Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-06T21:40:04.120760Z
Paper Citation Record · LEDGER
Partial Forward Blocking: A Novel Data Pruning Paradigm for Lossless Training Acceleration
As of 21 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.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
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
- Resolution
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Source-reported events for the cited work
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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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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
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
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
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
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
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
- Resolution
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Source-reported events for the cited work
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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
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
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
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
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
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
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
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
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
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
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
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
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
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
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
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T21:40:11.182894Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
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
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T21:40:10.971981Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
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
- Resolution
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Source-reported events for the cited work
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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
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
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
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
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
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
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
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
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
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
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
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
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
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
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
- Resolution
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Source-reported events for the cited work
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Observation b4c2f01e-b6db-4afa-a3c4-2d2ad28fc240 · outbound
This paper cites Mixed precision training.
Partial Forward Blocking: A Novel Data Pruning Paradigm for Lossless Training Acceleration Mixed precision training
Reference 27
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
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
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
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
- Resolution
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Source-reported events for the cited work
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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
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
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
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
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
- Resolution
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Source-reported events for the cited work
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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
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
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
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Source-reported events for the cited work
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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
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Source-reported events for the cited work
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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
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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
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Source-reported events for the cited work
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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
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
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
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Source-reported events for the cited work
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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
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
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 2026-08-21T06:32:19.484+00:00.
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 2026-08-21T06:32:19.484+00:00.
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 2026-08-21T06:32:19.484+00:00.
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 2026-08-21T06:32:19.484+00:00.
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 2026-08-21T06:32:19.484+00:00.
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 2026-08-21T06:32:19.484+00:00.
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 2026-08-21T06:32:19.484+00:00.
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 2026-08-21T06:32:19.484+00:00.
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 2026-08-21T06:32:19.484+00:00.
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 2026-08-21T06:32:19.484+00:00.
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 2026-08-21T06:32:19.484+00:00.
Pith citing papers
No inbound Pith citation observations are available.