Paper Citation Record · LEDGER

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

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 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 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T00:32:57.583608Z

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 620bc0a2-32fb-4c34-bac1-a0fdd71aaedc · inbound

Finding Optimal Kernel Size and Dimension in Convolutional Neural Networks An Architecture Optimization Approach cites this paper.

Finding Optimal Kernel Size and Dimension in Convolutional Neural Networks An Architecture Optimization Approach Omni-Scale CNNs: a simple and effective kernel size configuration for time series classification

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-07T00:32:57.583608Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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 2026-08-08T06:32:00.761636+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 2026-08-08T06:32:00.761636+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 2026-08-08T06:32:00.761636+00:00.