Paper Citation Record · LEDGER

Dataset Distillation with Probabilistic Latent Features

As of 24 August 2026, this Paper Citation Record lists 42 of 42 outbound references and 1 inbound Pith citation observation for arXiv:2505.06647.

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

pith.paper-citation-record.v1
2505.06647 v2

Coverage vector

measured 42 of 42 reference resolution

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measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T18:29:50.299639Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-08T18:29:53.190306Z

Reference resolution

42 of 42 outbound references displayed

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External citation measurements

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Outbound references

Observation c70cd850-a0dd-4be0-8603-1b5e85243561 · outbound

This paper cites Im- age2stylegan: How to embed images into the stylegan latent space? In Proceedings of the IEEE/CVF international con- ference on computer vision (ICCV), pages 4432–4441, 2019.

Dataset Distillation with Probabilistic Latent Features Im- age2stylegan: How to embed images into the stylegan latent space? In Proceedings of the IEEE/CVF international con- ference on computer vision (ICCV), pages 4432–4441, 2019

Reference 1

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Observation 2b56d23b-f631-4d1d-838d-5465437de6f3 · outbound

This paper cites All are worth words: A vit back- bone for diffusion models.

Dataset Distillation with Probabilistic Latent Features All are worth words: A vit back- bone for diffusion models

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:41:55.560606Z

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Observation 1573882d-af62-4f52-a189-20c66fd1d4a9 · outbound

This paper cites Dataset distillation by matching training trajectories.

Dataset Distillation with Probabilistic Latent Features Dataset distillation by matching training trajectories

Reference 3

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Observation b825c808-c184-40b4-8574-62b4dd2ccf88 · outbound

This paper cites Generalizing dataset distillation via deep generative prior.

Dataset Distillation with Probabilistic Latent Features Generalizing dataset distillation via deep generative prior

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:41:55.530957Z

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Observation 5ef47b34-87bd-4d1d-b4b3-e0b92f44e922 · outbound

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

Dataset Distillation with Probabilistic Latent Features Imagenet: A large-scale hierarchical image database

Reference 5

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Observation 81f19a61-1430-4500-984e-fcb9ec5bdd48 · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

Dataset Distillation with Probabilistic Latent Features An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-15T22:41:54.761668Z

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Observation 185931f6-48fd-4c6d-8f40-9609a9348289 · outbound

This paper cites Minimizing the accumulated trajectory error to improve dataset distillation.

Dataset Distillation with Probabilistic Latent Features Minimizing the accumulated trajectory error to improve dataset distillation

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:41:55.506813Z

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Observation 28ba4a15-7fb5-455d-9d8a-2ddb73182ffc · outbound

This paper cites Reliable mutual distillation for medical image seg- mentation under imperfect annotations.

Dataset Distillation with Probabilistic Latent Features Reliable mutual distillation for medical image seg- mentation under imperfect annotations

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:41:55.491742Z

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Observation 1093512d-675f-4a8d-8994-0dbc23d230b4 · outbound

This paper cites On calibration of modern neural networks.

Dataset Distillation with Probabilistic Latent Features On calibration of modern neural networks

Reference 9

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Observation 5a8fed0a-a8aa-4c9e-9da9-c1f470510b96 · outbound

This paper cites Vision trans- formers for small histological datasets learned through knowledge distillation.

Dataset Distillation with Probabilistic Latent Features Vision trans- formers for small histological datasets learned through knowledge distillation

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:41:55.466877Z

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Observation 778622af-f8f4-4833-bf4d-08bd9c8a8fa4 · outbound

This paper cites What uncertainties do we need in bayesian deep learning for computer vision? Advances in neural information processing systems, 30, 2017.

Dataset Distillation with Probabilistic Latent Features What uncertainties do we need in bayesian deep learning for computer vision? Advances in neural information processing systems, 30, 2017

Reference 11

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Observation 9d289b26-4c69-4c8e-ab0d-963277cb0d97 · outbound

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

Dataset Distillation with Probabilistic Latent Features Learning multiple layers of features from tiny images

Reference 12

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Observation b7de620a-e9ff-4a83-a806-aa4758ea8193 · outbound

This paper cites A Comprehensive Survey of Dataset Distillation.

Dataset Distillation with Probabilistic Latent Features A Comprehensive Survey of Dataset Distillation

Reference 13

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Observation 03ff1718-68ee-4743-a0eb-f62e712fd3e6 · outbound

This paper cites Soft-label anonymous gastric x-ray image distillation.

Dataset Distillation with Probabilistic Latent Features Soft-label anonymous gastric x-ray image distillation

Reference 14

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Observation e2f13908-1e60-4113-87bc-25603af07f3f · outbound

This paper cites Compressed gastric image generation based on soft-label dataset distillation for medical data sharing.Computer Meth- ods and Programs in Biomedicine, 227:107189, 2022.

Dataset Distillation with Probabilistic Latent Features Compressed gastric image generation based on soft-label dataset distillation for medical data sharing.Computer Meth- ods and Programs in Biomedicine, 227:107189, 2022

Reference 15

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Observation d16f793c-86dd-4457-aa54-77477dd17682 · outbound

This paper cites Dataset Distillation for Medical Dataset Sharing.

Dataset Distillation with Probabilistic Latent Features Dataset Distillation for Medical Dataset Sharing

Reference 16

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Observation 663954b8-d433-4c90-9880-926e981514e2 · outbound

This paper cites Image Distillation for Safe Data Sharing in Histopathology.

Dataset Distillation with Probabilistic Latent Features Image Distillation for Safe Data Sharing in Histopathology

Reference 17

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Observation f069197a-93ba-4d82-acb3-892b232ea290 · outbound

This paper cites Few-shot dataset dis- tillation via translative pre-training.

Dataset Distillation with Probabilistic Latent Features Few-shot dataset dis- tillation via translative pre-training

Reference 18

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Observation 8632c0f3-0d76-4673-ba13-624a3f1a301c · outbound

This paper cites Dataset distillation via factorization.

Dataset Distillation with Probabilistic Latent Features Dataset distillation via factorization

Reference 19

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Observation 4ff2bb3d-271d-4fc1-9632-19b540ff9cf2 · outbound

This paper cites Dream: Efficient dataset distillation by repre- sentative matching.

Dataset Distillation with Probabilistic Latent Features Dream: Efficient dataset distillation by repre- sentative matching

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:41:55.373548Z

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Observation e0507d91-e2bd-40c5-9eeb-8cbecca8bd39 · outbound

This paper cites Predictive uncertainty es- timation via prior networks.

Dataset Distillation with Probabilistic Latent Features Predictive uncertainty es- timation via prior networks

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:41:55.359488Z

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Observation 0705d0c8-df5d-4e1e-a1fb-786a65fa1cf9 · outbound

This paper cites Reverse kl-divergence training of prior networks: Improved uncertainty and adver- sarial robustness.

Dataset Distillation with Probabilistic Latent Features Reverse kl-divergence training of prior networks: Improved uncertainty and adver- sarial robustness

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:41:55.345417Z

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Observation 1631147d-6f7f-4f2d-a94e-f33f1b74b615 · outbound

This paper cites Stochastic seg- mentation networks: Modelling spatially correlated aleatoric uncertainty.

Dataset Distillation with Probabilistic Latent Features Stochastic seg- mentation networks: Modelling spatially correlated aleatoric uncertainty

Reference 23

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Observation 9f0f5016-e212-4aa8-91c6-ced89ca63c54 · outbound

This paper cites Dataset Meta-Learning from Kernel Ridge-Regression.

Dataset Distillation with Probabilistic Latent Features Dataset Meta-Learning from Kernel Ridge-Regression

Reference 24

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Observation 9a803da6-d459-40c4-a55d-b943d473942a · outbound

This paper cites Liu, Yuri A.

Dataset Distillation with Probabilistic Latent Features Liu, Yuri A

Reference 25

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Observation 04dddd71-7423-476a-8ab3-42ef9cba13e6 · outbound

This paper cites Stylegan- xl: Scaling stylegan to large diverse datasets.

Dataset Distillation with Probabilistic Latent Features Stylegan- xl: Scaling stylegan to large diverse datasets

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:41:55.302375Z

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Observation b54fd8df-131e-4a66-a7ba-16f9d81e134f · outbound

This paper cites Eviden- tial deep learning to quantify classification uncertainty.

Dataset Distillation with Probabilistic Latent Features Eviden- tial deep learning to quantify classification uncertainty

Reference 27

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Observation 0f3ad953-315a-4398-a8a5-a22d1027f98f · outbound

This paper cites Generative teaching networks: Accelerating neural architecture search by learning to gener- ate synthetic training data.

Dataset Distillation with Probabilistic Latent Features Generative teaching networks: Accelerating neural architecture search by learning to gener- ate synthetic training data

Reference 28

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Observation 912d68c2-3266-4d9f-b6ab-4ea60c91709d · outbound

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

Dataset Distillation with Probabilistic Latent Features Cafe: Learning to condense dataset by align- ing features

Reference 29

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Observation adce42fe-b036-4458-80f5-aba05104b301 · outbound

This paper cites Dataset Distillation.

Dataset Distillation with Probabilistic Latent Features Dataset Distillation

Reference 30

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Observation 7fa1b677-f8ab-4111-9518-aaf84dff8753 · outbound

This paper cites Bayesian deep learn- ing and a probabilistic perspective of generalization.

Dataset Distillation with Probabilistic Latent Features Bayesian deep learn- ing and a probabilistic perspective of generalization

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:41:55.242702Z

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Observation 4b04fc92-1d75-482a-b3e3-1550c4fd59c5 · outbound

This paper cites Medm- nist v2-a large-scale lightweight benchmark for 2d and 3d biomedical image classification.

Dataset Distillation with Probabilistic Latent Features Medm- nist v2-a large-scale lightweight benchmark for 2d and 3d biomedical image classification

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:41:55.226584Z

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Observation 04473332-eab8-4b05-b5f3-4013903ba7e0 · outbound

This paper cites Bootstrapping semi-supervised medical image segmentation with anatomical-aware contrastive dis- tillation.

Dataset Distillation with Probabilistic Latent Features Bootstrapping semi-supervised medical image segmentation with anatomical-aware contrastive dis- tillation

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:41:55.210760Z

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Observation 4d99ac51-e090-4c9d-bac0-38b72c6cc4ee · outbound

This paper cites Dataset Distillation: A Comprehensive Review.

Dataset Distillation with Probabilistic Latent Features Dataset Distillation: A Comprehensive Review

Reference 34

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Observation 585071c7-f1df-40b4-bc1e-695c828387a0 · outbound

This paper cites Accelerating dataset distillation via model augmenta- tion.

Dataset Distillation with Probabilistic Latent Features Accelerating dataset distillation via model augmenta- tion

Reference 35

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Observation ff0c2ae4-e6f9-4c64-8e3d-b483430589ee · outbound

This paper cites Dataset condensation with differ- entiable siamese augmentation.

Dataset Distillation with Probabilistic Latent Features Dataset condensation with differ- entiable siamese augmentation

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:41:55.179616Z

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This paper cites Synthesizing informative train- ing samples with gan.

Dataset Distillation with Probabilistic Latent Features Synthesizing informative train- ing samples with gan

Reference 37

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Observation e52aa5bc-49b1-4478-81bd-f14312c6a8f0 · outbound

This paper cites Dataset condensation with distri- bution matching.

Dataset Distillation with Probabilistic Latent Features Dataset condensation with distri- bution matching

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:41:55.149507Z

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Observation 29fc9e73-511a-4206-a391-3fbf84769fa8 · outbound

This paper cites Dataset condensation with gradient matching.

Dataset Distillation with Probabilistic Latent Features Dataset condensation with gradient matching

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:41:55.133358Z

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Observation c42022df-41cb-4fe6-95e6-87bb07798bbe · outbound

This paper cites Im- proved distribution matching for dataset condensation.

Dataset Distillation with Probabilistic Latent Features Im- proved distribution matching for dataset condensation

Reference 40

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Observation fcb0f980-30f3-4ada-80d9-849a09a805eb · outbound

This paper cites Rethinking data dis- tillation: Do not overlook calibration.

Dataset Distillation with Probabilistic Latent Features Rethinking data dis- tillation: Do not overlook calibration

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:41:55.108579Z

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Observation fa54811e-97c9-4592-aa05-15a966221bda · outbound

This paper cites 1; • The main paper can forward reference sub-sections within the supplementary explicitly (e.g.

Dataset Distillation with Probabilistic Latent Features 1; • The main paper can forward reference sub-sections within the supplementary explicitly (e.g

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:41:55.092569Z

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Pith citing papers

Observation b78e9c3b-357f-4e6c-86a9-492a8065e207 · inbound

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

The Evolution of Dataset Distillation: Toward Scalable and Generalizable Solutions Dataset Distillation with Probabilistic Latent Features

Reference 44

Resolution: verified exact; local_arxiv, observed 2026-08-08T18:29:53.194332Z

Source-reported events for the cited work

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