Paper Citation Record · LEDGER

Omni-Scale CNNs: a simple and effective kernel size configuration for time series classification

As of 6 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2002.10061.

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

pith.paper-citation-record.v1
2002.10061 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-05T06:32:48.257954+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-04T23:33:57.624119Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-22T08:26:16.650994Z

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 65a999f7-0939-4ced-ba32-b4baf52c6874 · inbound

QualityFM: a Multimodal Physiological Signal Foundation Model with Self-Distillation for Signal Quality Challenges in Critically Ill Patients cites this paper.

QualityFM: a Multimodal Physiological Signal Foundation Model with Self-Distillation for Signal Quality Challenges in Critically Ill Patients Omni-Scale CNNs: a simple and effective kernel size configuration for time series classification

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-04T23:33:57.624119Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 98981134-ea7d-4bb7-b351-d84c0ee96b6f · inbound

Amortized Predictability-aware Training Framework for Time Series Forecasting and Classification cites this paper.

Amortized Predictability-aware Training Framework for Time Series Forecasting and Classification Omni-Scale CNNs: a simple and effective kernel size configuration for time series classification

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-02T22:41:30.983773Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e280bcd0-6805-4a90-85ec-a4867dc8380c · inbound

ROMAN: A Multiscale Routing Operator for Convolutional Time Series Models cites this paper.

ROMAN: A Multiscale Routing Operator for Convolutional Time Series Models Omni-Scale CNNs: a simple and effective kernel size configuration for time series classification

Reference 25

Resolution: verified exact; arxiv_id, observed 2026-05-13T20:53:15.675115Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 59c9ac7d-44e1-4c24-9b47-da8ed643ce63 · inbound

Discrete Prototypical Memories for Federated Time Series Foundation Models cites this paper.

Discrete Prototypical Memories for Federated Time Series Foundation Models Omni-Scale CNNs: a simple and effective kernel size configuration for time series classification

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-05-10T23:35:51.907766Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation d514554e-1e3a-4590-b210-e8bddfb6caef · 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 Omni-Scale CNNs: a simple and effective kernel size configuration for time series classification

Reference 13

Resolution: verified exact; arxiv_id, observed 2026-05-22T08:26:16.655098Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.