Paper Citation Record · LEDGER

GRAFT: Gradient-Aware Fast MaxVol Technique for Dynamic Data Sampling

As of 9 August 2026, this Paper Citation Record lists 31 of 31 outbound references and 0 inbound Pith citation observations for arXiv:2508.13653.

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

pith.paper-citation-record.v1
2508.13653 v2

Coverage vector

measured 31 of 31 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T19:04:56.880181Z

measured 31 of 31 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

31 of 31 outbound references displayed

verified exact0
verified fuzzy27
unresolved2
parse uncertain0
malformed identifier1
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 91c61dae-04be-4e7b-8bcf-15ce4ceec057 · outbound

This paper cites Deep Learning on a Data Diet: Finding Important Examples Early in Training.

GRAFT: Gradient-Aware Fast MaxVol Technique for Dynamic Data Sampling Deep Learning on a Data Diet: Finding Important Examples Early in Training

Reference 1

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b8afbe19-a35b-4ef9-a3b5-a30c0df036fe · outbound

This paper cites An empirical study of example forgetting during deep neural network learning.

GRAFT: Gradient-Aware Fast MaxVol Technique for Dynamic Data Sampling An empirical study of example forgetting during deep neural network learning

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T19:05:03.233364Z

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 fb79dc72-ab6e-413a-8de6-c757ed73f8a4 · outbound

This paper cites Selection via proxy: Efficient data selection for deep learning.

GRAFT: Gradient-Aware Fast MaxVol Technique for Dynamic Data Sampling Selection via proxy: Efficient data selection for deep learning

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T19:05:03.021197Z

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 fa5913ef-f394-4b06-a79b-e130895b8842 · outbound

This paper cites DRoP: Distributionally Robust Data Pruning.

GRAFT: Gradient-Aware Fast MaxVol Technique for Dynamic Data Sampling DRoP: Distributionally Robust Data Pruning

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T19:05:02.903054Z

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 dbaf04df-bbb2-4789-9bf5-44b0e2b896bc · outbound

This paper cites GradMatch: Gradient Matching Based Data Subset Selection for Efficient Deep Model Training.

GRAFT: Gradient-Aware Fast MaxVol Technique for Dynamic Data Sampling GradMatch: Gradient Matching Based Data Subset Selection for Efficient Deep Model Training

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T19:05:02.725052Z

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 f7027ed6-ed93-4b37-ab18-43d3c7164bc5 · outbound

This paper cites Glister: Generalization based data subset selection for efficient and robust learning.

GRAFT: Gradient-Aware Fast MaxVol Technique for Dynamic Data Sampling Glister: Generalization based data subset selection for efficient and robust learning

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T19:05:02.462879Z

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 88269f93-e9fa-4784-9116-64f27edd7ac2 · outbound

This paper cites Coresets for Data-efficient Training of Machine Learning Models.

GRAFT: Gradient-Aware Fast MaxVol Technique for Dynamic Data Sampling Coresets for Data-efficient Training of Machine Learning Models

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T19:05:02.178808Z

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 2f4ec392-be2c-4f3f-bae9-a620529377ef · outbound

This paper cites Deep batch active learning by diverse, uncertain gradient lower bounds.

GRAFT: Gradient-Aware Fast MaxVol Technique for Dynamic Data Sampling Deep batch active learning by diverse, uncertain gradient lower bounds

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T19:05:01.955558Z

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 684c8a30-2688-44a1-b431-b58bfc5d7b37 · outbound

This paper cites Moderate Coreset: A Universal Method of Data Selection for Real-world Data-efficient Deep Learning.

GRAFT: Gradient-Aware Fast MaxVol Technique for Dynamic Data Sampling Moderate Coreset: A Universal Method of Data Selection for Real-world Data-efficient Deep Learning

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T19:05:01.638165Z

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 43a77f81-96c6-4259-b02c-2a0a7375f4de · outbound

This paper cites DRPN: Making CNN Dynamically Handle Scale Variation.

GRAFT: Gradient-Aware Fast MaxVol Technique for Dynamic Data Sampling DRPN: Making CNN Dynamically Handle Scale Variation

Reference 10

Resolution: metadata mismatch; local_arxiv, observed 2026-08-05T19:04:57.283146Z

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 c8b41643-d0c7-4aa9-9974-116627620aab · outbound

This paper cites SelMatch: Selection-based Dataset Distillation via Trajectory Matching.

GRAFT: Gradient-Aware Fast MaxVol Technique for Dynamic Data Sampling SelMatch: Selection-based Dataset Distillation via Trajectory Matching

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T19:05:01.418294Z

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 15fc38bf-d399-4fe8-8441-f700c5d05803 · outbound

This paper cites Efficient data subset selection to generalize training across models: Transductive and inductive networks.

GRAFT: Gradient-Aware Fast MaxVol Technique for Dynamic Data Sampling Efficient data subset selection to generalize training across models: Transductive and inductive networks

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T19:05:01.131737Z

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 033fb48d-2caa-45f0-ad68-97274f938ac0 · outbound

This paper cites Column subset selection and Nyström approximation via continuous optimization.

GRAFT: Gradient-Aware Fast MaxVol Technique for Dynamic Data Sampling Column subset selection and Nyström approximation via continuous optimization

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T19:05:00.856223Z

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 20992de4-c0e8-439d-9224-86e3d7bb0f68 · outbound

This paper cites Incomplete cross approximation in the mosaic-skeleton method.

GRAFT: Gradient-Aware Fast MaxVol Technique for Dynamic Data Sampling Incomplete cross approximation in the mosaic-skeleton method

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T19:05:00.571185Z

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 594c6a59-9999-478d-8a68-9e01029ad52a · outbound

This paper cites How to find a good submatrix.

GRAFT: Gradient-Aware Fast MaxVol Technique for Dynamic Data Sampling How to find a good submatrix

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T19:05:00.316821Z

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 4f2e686e-3fa7-4c74-b765-0fb82506c359 · outbound

This paper cites Optimization for machine learning.

GRAFT: Gradient-Aware Fast MaxVol Technique for Dynamic Data Sampling Optimization for machine learning

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T19:05:00.017992Z

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 b89e93be-f9aa-4bdf-acff-b599d62737cd · outbound

This paper cites Finding structure with randomness: Probabilistic algorithms for constructing approximate matrix decompositions.

GRAFT: Gradient-Aware Fast MaxVol Technique for Dynamic Data Sampling Finding structure with randomness: Probabilistic algorithms for constructing approximate matrix decompositions

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T19:04:59.769883Z

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 e02076dd-e625-4cd1-a024-d2e4e217956a · outbound

This paper cites Matrix Computations.

GRAFT: Gradient-Aware Fast MaxVol Technique for Dynamic Data Sampling Matrix Computations

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T19:04:59.435703Z

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 2a736fa4-17f4-4d7e-a3f3-b6b3df39abf6 · outbound

This paper cites Eco2ai: carbon emissions tracking of machine learning models as the first step towards sustainable ai.

GRAFT: Gradient-Aware Fast MaxVol Technique for Dynamic Data Sampling Eco2ai: carbon emissions tracking of machine learning models as the first step towards sustainable ai

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T19:04:59.216184Z

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 e5bea3a7-41ce-4acb-a0b2-186c77c0d0e9 · outbound

This paper cites CIFAR-10 and CIFAR-100 Datasets.

GRAFT: Gradient-Aware Fast MaxVol Technique for Dynamic Data Sampling CIFAR-10 and CIFAR-100 Datasets

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T19:04:59.016811Z

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 4f47729b-f796-43d9-8921-739917c704bb · outbound

This paper cites Tiny ImageNet Visual Recognition Challenge.

GRAFT: Gradient-Aware Fast MaxVol Technique for Dynamic Data Sampling Tiny ImageNet Visual Recognition Challenge

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T19:04:58.852293Z

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 cb857d33-a4a6-4034-9a48-f0ac461c39f1 · outbound

This paper cites Caltech-256 Object Category Dataset.

GRAFT: Gradient-Aware Fast MaxVol Technique for Dynamic Data Sampling Caltech-256 Object Category Dataset

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T19:04:58.673429Z

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 ec10852a-e7ae-4ef9-985f-37a7a8470a4e · outbound

This paper cites Teneva: A Fast and Efficient Tensor Decomposition Library.

GRAFT: Gradient-Aware Fast MaxVol Technique for Dynamic Data Sampling Teneva: A Fast and Efficient Tensor Decomposition Library

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T19:04:58.487510Z

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 889ba289-9e07-48f6-9cab-c9e568b62d7b · outbound

This paper cites The Use of Multiple Measurements in Taxonomic Problems.

GRAFT: Gradient-Aware Fast MaxVol Technique for Dynamic Data Sampling The Use of Multiple Measurements in Taxonomic Problems

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-05T19:04:56.123704Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ea966a0a-d53e-4082-a610-e75e6bed996d · outbound

This paper cites Aggregated residual transformations for deep neural networks.

GRAFT: Gradient-Aware Fast MaxVol Technique for Dynamic Data Sampling Aggregated residual transformations for deep neural networks

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T19:04:58.263912Z

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 456d9d76-c4fb-44fd-a9c3-6ee8d722da75 · outbound

This paper cites Krizhevsky, G.

GRAFT: Gradient-Aware Fast MaxVol Technique for Dynamic Data Sampling Krizhevsky, G

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T19:04:58.080801Z

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 4af23ff4-00bc-4232-8f55-24291c3ce7e3 · outbound

This paper cites Visualizing the loss landscape of neural nets.

GRAFT: Gradient-Aware Fast MaxVol Technique for Dynamic Data Sampling Visualizing the loss landscape of neural nets

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T19:04:57.920970Z

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 d3af463a-d208-49fa-b551-2ef5785537dc · outbound

This paper cites Mobilenetv2: Inverted residuals and linear bottlenecks.

GRAFT: Gradient-Aware Fast MaxVol Technique for Dynamic Data Sampling Mobilenetv2: Inverted residuals and linear bottlenecks

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T19:04:57.789367Z

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 dc7d475f-c892-47f5-a480-0e852ddca022 · outbound

This paper cites Chapter 5 - Text Mining Methodology.

GRAFT: Gradient-Aware Fast MaxVol Technique for Dynamic Data Sampling Chapter 5 - Text Mining Methodology

Reference 29

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c517e98d-2964-42fa-8b99-ec3b2f7a1278 · outbound

This paper cites A review of algorithms for SAW sensors e-nose based volatile compound identification.

GRAFT: Gradient-Aware Fast MaxVol Technique for Dynamic Data Sampling A review of algorithms for SAW sensors e-nose based volatile compound identification

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T19:04:57.655527Z

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 549a8fdb-a70c-4a97-b32f-f300bb45485f · outbound

This paper cites Analysis of complex mixtures using high-resolution nuclear magnetic resonance spectroscopy and chemometrics.

GRAFT: Gradient-Aware Fast MaxVol Technique for Dynamic Data Sampling Analysis of complex mixtures using high-resolution nuclear magnetic resonance spectroscopy and chemometrics

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T19:04:57.456076Z

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.

Pith citing papers

No inbound Pith citation observations are available.