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Paper Citation Record · LEDGER

Interpolation-Prediction Networks for Irregularly Sampled Time Series

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:1909.07782.

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

pith.paper-citation-record.v1
1909.07782 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 8 of 8 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T14:22:29.706805Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-29T00:02:50.282083Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation c4590bd9-83f3-4b82-ab20-e7ee10ac793b · inbound

Universal Time-Series Representation Learning: A Survey cites this paper.

Universal Time-Series Representation Learning: A Survey Interpolation-Prediction Networks for Irregularly Sampled Time Series

Reference 178

Resolution
verified exact
arxiv_id, observed 2026-05-24T04:28:53.323836Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-24T04:26:45.527625Z digest=sha256:b630f0530ef10220146cb4853291bf610df247b2a400a7f07b62b732653314f2

Observation cb10db71-24b8-4c97-9be8-d4cc4dc1e52e · inbound

A Comprehensive Survey of Electronic Health Record Modeling: From Deep Learning Approaches to Large Language Models cites this paper.

A Comprehensive Survey of Electronic Health Record Modeling: From Deep Learning Approaches to Large Language Models Interpolation-Prediction Networks for Irregularly Sampled Time Series

Reference 254

Resolution
unresolved
no resolver link, observed 2026-08-06T16:42:42.236291Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T16:42:42.236291Z digest=sha256:0176fd6d8e31e9d56b8e97162878aa259a57f12d765727a830a36c48e863e4a0

Observation a175d9db-5779-442d-9e23-be66334051d8 · inbound

DBGL: Decay-aware Bipartite Graph Learning for Irregular Medical Time Series Classification cites this paper.

DBGL: Decay-aware Bipartite Graph Learning for Irregular Medical Time Series Classification Interpolation-Prediction Networks for Irregularly Sampled Time Series

Reference 8

Resolution
verified exact
arxiv_id, observed 2026-05-11T10:21:00.684185Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-10T15:33:14.027429Z digest=sha256:c40ca9061d8d0b17363d32be8f4f95e3c5920d9233d8e109b24d54b865a1621a

Observation 7f26dcd8-d9f9-49d1-a9e3-43ee3ddda445 · inbound

Deep Kernel Learning for Stratifying Glaucoma Trajectories cites this paper.

Deep Kernel Learning for Stratifying Glaucoma Trajectories Interpolation-Prediction Networks for Irregularly Sampled Time Series

Reference 6

Resolution
verified exact
arxiv_id, observed 2026-05-11T15:36:10.654807Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-05-09T19:32:15.656522Z digest=sha256:7a6c1ba68c8f227267405f9fccf2ca70ff151b4c50ec982b6e1632153029bbd1

Observation d0133bd1-cf7b-42db-8646-b49d5befac6e · inbound

Transformer autoencoder with local attention for sparse and irregular time series with application on risk estimation cites this paper.

Transformer autoencoder with local attention for sparse and irregular time series with application on risk estimation Interpolation-Prediction Networks for Irregularly Sampled Time Series

Reference 22

Resolution
verified exact
arxiv_id, observed 2026-05-12T06:46:34.279401Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-12T04:00:56.702894Z digest=sha256:e03a5b5a292f40a5287514c2402641726ce1680889148192d4249f1a30530c2c

Observation 16126549-4583-4d44-9a58-1038ec05d86d · inbound

ReTAMamba: Reliability-Aware Temporal Aggregation with Mamba for Irregular Clinical Time Series Prediction cites this paper.

ReTAMamba: Reliability-Aware Temporal Aggregation with Mamba for Irregular Clinical Time Series Prediction Interpolation-Prediction Networks for Irregularly Sampled Time Series

Reference 27

Resolution
verified exact
arxiv_id, observed 2026-05-20T22:13:47.065556Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-20T22:12:09.565448Z digest=sha256:30200a0c0af24573806c1795482a533143f61fe575b888a5848f93f4872f52e2

Observation e7f3cf18-cb93-41e8-867c-5e53b9eb7ddf · inbound

GlucoFM: A Dual-Stream Foundation Model for Continuous Glucose Monitoring cites this paper.

GlucoFM: A Dual-Stream Foundation Model for Continuous Glucose Monitoring Interpolation-Prediction Networks for Irregularly Sampled Time Series

Reference 46

Resolution
verified exact
arxiv_id, observed 2026-06-29T00:02:50.283774Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-06-28T23:23:38.624741Z digest=sha256:67320fe26e5d27e9ba260d65dfdadc45ab6fad835d3182a7e9a523096be3e60b

Observation cae8dcd4-576f-41c6-8c39-1400091f9334 · inbound

DoctorAgents: an agentic framework to iteratively refine AutoML pipeline for small clinical temporal data cites this paper.

DoctorAgents: an agentic framework to iteratively refine AutoML pipeline for small clinical temporal data Interpolation-Prediction Networks for Irregularly Sampled Time Series

Reference 28

Resolution
unresolved
no resolver link, observed 2026-08-08T14:22:29.706805Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T14:22:29.706805Z digest=sha256:af83ba348c3d65e318cf2711fb8384e546ac6658312523d4048e5301c9248736