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Paper Citation Record · LEDGER

Partial Forward Blocking: A Novel Data Pruning Paradigm for Lossless Training Acceleration

As of 18 August 2026, this Paper Citation Record lists 53 of 53 outbound references and 0 inbound Pith citation observations for arXiv:2506.23674.

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

pith.paper-citation-record.v1
2506.23674 v1

Coverage vector

measured 53 of 53 reference resolution

Typed states for the displayed outbound observations.

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measured 53 of 53 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 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

53 of 53 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 8cf422ce-bcce-4255-b3e4-db3600f408c4 · outbound

This paper cites YouTube-8M: A Large-Scale Video Classification Benchmark.

Partial Forward Blocking: A Novel Data Pruning Paradigm for Lossless Training Acceleration YouTube-8M: A Large-Scale Video Classification Benchmark

Reference 1

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Observation 094056e3-3103-4306-a0f7-64e3d7d99602 · outbound

This paper cites Fast kernel classifiers with online and active learn- ing.

Partial Forward Blocking: A Novel Data Pruning Paradigm for Lossless Training Acceleration Fast kernel classifiers with online and active learn- ing

Reference 2

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Observation c01fb922-6e46-4d24-a766-9b7f3e727abe · outbound

This paper cites Dataset distillation by matching training trajectories.

Partial Forward Blocking: A Novel Data Pruning Paradigm for Lossless Training Acceleration Dataset distillation by matching training trajectories

Reference 3

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Observation af43e0e8-6f60-44fc-a51b-1dad728579da · outbound

This paper cites Conceptual 12m: Pushing web-scale image-text pre- training to recognize long-tail visual concepts.

Partial Forward Blocking: A Novel Data Pruning Paradigm for Lossless Training Acceleration Conceptual 12m: Pushing web-scale image-text pre- training to recognize long-tail visual concepts

Reference 4

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Observation d95271d3-6bc8-4ede-8475-443b889ea863 · outbound

This paper cites Rethinking Atrous Convolution for Semantic Image Segmentation.

Partial Forward Blocking: A Novel Data Pruning Paradigm for Lossless Training Acceleration Rethinking Atrous Convolution for Semantic Image Segmentation

Reference 5

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Observation 683171d6-c447-43d6-b656-1bda6151a7d3 · outbound

This paper cites Encoder-decoder with atrous separable convolution for semantic image segmentation.

Partial Forward Blocking: A Novel Data Pruning Paradigm for Lossless Training Acceleration Encoder-decoder with atrous separable convolution for semantic image segmentation

Reference 6

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Observation c86e9bf0-e45a-41e3-b502-6530f53db682 · outbound

This paper cites Super-Samples from Kernel Herding.

Partial Forward Blocking: A Novel Data Pruning Paradigm for Lossless Training Acceleration Super-Samples from Kernel Herding

Reference 7

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Observation a839e4a1-b5d2-4d10-9a49-fee4984f7ff1 · outbound

This paper cites Selection via proxy: Efficient data se- lection for deep learning.

Partial Forward Blocking: A Novel Data Pruning Paradigm for Lossless Training Acceleration Selection via proxy: Efficient data se- lection for deep learning

Reference 8

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Observation 6edf52e0-d60b-4ec5-9264-b854c061d11a · outbound

This paper cites MMSegmentation: Openmmlab semantic segmentation toolbox and benchmark.

Partial Forward Blocking: A Novel Data Pruning Paradigm for Lossless Training Acceleration MMSegmentation: Openmmlab semantic segmentation toolbox and benchmark

Reference 9

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Observation b24862b6-af38-4dc9-a710-010dd061a49a · outbound

This paper cites The cityscapes dataset for semantic urban scene understanding.

Partial Forward Blocking: A Novel Data Pruning Paradigm for Lossless Training Acceleration The cityscapes dataset for semantic urban scene understanding

Reference 10

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Observation dae414a5-f974-4270-9790-05cf9f795650 · outbound

This paper cites AutoAugment: Learning Augmentation Policies from Data.

Partial Forward Blocking: A Novel Data Pruning Paradigm for Lossless Training Acceleration AutoAugment: Learning Augmentation Policies from Data

Reference 11

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Observation cee972d3-b544-4750-b9ea-2dd8a492c6fd · outbound

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

Partial Forward Blocking: A Novel Data Pruning Paradigm for Lossless Training Acceleration Imagenet: A large-scale hierarchical image database

Reference 12

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Observation f87b5f2f-548d-4456-9de2-086265a885db · outbound

This paper cites Towards accelerated model training via bayesian data selection.Advances in Neu- ral Information Processing Systems, 36, 2024.

Partial Forward Blocking: A Novel Data Pruning Paradigm for Lossless Training Acceleration Towards accelerated model training via bayesian data selection.Advances in Neu- ral Information Processing Systems, 36, 2024

Reference 13

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Observation 04af4a57-042e-41f8-b349-3b17a153718c · outbound

This paper cites Deep residual learning for image recognition.

Partial Forward Blocking: A Novel Data Pruning Paradigm for Lossless Training Acceleration Deep residual learning for image recognition

Reference 14

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Observation 560fad38-f78e-4984-bd4f-c013ab1483b8 · outbound

This paper cites Large- scale dataset pruning with dynamic uncertainty.

Partial Forward Blocking: A Novel Data Pruning Paradigm for Lossless Training Acceleration Large- scale dataset pruning with dynamic uncertainty

Reference 15

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Observation d234b5ff-0f96-437c-857d-ec3fb7e619da · outbound

This paper cites You only condense once: Two rules for pruning condensed datasets.

Partial Forward Blocking: A Novel Data Pruning Paradigm for Lossless Training Acceleration You only condense once: Two rules for pruning condensed datasets

Reference 16

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Observation 75db8e64-2264-41f0-a6e3-c40b3db355f6 · outbound

This paper cites Diversified batch selection for training acceleration.

Partial Forward Blocking: A Novel Data Pruning Paradigm for Lossless Training Acceleration Diversified batch selection for training acceleration

Reference 17

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

This paper cites Accelerating Deep Learning by Focusing on the Biggest Losers.

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

Reference 18

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Observation a560dd09-e208-427d-ad04-96e88955ee4a · outbound

This paper cites Not all sam- ples are created equal: Deep learning with importance sam- pling.

Partial Forward Blocking: A Novel Data Pruning Paradigm for Lossless Training Acceleration Not all sam- ples are created equal: Deep learning with importance sam- pling

Reference 19

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Observation 4fdc3bbc-285b-435d-b692-f4c1c51f7dad · outbound

This paper cites Ordered sgd: A new stochastic optimization framework for empirical risk mini- mization.

Partial Forward Blocking: A Novel Data Pruning Paradigm for Lossless Training Acceleration Ordered sgd: A new stochastic optimization framework for empirical risk mini- mization

Reference 20

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Observation 37720398-098e-4e31-89ab-437409fd71c0 · outbound

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

Partial Forward Blocking: A Novel Data Pruning Paradigm for Lossless Training Acceleration Glister: Generalization based data subset selection for efficient and robust learning

Reference 21

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Observation 2649f5dd-1282-4693-ae78-961ec62d5b6f · outbound

This paper cites Segment any- thing.

Partial Forward Blocking: A Novel Data Pruning Paradigm for Lossless Training Acceleration Segment any- thing

Reference 22

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Observation c11eb1a0-7687-4bc4-bc21-d7ee4c99b892 · outbound

This paper cites Understanding black-box predictions via influence functions.

Partial Forward Blocking: A Novel Data Pruning Paradigm for Lossless Training Acceleration Understanding black-box predictions via influence functions

Reference 23

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Observation ea36f325-afc4-4da2-8425-11104e6d070b · outbound

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

Partial Forward Blocking: A Novel Data Pruning Paradigm for Lossless Training Acceleration Learning multiple layers of features from tiny images

Reference 24

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Observation ff123826-0a05-4b76-85e8-688288b8eec0 · outbound

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

Partial Forward Blocking: A Novel Data Pruning Paradigm for Lossless Training Acceleration Swin transformer: Hierarchical vision transformer using shifted windows

Reference 25

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Observation c9708a99-1843-4dc7-93a7-334681577694 · outbound

This paper cites Online Batch Selection for Faster Training of Neural Networks.

Partial Forward Blocking: A Novel Data Pruning Paradigm for Lossless Training Acceleration Online Batch Selection for Faster Training of Neural Networks

Reference 26

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Observation b4c2f01e-b6db-4afa-a3c4-2d2ad28fc240 · outbound

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Partial Forward Blocking: A Novel Data Pruning Paradigm for Lossless Training Acceleration Mixed precision training

Reference 27

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Observation 357f61a2-b5b2-46b4-b176-4e66f45b855a · outbound

This paper cites Prioritized training on points that are learnable, worth learning, and not yet learnt.

Partial Forward Blocking: A Novel Data Pruning Paradigm for Lossless Training Acceleration Prioritized training on points that are learnable, worth learning, and not yet learnt

Reference 28

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Observation 9e051588-59e7-43f9-b6c4-23ae1f2463bd · outbound

This paper cites Coresets for data-efficient training of machine learning mod- els.

Partial Forward Blocking: A Novel Data Pruning Paradigm for Lossless Training Acceleration Coresets for data-efficient training of machine learning mod- els

Reference 29

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Observation 3f536a92-4075-4004-8b62-342791770de6 · outbound

This paper cites Coresets for robust training of deep neural networks against noisy labels.

Partial Forward Blocking: A Novel Data Pruning Paradigm for Lossless Training Acceleration Coresets for robust training of deep neural networks against noisy labels

Reference 30

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Observation 7888dcd4-5eb0-4f56-a408-6ca931baa821 · outbound

This paper cites Reading digits in natural images with unsupervised feature learning.

Partial Forward Blocking: A Novel Data Pruning Paradigm for Lossless Training Acceleration Reading digits in natural images with unsupervised feature learning

Reference 31

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Source-reported events for the cited work

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Observation 0fcd6c3b-38a7-4f83-a735-f1eba236ab6f · outbound

This paper cites Dataset meta-learning from kernel ridge-regression.

Partial Forward Blocking: A Novel Data Pruning Paradigm for Lossless Training Acceleration Dataset meta-learning from kernel ridge-regression

Reference 32

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Source-reported events for the cited work

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Observation 4607991c-6e58-4594-ab1c-97e02bf6ed91 · outbound

This paper cites Deep learning on a data diet: Finding important ex- amples early in training.

Partial Forward Blocking: A Novel Data Pruning Paradigm for Lossless Training Acceleration Deep learning on a data diet: Finding important ex- amples early in training

Reference 33

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Source-reported events for the cited work

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Observation d8d3ff29-ab61-4953-b0e6-e04bfde54195 · outbound

This paper cites Identifying mislabeled data using the area under the margin ranking.

Partial Forward Blocking: A Novel Data Pruning Paradigm for Lossless Training Acceleration Identifying mislabeled data using the area under the margin ranking

Reference 34

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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.

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Observation 6426b366-8ace-4984-aad5-501ead0ef2cc · outbound

This paper cites Infobatch: Lossless training speed up by unbiased dynamic data pruning.

Partial Forward Blocking: A Novel Data Pruning Paradigm for Lossless Training Acceleration Infobatch: Lossless training speed up by unbiased dynamic data pruning

Reference 35

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verified fuzzy
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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.

source=pdf_text observed=2026-08-06T21:40:01.922175Z digest=sha256:34325aed9fa7fbbb594a5172d9c4456978a4249c0cb3010cd6ce6ec391bb9b72

Observation cdee6c5c-f9a3-40ce-acc3-91778ab1572b · outbound

This paper cites Learning transferable visual models from natural language supervi- sion.

Partial Forward Blocking: A Novel Data Pruning Paradigm for Lossless Training Acceleration Learning transferable visual models from natural language supervi- sion

Reference 36

Resolution
unresolved
no resolver link, observed 2026-08-06T21:40:01.993319Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T21:40:01.993319Z digest=sha256:f7387611fdf77c5cd45c6f0138793d58c3a6961a51fa208ce6a44fec44913e88

Observation 75658fb2-e379-4cce-bb3a-6642495c629b · outbound

This paper cites Accelerating Deep Learning with Dynamic Data Pruning.

Partial Forward Blocking: A Novel Data Pruning Paradigm for Lossless Training Acceleration Accelerating Deep Learning with Dynamic Data Pruning

Reference 37

Resolution
unresolved
no resolver link, observed 2026-08-06T21:40:02.068060Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T21:40:02.068060Z digest=sha256:fa8d1fa75205955edfbca06cad9e1f9e954d4439eee27002a1c40707ad312d91

Observation 61aa79dc-d894-4139-9958-efbe3d63031d · outbound

This paper cites High-resolution image synthesis with latent diffusion models.

Partial Forward Blocking: A Novel Data Pruning Paradigm for Lossless Training Acceleration High-resolution image synthesis with latent diffusion models

Reference 38

Resolution
unresolved
no resolver link, observed 2026-08-06T21:40:02.147011Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T21:40:02.147011Z digest=sha256:7aba3fa577c5f655ea490e994566991951a30c5fbc3be33dd5d3c96457f95160

Observation b8c4108e-bb08-49db-a5a6-000ade2b72eb · outbound

This paper cites Imagenet large scale visual recognition challenge.

Partial Forward Blocking: A Novel Data Pruning Paradigm for Lossless Training Acceleration Imagenet large scale visual recognition challenge

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T21:40:07.650723Z

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.

source=pdf_text observed=2026-08-06T21:40:02.248433Z digest=sha256:ea3eb8ba399e0ce4061fd9f14cda9cd378f2a9c8ba4a4078d2a725b26303f76a

Observation 59047e10-6054-42e9-a668-f6edf18f025d · outbound

This paper cites Multivariate density estimation: theory, practice, and visualization.

Partial Forward Blocking: A Novel Data Pruning Paradigm for Lossless Training Acceleration Multivariate density estimation: theory, practice, and visualization

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T21:40:07.524044Z

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.

source=pdf_text observed=2026-08-06T21:40:02.388751Z digest=sha256:03d7b3cf34294066b08649ca34a74c0c816f107341de31428da298f2bf7b10c2

Observation d29f15c5-2890-4f4a-afe5-c5cfd35a874b · outbound

This paper cites Active Learning for Convolutional Neural Networks: A Core-Set Approach.

Partial Forward Blocking: A Novel Data Pruning Paradigm for Lossless Training Acceleration Active Learning for Convolutional Neural Networks: A Core-Set Approach

Reference 41

Resolution
unresolved
no resolver link, observed 2026-08-06T21:40:02.527341Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T21:40:02.527341Z digest=sha256:8a4113f9526d66e95742b25c570ff2ba84f8677b05cf3bc46d5612d063e730a5

Observation 47274f36-b091-401f-b101-669b1aeb4738 · outbound

This paper cites Active learning for convo- lutional neural networks: A core-set approach.

Partial Forward Blocking: A Novel Data Pruning Paradigm for Lossless Training Acceleration Active learning for convo- lutional neural networks: A core-set approach

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T21:40:07.364338Z

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.

source=pdf_text observed=2026-08-06T21:40:02.660835Z digest=sha256:519018f0309a74c72d2d133a8a0f672426f9cec6552ca1ba59c355dcbf4c6865

Observation e7500815-8836-4e34-b16e-57f9d3349f83 · outbound

This paper cites Density estimation for statistics and data analysis.

Partial Forward Blocking: A Novel Data Pruning Paradigm for Lossless Training Acceleration Density estimation for statistics and data analysis

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T21:40:07.127091Z

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.

source=pdf_text observed=2026-08-06T21:40:02.829839Z digest=sha256:f31c10aca9b98a0522e2537d8875ae8933790672e5dca11c79b69307c3c08f42

Observation 41bdd857-6978-451d-8a64-32f12a79b1ff · outbound

This paper cites Beyond neural scaling laws: beat- ing power law scaling via data pruning.

Partial Forward Blocking: A Novel Data Pruning Paradigm for Lossless Training Acceleration Beyond neural scaling laws: beat- ing power law scaling via data pruning

Reference 44

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T21:40:06.769799Z

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.

source=pdf_text observed=2026-08-06T21:40:02.994619Z digest=sha256:971d126bfa930e06e855ea3b352ae4074bd9371af9811afe1b7cfc64fabb88d9

Observation 072e549a-a26c-4da8-ae39-b2d43d00f3c7 · outbound

This paper cites Data pruning via moving-one- sample-out.

Partial Forward Blocking: A Novel Data Pruning Paradigm for Lossless Training Acceleration Data pruning via moving-one- sample-out

Reference 45

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T21:40:06.473216Z

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.

source=pdf_text observed=2026-08-06T21:40:03.126577Z digest=sha256:94c99c12ced055550331f18f20d3462ec92c7c1aa211e4dbf7575096805e9f3e

Observation a775506f-3ee1-4ce8-938f-035018a1119e · outbound

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

Partial Forward Blocking: A Novel Data Pruning Paradigm for Lossless Training Acceleration An empirical study of example forgetting during deep neural network learning

Reference 46

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T21:40:06.131123Z

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.

source=pdf_text observed=2026-08-06T21:40:03.310582Z digest=sha256:0aa1a6ec70a2a654e0029882192e1a8f10ab39b11ea6e914d13394b724f526e4

Observation f2b689f9-730f-4b03-8277-640aff2e8488 · outbound

This paper cites Cafe: Learning to condense dataset by align- ing features.

Partial Forward Blocking: A Novel Data Pruning Paradigm for Lossless Training Acceleration Cafe: Learning to condense dataset by align- ing features

Reference 47

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T21:40:05.871980Z

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.

source=pdf_text observed=2026-08-06T21:40:03.462557Z digest=sha256:e30ebc0b0545ad870b7724ec408311acba2523ff3efe756596badf44a1af0788

Observation 95e9ede3-ee85-42ba-b1bb-63edbd530948 · outbound

This paper cites Moderate coreset: A universal method of data selection for real-world data-efficient deep learning.

Partial Forward Blocking: A Novel Data Pruning Paradigm for Lossless Training Acceleration Moderate coreset: A universal method of data selection for real-world data-efficient deep learning

Reference 48

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T21:40:05.557046Z

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.

source=pdf_text observed=2026-08-06T21:40:03.627436Z digest=sha256:e4ca68d6bd8c812eac5f211acecb47a999d13d6e24771f7a2fcb66f0256ca68d

Observation e08c81f1-eea2-49e0-a124-cfc7cc05982a · outbound

This paper cites Dataset pruning: Reducing training data by ex- amining generalization influence.

Partial Forward Blocking: A Novel Data Pruning Paradigm for Lossless Training Acceleration Dataset pruning: Reducing training data by ex- amining generalization influence

Reference 49

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T21:40:05.222685Z

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.

source=pdf_text observed=2026-08-06T21:40:03.794635Z digest=sha256:f18e26f336bd231865f49eed2c141defd5b99e86d450b7b4ed37a26f5cc74fab

Observation 748e7fdc-4e8a-45c8-9f78-7f2d602ec243 · outbound

This paper cites Online coreset selection for rehearsal-based contin- ual learning.

Partial Forward Blocking: A Novel Data Pruning Paradigm for Lossless Training Acceleration Online coreset selection for rehearsal-based contin- ual learning

Reference 50

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T21:40:04.927730Z

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.

source=pdf_text observed=2026-08-06T21:40:03.911691Z digest=sha256:bb5f2921898cd3d5d5c0cdea20640ae88c369325adbe3c6eca7daf901c1d0b85

Observation 8e3847bf-af24-41f4-9099-0de2a530d185 · outbound

This paper cites Dataset condensation with dis- tribution matching.

Partial Forward Blocking: A Novel Data Pruning Paradigm for Lossless Training Acceleration Dataset condensation with dis- tribution matching

Reference 51

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T21:40:04.802634Z

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.

source=pdf_text observed=2026-08-06T21:40:03.987025Z digest=sha256:7a1326bd76e5eed0203f0312e2cc9a4c0b47e76c797049564d7c38c18ef158d1

Observation 48b8a6e4-aaa0-4956-834b-d5e1587ff88d · outbound

This paper cites Dataset condensation with gradient matching.

Partial Forward Blocking: A Novel Data Pruning Paradigm for Lossless Training Acceleration Dataset condensation with gradient matching

Reference 52

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T21:40:04.626252Z

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.

source=pdf_text observed=2026-08-06T21:40:04.065358Z digest=sha256:88ad363d1288e39dc269b7c8b751a3a5207bad2c71b3d54f9cdf863bcaa9100a

Observation c71ea279-c78d-4bd4-81f6-867556852880 · outbound

This paper cites Coverage-centric coreset selection for high pruning rates.

Partial Forward Blocking: A Novel Data Pruning Paradigm for Lossless Training Acceleration Coverage-centric coreset selection for high pruning rates

Reference 53

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T21:40:04.464228Z

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.

source=pdf_text observed=2026-08-06T21:40:04.120760Z digest=sha256:0f1a457e47da4bdd9f726c135174a915f2528e54033d5e4250e5d7cce5e30465

Pith citing papers

No inbound Pith citation observations are available.