Paper Citation Record · LEDGER

Accelerating Deep Learning by Focusing on the Biggest Losers

As of 23 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 20 inbound Pith citation observations for arXiv:1910.00762.

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

pith.paper-citation-record.v1
1910.00762 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 20 of 20 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+00:00

measured 20 of 20 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T11:46:10.887709Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-09T21:36:34.301049Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
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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 4f29ae8e-cd8e-4f1a-a6d0-fbee41d63afa · inbound

Active Data Curation Effectively Distills Large-Scale Multimodal Models cites this paper.

Active Data Curation Effectively Distills Large-Scale Multimodal Models Accelerating Deep Learning by Focusing on the Biggest Losers

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-12T11:06:35.928215Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0ffc683c-152c-4b98-ba90-78dfe9aadd15 · inbound

Navigating Towards Fairness with Data Selection cites this paper.

Navigating Towards Fairness with Data Selection Accelerating Deep Learning by Focusing on the Biggest Losers

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-11T15:26:06.844056Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a908a46f-4f5f-44e6-9726-cf66d8072390 · inbound

Dynamic Loss-Based Sample Reweighting for Improved Large Language Model Pretraining cites this paper.

Dynamic Loss-Based Sample Reweighting for Improved Large Language Model Pretraining Accelerating Deep Learning by Focusing on the Biggest Losers

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-08T14:36:36.447693Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 06c97e87-7ea7-41db-8e80-f661fde2a238 · inbound

Instance-dependent Early Stopping cites this paper.

Instance-dependent Early Stopping Accelerating Deep Learning by Focusing on the Biggest Losers

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-08T12:26:16.257485Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 68a5714a-3a2a-4692-b3ba-bddf26efcd18 · inbound

Step-Video-T2V Technical Report: The Practice, Challenges, and Future of Video Foundation Model cites this paper.

Step-Video-T2V Technical Report: The Practice, Challenges, and Future of Video Foundation Model Accelerating Deep Learning by Focusing on the Biggest Losers

Reference 271

Resolution: metadata mismatch; arxiv_id, observed 2026-05-19T08:02:23.824379Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 658cc2a6-ce97-4eb2-93cf-fe04e7e4dc14 · inbound

Learning to Reason at the Frontier of Learnability cites this paper.

Learning to Reason at the Frontier of Learnability Accelerating Deep Learning by Focusing on the Biggest Losers

Reference 49

Resolution: verified exact; arxiv_id, observed 2026-05-23T02:42:26.150995Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 68b72752-dc11-450d-8b71-772d457855d0 · inbound

DONOD: Efficient and Generalizable Instruction Fine-Tuning for LLMs via Model-Intrinsic Dataset Pruning cites this paper.

DONOD: Efficient and Generalizable Instruction Fine-Tuning for LLMs via Model-Intrinsic Dataset Pruning Accelerating Deep Learning by Focusing on the Biggest Losers

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-16T11:46:10.887709Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5ed818e2-10a8-43cd-b75e-02687ffcc794 · inbound

ESLM: Risk-Averse Selective Language Modeling for Efficient Pretraining cites this paper.

ESLM: Risk-Averse Selective Language Modeling for Efficient Pretraining Accelerating Deep Learning by Focusing on the Biggest Losers

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-07T14:09:46.918110Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fcb8d62d-5ead-4797-9508-4f68d79521a4 · inbound

Partial Forward Blocking: A Novel Data Pruning Paradigm for Lossless Training Acceleration cites this paper.

Partial Forward Blocking: A Novel Data Pruning Paradigm for Lossless Training Acceleration Accelerating Deep Learning by Focusing on the Biggest Losers

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-06T21:40:00.639939Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 341e7fa7-af14-4d0c-b79b-1637b8d87d7e · inbound

LoReUn: Data Itself Implicitly Provides Cues to Improve Machine Unlearning cites this paper.

LoReUn: Data Itself Implicitly Provides Cues to Improve Machine Unlearning Accelerating Deep Learning by Focusing on the Biggest Losers

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-06T11:42:04.362742Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f55bedfc-995d-4ca4-a5da-e2a153648eb4 · inbound

Disagreement-Regularized Importance Sampling for Adversarial Label Corruption cites this paper.

Disagreement-Regularized Importance Sampling for Adversarial Label Corruption Accelerating Deep Learning by Focusing on the Biggest Losers

Reference 13

Resolution: verified exact; arxiv_id, observed 2026-05-11T03:55:56.290650Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 028b931e-1aa0-44f1-b926-49f75764240f · inbound

The Long-Term Effects of Data Selection in LLM Fine-Tuning cites this paper.

The Long-Term Effects of Data Selection in LLM Fine-Tuning Accelerating Deep Learning by Focusing on the Biggest Losers

Reference 7

Resolution: verified exact; arxiv_id, observed 2026-06-29T09:03:15.777982Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation b254516f-de78-4596-9f2f-96ed2cee9c53 · inbound

Initialization is Half the Battle: Generating Diverse Images from a Guidance Potential Posterior cites this paper.

Initialization is Half the Battle: Generating Diverse Images from a Guidance Potential Posterior Accelerating Deep Learning by Focusing on the Biggest Losers

Reference 60

Resolution: metadata mismatch; arxiv_id, observed 2026-07-01T22:36:16.677863Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 0e37cd66-ff1a-46e5-944e-f304143f2208 · inbound

Policy-based Foveated Imaging and Perception cites this paper.

Policy-based Foveated Imaging and Perception Accelerating Deep Learning by Focusing on the Biggest Losers

Reference 113

Resolution: metadata mismatch; arxiv_id, observed 2026-07-01T22:26:17.709541Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 24a21a2f-edfb-4965-8cc6-753a2b516ac9 · inbound

RCAP: Robust, Class-Aware, Probabilistic Dynamic Dataset Pruning cites this paper.

RCAP: Robust, Class-Aware, Probabilistic Dynamic Dataset Pruning Accelerating Deep Learning by Focusing on the Biggest Losers

Reference 64

Resolution: verified exact; arxiv_id, observed 2026-07-03T10:17:57.254614Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 426dd18d-ac07-4abd-ba49-2ceb066a3d65 · inbound

Selecting Samples on Graphs: A Unified Dataset Pruning Framework for Lossless Training Acceleration cites this paper.

Selecting Samples on Graphs: A Unified Dataset Pruning Framework for Lossless Training Acceleration Accelerating Deep Learning by Focusing on the Biggest Losers

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-07-03T13:58:21.852363Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 71c3e757-2a33-4aa1-8d8b-910b8529285d · inbound

CHERRY: Compressed Hierarchical Experts with Recurrent Representational Yield cites this paper.

CHERRY: Compressed Hierarchical Experts with Recurrent Representational Yield Accelerating Deep Learning by Focusing on the Biggest Losers

Reference 15

Resolution: verified exact; arxiv_id, observed 2026-07-01T10:15:44.041091Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation a84e778b-3129-44ba-956a-98ded106cd35 · inbound

CHERRY: Compressed Hierarchical Experts with Recurrent Representational Yield cites this paper.

CHERRY: Compressed Hierarchical Experts with Recurrent Representational Yield Accelerating Deep Learning by Focusing on the Biggest Losers

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-02T09:24:13.455779Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6fa11cb3-c45e-4604-a5d0-26e6dcbe42e2 · inbound

K-ABENA: K-Adaptive Backpropagation with Error-based N-exclusion Algorithm : (Compensated Loss-Based Sample Exclusion with Unbiased Gradient Estimation) cites this paper.

K-ABENA: K-Adaptive Backpropagation with Error-based N-exclusion Algorithm : (Compensated Loss-Based Sample Exclusion with Unbiased Gradient Estimation) Accelerating Deep Learning by Focusing on the Biggest Losers

Reference 6

Resolution: verified exact; local_arxiv, observed 2026-07-08T21:35:37.692623Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation cf79d1f3-b2de-4387-8e7d-80d01529b784 · inbound

Online Data Selection Is Implicit Alignment cites this paper.

Online Data Selection Is Implicit Alignment Accelerating Deep Learning by Focusing on the Biggest Losers

Reference 63

Resolution: verified exact; local_arxiv, observed 2026-07-09T21:36:34.302303Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.