markov chain Monte Carlo.

Learning to Predict Global Atrial Fibrillation Dynamics from Sparse Measurements Bayesian probabilistic matrix factorization using Markov chain Monte Carlo

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T21:26:17.321463Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 6e495288-5568-4465-8f05-0ae92396d3b5 · outbound

This paper cites Algorithms for non-negative matrix factorization.

Learning to Predict Global Atrial Fibrillation Dynamics from Sparse Measurements Algorithms for non-negative matrix factorization

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T21:26:17.304195Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 1d502099-73f0-4bea-9363-7ff59429b243 · outbound

This paper cites Graph regularized nonnegative matrix factorization for data representation.

Learning to Predict Global Atrial Fibrillation Dynamics from Sparse Measurements Graph regularized nonnegative matrix factorization for data representation

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T21:26:17.287705Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 56ede9c5-1766-4c5a-ac80-07cfdd146d4e · outbound

This paper cites Temporal regularized matrix factorization for high-dimensional time series prediction.

Learning to Predict Global Atrial Fibrillation Dynamics from Sparse Measurements Temporal regularized matrix factorization for high-dimensional time series prediction

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T21:26:17.271233Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 8ccba2cd-601b-49e2-ad21-6bba8c71b94e · outbound

This paper cites BRITS: Bidirectional recurrent imputation for time series.

Learning to Predict Global Atrial Fibrillation Dynamics from Sparse Measurements BRITS: Bidirectional recurrent imputation for time series

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T21:26:17.254424Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 3c5f93b0-5b26-43b1-b358-f7963a9c4308 · outbound

This paper cites Time-series generative adversarial networks.

Learning to Predict Global Atrial Fibrillation Dynamics from Sparse Measurements Time-series generative adversarial networks

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T21:26:17.237870Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 598f5c9b-1488-4e3e-b9dd-125dfe252d5f · outbound

This paper cites NAOMI: Non-autoregressive multiresolution sequence imputation.

Learning to Predict Global Atrial Fibrillation Dynamics from Sparse Measurements NAOMI: Non-autoregressive multiresolution sequence imputation

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T21:26:17.221556Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 753581bd-8bfe-4def-b634-c85dc2254ea2 · outbound

This paper cites SAITS: Self-attention-based imputation for time series.

Learning to Predict Global Atrial Fibrillation Dynamics from Sparse Measurements SAITS: Self-attention-based imputation for time series

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T21:26:17.205100Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 764e54b1-67d4-4ee1-be20-fcf6a13fd5f3 · outbound

This paper cites CSDI: Conditional score-based diffusion models for probabilistic time series imputation.

Learning to Predict Global Atrial Fibrillation Dynamics from Sparse Measurements CSDI: Conditional score-based diffusion models for probabilistic time series imputation

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T21:26:17.188650Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 4187c99a-df05-4d3b-bc5d-921ee4ce9e36 · outbound

This paper cites Diffusion-based time series imputation and forecasting with structured state space models.

Learning to Predict Global Atrial Fibrillation Dynamics from Sparse Measurements Diffusion-based time series imputation and forecasting with structured state space models

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T21:26:17.170933Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 385c746d-f16e-4b93-94ee-8f36efc9e7b1 · outbound

This paper cites Improving Diffusion Models for ECG Imputation with an Augmented Template Prior.

Learning to Predict Global Atrial Fibrillation Dynamics from Sparse Measurements Improving Diffusion Models for ECG Imputation with an Augmented Template Prior

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-07T21:26:16.863257Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 541acb7e-ab85-4b0f-91d6-16bf282ca22f · outbound

This paper cites Principles and algorithms for forecasting groups of time series: Locality and globality.

Learning to Predict Global Atrial Fibrillation Dynamics from Sparse Measurements Principles and algorithms for forecasting groups of time series: Locality and globality

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T21:26:17.152199Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 3aca7c53-e87d-4228-8850-27fac98ef3ea · outbound

This paper cites Residual Gated Graph ConvNets.

Learning to Predict Global Atrial Fibrillation Dynamics from Sparse Measurements Residual Gated Graph ConvNets

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-07T21:26:16.875312Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9888efaa-2a2e-4795-b991-2ddfd7bf157c · outbound

This paper cites Diffusion-convolutional neural networks.

Learning to Predict Global Atrial Fibrillation Dynamics from Sparse Measurements Diffusion-convolutional neural networks

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T21:26:17.135958Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 2799ed74-47c7-4b1d-bdb7-7a19cfd27af0 · outbound

This paper cites Quantile regression.

Learning to Predict Global Atrial Fibrillation Dynamics from Sparse Measurements Quantile regression

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T21:26:17.120131Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation c66ff37b-1458-43a8-9f66-7a839365cfda · outbound

This paper cites Laplacian eigenmaps for dimensionality reduction and data representation.

Learning to Predict Global Atrial Fibrillation Dynamics from Sparse Measurements Laplacian eigenmaps for dimensionality reduction and data representation

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T21:26:17.102760Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 444847be-15df-499b-802a-b54139339877 · outbound

This paper cites Method for registration of 3-D shapes.

Learning to Predict Global Atrial Fibrillation Dynamics from Sparse Measurements Method for registration of 3-D shapes

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T21:26:17.085939Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation db4f455e-85f7-446e-adf8-4a0bd23e30e0 · outbound

This paper cites an unresolved cited work.

Learning to Predict Global Atrial Fibrillation Dynamics from Sparse Measurements Unresolved cited work

Reference 70

Resolution: unresolved; raw_fallback, observed 2026-08-07T21:26:17.070043Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 286da05b-7d20-49ed-80d6-0c7cff01adfa · outbound

This paper cites an unresolved cited work.

Learning to Predict Global Atrial Fibrillation Dynamics from Sparse Measurements Unresolved cited work

Reference 71

Resolution: unresolved; raw_fallback, observed 2026-08-07T21:26:17.055148Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 5ac8b1ae-6b36-45c7-bf92-ab763f2e41dd · outbound

This paper cites Mean and MF baseline models are employed solely on the test set due to their transductive nature.

Learning to Predict Global Atrial Fibrillation Dynamics from Sparse Measurements Mean and MF baseline models are employed solely on the test set due to their transductive nature

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T21:26:17.040013Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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