Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Deep Learning on a Data Diet: Finding Important Examples Early in Training
As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 9 inbound Pith citation observations for arXiv:2107.07075.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-06T23:51:48.265454Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-02T02:56:29.498177Z
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 d5572fed-d1d8-421f-afe8-bfba04f75597 · inbound
Quality over Quantity: An Effective Large-Scale Data Reduction Strategy Based on Pointwise V-Information cites this paper.
Quality over Quantity: An Effective Large-Scale Data Reduction Strategy Based on Pointwise V-Information Deep Learning on a Data Diet: Finding Important Examples Early in Training
Reference 40
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T23:51:48.265454Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 91c61dae-04be-4e7b-8bcf-15ce4ceec057 · inbound
GRAFT: Gradient-Aware Fast MaxVol Technique for Dynamic Data Sampling cites this paper.
GRAFT: Gradient-Aware Fast MaxVol Technique for Dynamic Data Sampling Deep Learning on a Data Diet: Finding Important Examples Early in Training
Reference 1
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T19:04:53.951203Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 22619208-d700-4da3-9bbf-7fbac00d73cb · 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 Deep Learning on a Data Diet: Finding Important Examples Early in Training
Reference 19
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-18T15:02:41.223402Z
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.
Observation b4de92bb-889d-4422-ac3f-a635ae3a8e05 · inbound
VisNec: Measuring and Leveraging Visual Necessity for Multimodal Instruction Tuning cites this paper.
VisNec: Measuring and Leveraging Visual Necessity for Multimodal Instruction Tuning Deep Learning on a Data Diet: Finding Important Examples Early in Training
Reference 24
- Resolution
- unresolved
- no resolver link, observed 2026-08-02T19:46:20.705950Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation ed43de6f-c7a0-4be2-869a-85a72305a2e2 · inbound
Less Data, Faster Convergence: Goal-Driven Data Optimization for Multimodal Instruction Tuning cites this paper.
Less Data, Faster Convergence: Goal-Driven Data Optimization for Multimodal Instruction Tuning Deep Learning on a Data Diet: Finding Important Examples Early in Training
Reference 43
- Resolution
- unresolved
- no resolver link, observed 2026-07-14T22:18:56.115559Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 9fded019-b5a9-4826-8349-26b1af9e9ed9 · inbound
Gradient-Discrepancy Acquisition for Pool-Based Active Learning cites this paper.
Gradient-Discrepancy Acquisition for Pool-Based Active Learning Deep Learning on a Data Diet: Finding Important Examples Early in Training
Reference 9
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-09T06:15:37.314329Z
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.
Observation 7f109a3a-4575-41a0-9ada-0f5c9cdd58e3 · inbound
Gradient-Discrepancy Acquisition for Pool-Based Active Learning cites this paper.
Gradient-Discrepancy Acquisition for Pool-Based Active Learning Deep Learning on a Data Diet: Finding Important Examples Early in Training
Reference 10
- Resolution
- verified exact
- arxiv_id, observed 2026-05-19T17:22:42.037579Z
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.
Observation 4a9de1ea-d4e7-4468-9f60-85013a181a9f · inbound
Once-For-All: A Train-Once and Select-Anytime Framework for Multimodal Instruction Tuning cites this paper.
Once-For-All: A Train-Once and Select-Anytime Framework for Multimodal Instruction Tuning Deep Learning on a Data Diet: Finding Important Examples Early in Training
Reference 16
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-06-29T18:33:50.138290Z
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.
Observation b571ffd5-6ef9-4e19-8889-8eadc72f92bb · inbound
Edge of Stability Selectively Shapes Learning Across the Data Distribution cites this paper.
Edge of Stability Selectively Shapes Learning Across the Data Distribution Deep Learning on a Data Diet: Finding Important Examples Early in Training
Reference 31
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-07-02T02:56:29.500557Z
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.