Paper Citation Record · LEDGER

FADRM: Fast and Accurate Data Residual Matching for Dataset Distillation

As of 8 August 2026, this Paper Citation Record lists 51 of 51 outbound references and 1 inbound Pith citation observation for arXiv:2506.24125.

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

pith.paper-citation-record.v1
2506.24125 v1

Coverage vector

measured 51 of 51 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T21:32:00.072009Z

measured 52 of 52 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 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-03T15:50:35.304235Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

51 of 51 outbound references displayed

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verified fuzzy23
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parse uncertain0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 6584032a-5006-4f32-9aae-ad1f2ffc0a67 · outbound

This paper cites GPT-4 Technical Report.

FADRM: Fast and Accurate Data Residual Matching for Dataset Distillation GPT-4 Technical Report

Reference 1

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Observation 9102774d-a313-4200-865f-b83213577eb1 · outbound

This paper cites Rademacher and gaussian complexities: Risk bounds and structural results.

FADRM: Fast and Accurate Data Residual Matching for Dataset Distillation Rademacher and gaussian complexities: Risk bounds and structural results

Reference 2

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Observation 162c5b1e-c443-4537-83a7-b9dba34083c2 · outbound

This paper cites An intuitive proof of the data processing inequality.

FADRM: Fast and Accurate Data Residual Matching for Dataset Distillation An intuitive proof of the data processing inequality

Reference 3

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Observation cb52d259-3614-4557-ba65-b3b1048f8057 · outbound

This paper cites Dataset distillation by matching training trajectories.

FADRM: Fast and Accurate Data Residual Matching for Dataset Distillation Dataset distillation by matching training trajectories

Reference 4

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Observation 62ed99b9-8263-4b0c-81ee-35987a5a3469 · outbound

This paper cites Dataset Distillation via Adversarial Prediction Matching.

FADRM: Fast and Accurate Data Residual Matching for Dataset Distillation Dataset Distillation via Adversarial Prediction Matching

Reference 5

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Observation 902cbd81-c7c2-4c75-893b-4e50921a800c · outbound

This paper cites Dataset distillation via committee voting.

FADRM: Fast and Accurate Data Residual Matching for Dataset Distillation Dataset distillation via committee voting

Reference 6

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Source-reported events for the cited work

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Observation 0a828a6d-a2d7-4d75-91a0-1068c1c59c03 · outbound

This paper cites Scaling up dataset distillation to imagenet- 1k with constant memory.

FADRM: Fast and Accurate Data Residual Matching for Dataset Distillation Scaling up dataset distillation to imagenet- 1k with constant memory

Reference 7

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Observation c4504ae4-c5d1-4626-ae1d-382d64e1a0a5 · outbound

This paper cites Imagenet: A large- scale hierarchical image database.

FADRM: Fast and Accurate Data Residual Matching for Dataset Distillation Imagenet: A large- scale hierarchical image database

Reference 8

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Observation b232e7cc-4b81-485b-b4bd-3f7783467630 · outbound

This paper cites Remember the Past: Distilling Datasets into Addressable Memories for Neural Networks.

FADRM: Fast and Accurate Data Residual Matching for Dataset Distillation Remember the Past: Distilling Datasets into Addressable Memories for Neural Networks

Reference 9

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Observation f3e2a548-bac3-4493-9024-7dd0ef6a49e4 · outbound

This paper cites To- wards lossless dataset distillation via difficulty-aligned trajectory matching.

FADRM: Fast and Accurate Data Residual Matching for Dataset Distillation To- wards lossless dataset distillation via difficulty-aligned trajectory matching

Reference 10

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Observation 6e8d37d4-39f1-4d38-bf64-986a051553d1 · outbound

This paper cites Deep residual learning for image recognition.

FADRM: Fast and Accurate Data Residual Matching for Dataset Distillation Deep residual learning for image recognition

Reference 11

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Source-reported events for the cited work

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Observation d3ac94c9-b4bb-445d-8177-b43848029fe8 · outbound

This paper cites Multisize dataset condensation.

FADRM: Fast and Accurate Data Residual Matching for Dataset Distillation Multisize dataset condensation

Reference 12

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Observation 0b0ef0ee-759a-42cd-8730-38782df79228 · outbound

This paper cites Densely connected convolutional networks.

FADRM: Fast and Accurate Data Residual Matching for Dataset Distillation Densely connected convolutional networks

Reference 13

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Observation af914229-3750-4f7e-bb44-112c4d2e1630 · outbound

This paper cites Dataset condensation via efficient synthetic-data param- eterization.

FADRM: Fast and Accurate Data Residual Matching for Dataset Distillation Dataset condensation via efficient synthetic-data param- eterization

Reference 14

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Observation 2fabd374-1c30-4426-8318-cb93cc0c50d2 · outbound

This paper cites Learning multiple layers of features from tiny images.

FADRM: Fast and Accurate Data Residual Matching for Dataset Distillation Learning multiple layers of features from tiny images

Reference 15

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Observation b2ff6812-64c4-45a3-9cbd-1a78cc4ed176 · outbound

This paper cites Dataset Condensation with Latent Space Knowledge Factorization and Sharing.

FADRM: Fast and Accurate Data Residual Matching for Dataset Distillation Dataset Condensation with Latent Space Knowledge Factorization and Sharing

Reference 16

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Observation 27a7f0a3-1e26-4e4d-bfa8-aceb54cf093c · outbound

This paper cites Dataset condensation with contrastive signals.

FADRM: Fast and Accurate Data Residual Matching for Dataset Distillation Dataset condensation with contrastive signals

Reference 17

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Source-reported events for the cited work

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Observation 990a5991-ab43-4efc-b6e9-1fd29ee9f846 · outbound

This paper cites DeepSeek-V3 Technical Report.

FADRM: Fast and Accurate Data Residual Matching for Dataset Distillation DeepSeek-V3 Technical Report

Reference 18

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Observation 0928311c-70aa-44cf-b0c4-317bf647285b · outbound

This paper cites The Evolution of Dataset Distillation: Toward Scalable and Generalizable Solutions.

FADRM: Fast and Accurate Data Residual Matching for Dataset Distillation The Evolution of Dataset Distillation: Toward Scalable and Generalizable Solutions

Reference 19

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Observation d5c39e0d-e890-41a5-b1ee-b23e2ad4ec8a · outbound

This paper cites Dataset distillation via factorization.

FADRM: Fast and Accurate Data Residual Matching for Dataset Distillation Dataset distillation via factorization

Reference 20

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Observation 3321f48b-6c0f-435b-8be4-98b2f4eba89f · outbound

This paper cites Swin transformer: Hierarchical vision transformer using shifted windows.

FADRM: Fast and Accurate Data Residual Matching for Dataset Distillation Swin transformer: Hierarchical vision transformer using shifted windows

Reference 21

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Observation d2d097ca-de7c-4c51-a5b0-7ff12e002af3 · outbound

This paper cites Efficient Dataset Distillation Using Random Feature Approximation.

FADRM: Fast and Accurate Data Residual Matching for Dataset Distillation Efficient Dataset Distillation Using Random Feature Approximation

Reference 22

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Observation a398d585-fcbd-4cf2-90d2-9c9c13836bbc · outbound

This paper cites Mixed Precision Training.

FADRM: Fast and Accurate Data Residual Matching for Dataset Distillation Mixed Precision Training

Reference 23

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Observation e09e12b0-644e-4690-9c2a-84813c529d87 · outbound

This paper cites Dataset distillation with infinitely wide convolutional networks.

FADRM: Fast and Accurate Data Residual Matching for Dataset Distillation Dataset distillation with infinitely wide convolutional networks

Reference 24

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Observation 86db2fae-edd9-43a9-9d59-7ee3e74dcd41 · outbound

This paper cites Improving language understanding by generative pre-training.

FADRM: Fast and Accurate Data Residual Matching for Dataset Distillation Improving language understanding by generative pre-training

Reference 25

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Observation a9229910-dd2d-453c-9936-eb50789a0271 · outbound

This paper cites Liu, Yuri A.

FADRM: Fast and Accurate Data Residual Matching for Dataset Distillation Liu, Yuri A

Reference 26

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Observation 7be36bca-f495-43a3-9f99-5b95218de64b · outbound

This paper cites Mobilenetv2: Inverted residuals and linear bottlenecks.

FADRM: Fast and Accurate Data Residual Matching for Dataset Distillation Mobilenetv2: Inverted residuals and linear bottlenecks

Reference 27

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Observation ce49c717-36d5-40fa-ad35-d5ad71bd11c7 · outbound

This paper cites Dataset distillation in the era of large-scale data: Methods, analysis, and future directions.

FADRM: Fast and Accurate Data Residual Matching for Dataset Distillation Dataset distillation in the era of large-scale data: Methods, analysis, and future directions

Reference 28

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Observation 0372b1ad-09c5-483a-874c-4fa31f8fcf22 · outbound

This paper cites Generalized large-scale data condensation via various backbone and statistical matching.

FADRM: Fast and Accurate Data Residual Matching for Dataset Distillation Generalized large-scale data condensation via various backbone and statistical matching

Reference 29

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Observation ddd13eab-7c54-431c-9404-28782260ddbb · outbound

This paper cites Elucidating the Design Space of Dataset Condensation.

FADRM: Fast and Accurate Data Residual Matching for Dataset Distillation Elucidating the Design Space of Dataset Condensation

Reference 30

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Observation bd73bd32-50b6-45e7-ab74-a7d4a82c56b4 · outbound

This paper cites Frequency domain-based dataset distilla- tion.

FADRM: Fast and Accurate Data Residual Matching for Dataset Distillation Frequency domain-based dataset distilla- tion

Reference 31

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Observation a9ab5eda-5c9b-4136-adf7-b8552c96080f · outbound

This paper cites On the diversity and realism of distilled dataset: An efficient dataset distillation paradigm.

FADRM: Fast and Accurate Data Residual Matching for Dataset Distillation On the diversity and realism of distilled dataset: An efficient dataset distillation paradigm

Reference 32

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Observation d01ab754-793c-4162-ab7a-f0c25321109c · outbound

This paper cites Inception-v4, inception-resnet and the impact of residual connections on learning.

FADRM: Fast and Accurate Data Residual Matching for Dataset Distillation Inception-v4, inception-resnet and the impact of residual connections on learning

Reference 33

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Observation 0ea06d0f-3c82-4abe-9c7f-23c1b532e1a9 · outbound

This paper cites Efficientnetv2: Smaller models and faster training.

FADRM: Fast and Accurate Data Residual Matching for Dataset Distillation Efficientnetv2: Smaller models and faster training

Reference 34

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Observation 86f332b6-9f69-4235-bc9b-330224127a8e · outbound

This paper cites Gemini: A Family of Highly Capable Multimodal Models.

FADRM: Fast and Accurate Data Residual Matching for Dataset Distillation Gemini: A Family of Highly Capable Multimodal Models

Reference 35

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This paper cites Attention is all you need.

FADRM: Fast and Accurate Data Residual Matching for Dataset Distillation Attention is all you need

Reference 36

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Observation 1547b4b6-eaa1-4643-972d-a396eb5d01b2 · outbound

This paper cites Cafe: Learning to condense dataset by aligning features.

FADRM: Fast and Accurate Data Residual Matching for Dataset Distillation Cafe: Learning to condense dataset by aligning features

Reference 37

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Observation 325bf653-1b86-4e6e-a549-e7c8e1de3257 · outbound

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FADRM: Fast and Accurate Data Residual Matching for Dataset Distillation Dataset Distillation

Reference 38

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Observation a5c0af80-824d-4f89-8169-c1f96e232e7e · outbound

This paper cites Are Large-scale Soft Labels Necessary for Large-scale Dataset Distillation?.

FADRM: Fast and Accurate Data Residual Matching for Dataset Distillation Are Large-scale Soft Labels Necessary for Large-scale Dataset Distillation?

Reference 39

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Observation 4bad430f-0dab-41fc-98a1-2fdd7989ed32 · outbound

This paper cites Towards Adversarially Robust Dataset Distillation by Curvature Regularization.

FADRM: Fast and Accurate Data Residual Matching for Dataset Distillation Towards Adversarially Robust Dataset Distillation by Curvature Regularization

Reference 40

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Observation 676af3ce-5059-4990-8a04-15956b6f4257 · outbound

This paper cites Image classification using deep convolutional neural net- works.

FADRM: Fast and Accurate Data Residual Matching for Dataset Distillation Image classification using deep convolutional neural net- works

Reference 41

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Observation 7429bc8c-6f34-4f84-9b74-5fea84a4e57e · outbound

This paper cites Dataset distillation via curriculum data synthesis in large data era.

FADRM: Fast and Accurate Data Residual Matching for Dataset Distillation Dataset distillation via curriculum data synthesis in large data era

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:32:02.654184Z

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Observation 7cb31c9b-7b40-4863-a8b3-4b26d2af9aef · outbound

This paper cites Squeeze, recover and relabel: Dataset condensation at imagenet scale from a new perspective.

FADRM: Fast and Accurate Data Residual Matching for Dataset Distillation Squeeze, recover and relabel: Dataset condensation at imagenet scale from a new perspective

Reference 43

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Source-reported events for the cited work

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Observation d72fe51e-5253-4e11-ac61-7e814511f904 · outbound

This paper cites Shufflenet: An extremely efficient convolutional neural network for mobile devices.

FADRM: Fast and Accurate Data Residual Matching for Dataset Distillation Shufflenet: An extremely efficient convolutional neural network for mobile devices

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:32:02.324364Z

Source-reported events for the cited work

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Observation cfc1ca97-1ac5-4476-a7ff-806719587c9a · outbound

This paper cites Dataset condensation with differentiable siamese augmentation.

FADRM: Fast and Accurate Data Residual Matching for Dataset Distillation Dataset condensation with differentiable siamese augmentation

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:32:01.965002Z

Source-reported events for the cited work

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Observation 79292212-561c-4dfd-8eb4-0dcf858c6de5 · outbound

This paper cites Dataset condensation with distribution matching.

FADRM: Fast and Accurate Data Residual Matching for Dataset Distillation Dataset condensation with distribution matching

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:32:01.650624Z

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 7d26a42d-a2e7-4345-a980-d5639aa39ee5 · outbound

This paper cites Dataset condensation with gradient matching.

FADRM: Fast and Accurate Data Residual Matching for Dataset Distillation Dataset condensation with gradient matching

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:32:01.342023Z

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 10346d2c-c2e7-416d-a894-1c6bcbe0ab7a · outbound

This paper cites Improve Cross-Architecture Generalization on Dataset Distillation.

FADRM: Fast and Accurate Data Residual Matching for Dataset Distillation Improve Cross-Architecture Generalization on Dataset Distillation

Reference 48

Resolution: verified exact; local_arxiv, observed 2026-08-06T21:32:00.219850Z

Source-reported events for the cited work

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Observation cac3a918-7090-45c6-b7d5-95fc704adaf6 · outbound

This paper cites Dataset distillation using neural feature regression.

FADRM: Fast and Accurate Data Residual Matching for Dataset Distillation Dataset distillation using neural feature regression

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:32:01.168375Z

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 e6381798-649b-47ae-9c19-4ca639284b09 · outbound

This paper cites an unresolved cited work.

FADRM: Fast and Accurate Data Residual Matching for Dataset Distillation Unresolved cited work

Reference 50

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Source-reported events for the cited work

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Observation a1ac54e4-0d79-4fa5-a997-1e29bdef76f7 · outbound

This paper cites theℓ∞ norm with L = 1 T.

FADRM: Fast and Accurate Data Residual Matching for Dataset Distillation theℓ∞ norm with L = 1 T

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:32:00.871193Z

Source-reported events for the cited work

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Pith citing papers

Observation 31d137a6-8ba7-4d9f-8bac-3e0e44a0275e · inbound

Hard Labels In! Rethinking the Role of Hard Labels in Mitigating Local Semantic Drift cites this paper.

Hard Labels In! Rethinking the Role of Hard Labels in Mitigating Local Semantic Drift FADRM: Fast and Accurate Data Residual Matching for Dataset Distillation

Reference 2

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Source-reported events for the cited work

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