Paper Citation Record · LEDGER

Understanding the role of importance weighting for deep learning

As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2103.15209.

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

pith.paper-citation-record.v1
2103.15209 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 4 of 4 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T18:29:35.093196Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-18T15:02:41.151007Z

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 74083d45-1096-4b3b-b64b-ccb0e68455ca · inbound

IDQL: Implicit Q-Learning as an Actor-Critic Method with Diffusion Policies cites this paper.

IDQL: Implicit Q-Learning as an Actor-Critic Method with Diffusion Policies Understanding the role of importance weighting for deep learning

Reference 49

Resolution: metadata mismatch; arxiv_id, observed 2026-05-13T13:48:36.605424Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation c3d7f983-0c1e-4930-b15d-747711764f52 · inbound

Thumb on the Scale: Optimal Loss Weighting in Last Layer Retraining cites this paper.

Thumb on the Scale: Optimal Loss Weighting in Last Layer Retraining Understanding the role of importance weighting for deep learning

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-15T18:29:35.093196Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e3cccd55-5745-4a02-b70b-43e8d9a901d1 · inbound

CaTE Data Curation for Trustworthy AI cites this paper.

CaTE Data Curation for Trustworthy AI Understanding the role of importance weighting for deep learning

Reference 157

Resolution: unresolved; no resolver link, observed 2026-08-05T18:23:32.210229Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation aa4a1626-7f7a-4f88-bea8-b004e8e393df · inbound

LiLAW: Lightweight Learnable Adaptive Weighting to Learn Sample Difficulty & Improve Noisy Training cites this paper.

LiLAW: Lightweight Learnable Adaptive Weighting to Learn Sample Difficulty & Improve Noisy Training Understanding the role of importance weighting for deep learning

Reference 31

Resolution: verified exact; arxiv_id, observed 2026-05-18T15:02:41.153790Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.