Paper Citation Record · LEDGER

Conditional Distribution Quantization in Machine Learning

As of 11 August 2026, this Paper Citation Record lists 37 of 37 outbound references and 0 inbound Pith citation observations for arXiv:2502.07151.

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

pith.paper-citation-record.v1
2502.07151 v2

Coverage vector

measured 37 of 37 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-08T13:44:54.379969Z

measured 37 of 37 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+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

37 of 37 outbound references displayed

verified exact1
verified fuzzy28
unresolved8
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 0f6c683c-de40-4937-9b51-addfe5f7c7b5 · outbound

This paper cites write newline.

Conditional Distribution Quantization in Machine Learning write newline

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-08T13:44:54.264828Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fe761f02-16d2-49a4-8c3d-89ef59f115ad · outbound

This paper cites Image-to-image regression with distribution-free uncertainty quantification and applications in imaging.

Conditional Distribution Quantization in Machine Learning Image-to-image regression with distribution-free uncertainty quantification and applications in imaging

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:44:55.073546Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 92ff80c2-5e0a-4fb3-bb80-f4e6ffbcfa78 · outbound

This paper cites pca GAN : Improving posterior-sampling c GAN s via principal component regularization.

Conditional Distribution Quantization in Machine Learning pca GAN : Improving posterior-sampling c GAN s via principal component regularization

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:44:55.060557Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 24433340-e0b3-49c1-a374-0057e1ea4459 · outbound

This paper cites an unresolved cited work.

Conditional Distribution Quantization in Machine Learning Unresolved cited work

Reference 4

Resolution: unresolved; raw_fallback, observed 2026-08-08T13:44:55.048726Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation ac1ea90a-16e7-4f82-afc2-9518f3db6d4a · outbound

This paper cites Weight uncertainty in neural networks.

Conditional Distribution Quantization in Machine Learning Weight uncertainty in neural networks

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:44:55.038298Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 0c99d6c2-1b2d-4c2b-8132-f4b5055226d6 · outbound

This paper cites About the multidimensional competitive learning vector quantization algorithm with constant gain.

Conditional Distribution Quantization in Machine Learning About the multidimensional competitive learning vector quantization algorithm with constant gain

Reference 6

Resolution: verified exact; raw_fallback, observed 2026-08-08T13:44:54.738327Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation b774290b-231c-4862-b197-841449628293 · outbound

This paper cites Large scale GAN training for high fidelity natural image synthesis.

Conditional Distribution Quantization in Machine Learning Large scale GAN training for high fidelity natural image synthesis

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-08T13:44:54.287289Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 60bc36a6-3d1e-469c-995c-c1237bc93a28 · outbound

This paper cites Relaxing bijectivity constraints with continuously indexed normalising flows.

Conditional Distribution Quantization in Machine Learning Relaxing bijectivity constraints with continuously indexed normalising flows

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:44:55.020108Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 0690bae9-e6ca-4485-b293-bb0c19f8d4a9 · outbound

This paper cites Density estimation using real NVP.

Conditional Distribution Quantization in Machine Learning Density estimation using real NVP

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-08T13:44:54.293789Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation be77fbcb-49b4-46f8-a8f5-937de8c15407 · outbound

This paper cites U-net: deep learning for cell counting, detection, and morphometry.

Conditional Distribution Quantization in Machine Learning U-net: deep learning for cell counting, detection, and morphometry

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:44:54.999710Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation c12b86d8-544f-42e7-b6a1-027b1694231e · outbound

This paper cites Dropout as a bayesian approximation: Representing model uncertainty in deep learning.

Conditional Distribution Quantization in Machine Learning Dropout as a bayesian approximation: Representing model uncertainty in deep learning

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:44:54.989643Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation f1e458a9-a281-4d03-b4f9-9a5e55a713b0 · outbound

This paper cites Foundations of quantization for probability distributions, volume 1730 of Lecture Notes in Mathematics.

Conditional Distribution Quantization in Machine Learning Foundations of quantization for probability distributions, volume 1730 of Lecture Notes in Mathematics

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-08T13:44:54.302878Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 74845db5-431a-4447-b27f-21ba7573506d · outbound

This paper cites Multiple choice learning: Learning to produce multiple structured outputs.

Conditional Distribution Quantization in Machine Learning Multiple choice learning: Learning to produce multiple structured outputs

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:44:54.979124Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 87d6192f-0676-4223-be67-bd6df13956fd · outbound

This paper cites GANs Trained by a Two Time - Scale Update Rule Converge to a Local Nash Equilibrium.

Conditional Distribution Quantization in Machine Learning GANs Trained by a Two Time - Scale Update Rule Converge to a Local Nash Equilibrium

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:44:54.969225Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation ab99c26c-2d88-4a8b-abd9-a268b4be1dd5 · outbound

This paper cites What uncertainties do we need in bayesian deep learning for computer vision? In Advances in Neural Information Processing Systems (NeurIPS), 2017.

Conditional Distribution Quantization in Machine Learning What uncertainties do we need in bayesian deep learning for computer vision? In Advances in Neural Information Processing Systems (NeurIPS), 2017

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:44:54.958976Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 36d37374-4e1b-402d-bea2-c53425b0a1d6 · outbound

This paper cites Learning vector quantization for pattern recognition.

Conditional Distribution Quantization in Machine Learning Learning vector quantization for pattern recognition

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:44:54.948958Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 13237861-3970-4f00-af5d-8cb5f33617cb · outbound

This paper cites Conformal prediction masks: Visualizing uncertainty in medical imaging.

Conditional Distribution Quantization in Machine Learning Conformal prediction masks: Visualizing uncertainty in medical imaging

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:44:54.938312Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation d7d63826-fb6f-4c6f-a4c3-04ebea4270ec · outbound

This paper cites Improved Precision and Recall Metric for Assessing Generative Models.

Conditional Distribution Quantization in Machine Learning Improved Precision and Recall Metric for Assessing Generative Models

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-08T13:44:54.321765Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 530982c2-910d-46a1-9e39-f0e8e7a41e94 · outbound

This paper cites Simple and scalable predictive uncertainty estimation using deep ensembles.

Conditional Distribution Quantization in Machine Learning Simple and scalable predictive uncertainty estimation using deep ensembles

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:44:54.928705Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 0132eee9-5da2-40e5-a5ea-6c5a7291024b · outbound

This paper cites Confident multiple choice learning.

Conditional Distribution Quantization in Machine Learning Confident multiple choice learning

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:44:54.918680Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 6f6c6d7d-9062-4335-8f96-5a5381d16d2d · outbound

This paper cites Stochastic multiple choice learning for training diverse deep ensembles.

Conditional Distribution Quantization in Machine Learning Stochastic multiple choice learning for training diverse deep ensembles

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:44:54.908752Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 72ecb0ff-db52-458f-a9ae-1ffc6aa70c0e · outbound

This paper cites Resilient multiple choice learning: A learned scoring scheme with application to audio scene analysis.

Conditional Distribution Quantization in Machine Learning Resilient multiple choice learning: A learned scoring scheme with application to audio scene analysis

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:44:54.898812Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation a327f049-bd5c-4ea5-bfb2-710164a82297 · outbound

This paper cites Winner-takes-all learners are geometry-aware conditional density estimators.

Conditional Distribution Quantization in Machine Learning Winner-takes-all learners are geometry-aware conditional density estimators

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:44:54.888894Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 8058b26e-3484-44e0-81ad-af441b819e7a · outbound

This paper cites Implicit maximum likelihood estimation, 2019.

Conditional Distribution Quantization in Machine Learning Implicit maximum likelihood estimation, 2019

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:44:54.878658Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation fcd4d2fa-3ab8-4f98-b35d-5724c3c9a221 · outbound

This paper cites An algorithm for vector quantizer design.

Conditional Distribution Quantization in Machine Learning An algorithm for vector quantizer design

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:44:54.868312Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 671ddbfd-cedb-4cba-9677-d544247b1a17 · outbound

This paper cites Least squares quantization in pcm.

Conditional Distribution Quantization in Machine Learning Least squares quantization in pcm

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:44:54.858667Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 85f7aac3-5d5d-45bd-b513-c8bb3a36f668 · outbound

This paper cites On the posterior distribution in denoising: Application to uncertainty quantification.

Conditional Distribution Quantization in Machine Learning On the posterior distribution in denoising: Application to uncertainty quantification

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:44:54.849001Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 6a1e285f-ee4b-4eb0-8616-55f8a2cdf869 · outbound

This paper cites Uncertainty quantification via neural posterior principal components.

Conditional Distribution Quantization in Machine Learning Uncertainty quantification via neural posterior principal components

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:44:54.838190Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 5ebefc67-7980-4d3f-8de7-185a52a3d31d · outbound

This paper cites Introduction to vector quantization and its applications for numerics.

Conditional Distribution Quantization in Machine Learning Introduction to vector quantization and its applications for numerics

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-08T13:44:54.357009Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4ede646c-2d9e-43f2-94a2-71fb68b24009 · outbound

This paper cites a henb \.

Conditional Distribution Quantization in Machine Learning a henb \

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:44:54.827943Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 93a32b75-6a6d-4322-9854-3a2d16164479 · outbound

This paper cites CHIMLE : Conditional hierarchical IMLE.

Conditional Distribution Quantization in Machine Learning CHIMLE : Conditional hierarchical IMLE

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:44:54.817862Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation d81b50cf-9e0f-43b6-af83-b305bbcb62a7 · outbound

This paper cites Annealed multiple choice learning: Overcoming limitations of winner-takes-all with annealing.

Conditional Distribution Quantization in Machine Learning Annealed multiple choice learning: Overcoming limitations of winner-takes-all with annealing

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:44:54.806954Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation dc9636b7-5880-410d-b80f-19c738c96fe0 · outbound

This paper cites Computational Optimal Transport: With Applications to Data Science.

Conditional Distribution Quantization in Machine Learning Computational Optimal Transport: With Applications to Data Science

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:44:54.795417Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation fd1ed034-47d6-4ec1-953d-7d63a5e99e3f · outbound

This paper cites U-net: Convolutional networks for biomedical image segmentation.

Conditional Distribution Quantization in Machine Learning U-net: Convolutional networks for biomedical image segmentation

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:44:54.783422Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 30742a41-f70a-42b6-a80b-b0883ac67a2a · outbound

This paper cites an unresolved cited work.

Conditional Distribution Quantization in Machine Learning Unresolved cited work

Reference 35

Resolution: unresolved; raw_fallback, observed 2026-08-08T13:44:54.772098Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 4aff14f0-b9ce-4740-834c-162209200f80 · outbound

This paper cites Precision-recall divergence optimization for generative modeling with GAN s and normalizing flows.

Conditional Distribution Quantization in Machine Learning Precision-recall divergence optimization for generative modeling with GAN s and normalizing flows

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:44:54.761481Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation bab01c41-41d0-47eb-b8cf-61096877b20e · outbound

This paper cites On the expressivity of bi-lipschitz normalizing flows.

Conditional Distribution Quantization in Machine Learning On the expressivity of bi-lipschitz normalizing flows

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:44:54.750296Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Pith citing papers

No inbound Pith citation observations are available.