Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-06T18:52:38.803990Z
Paper Citation Record · LEDGER
Estimating Dataset Dimension via Singular Metrics under the Manifold Hypothesis: Application to Inverse Problems
As of 18 August 2026, this Paper Citation Record lists 20 of 20 outbound references and 0 inbound Pith citation observations for arXiv:2507.07291.
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 2026-08-18T06:34:40.430872+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
20 of 20 outbound references displayed
- verified exact1
- verified fuzzy13
- unresolved3
- parse uncertain0
- malformed identifier0
- metadata mismatch3
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation a63b54e9-28ca-4e85-b4c2-705ef3349bbc · outbound
This paper cites Losing dimensions: Ge- ometric memorization in generative diffusion.
Estimating Dataset Dimension via Singular Metrics under the Manifold Hypothesis: Application to Inverse Problems Losing dimensions: Ge- ometric memorization in generative diffusion
Reference 1
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T18:52:37.431929Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 588fb563-241d-4aa2-972c-5d1be17cf281 · outbound
This paper cites Visualizing feature maps for model selec- tion in convolutional neural networks.
Estimating Dataset Dimension via Singular Metrics under the Manifold Hypothesis: Application to Inverse Problems Visualizing feature maps for model selec- tion in convolutional neural networks
Reference 13
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:52:39.114798Z
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.
Observation 80864cc9-2c31-4ecd-8e5c-1b1f78dbd0e1 · outbound
This paper cites Manifold learning: What, how, and why.
Estimating Dataset Dimension via Singular Metrics under the Manifold Hypothesis: Application to Inverse Problems Manifold learning: What, how, and why
Reference 14
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:52:39.097830Z
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.
Observation a27ce9df-a3ab-4a96-8741-a68d82042272 · outbound
This paper cites InfoCatVAE: Representation Learning with Categorical Variational Autoencoders.
Estimating Dataset Dimension via Singular Metrics under the Manifold Hypothesis: Application to Inverse Problems InfoCatVAE: Representation Learning with Categorical Variational Autoencoders
Reference 16
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-08-06T18:52:38.864551Z
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.
Observation 71cce1d0-2d9f-407c-a834-d3fa55e38180 · outbound
This paper cites Tashlinskiy and Alena V.
Estimating Dataset Dimension via Singular Metrics under the Manifold Hypothesis: Application to Inverse Problems Tashlinskiy and Alena V
Reference 18
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:52:39.053092Z
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.
Observation ae3a122c-67ab-4791-918a-0473833f7b8d · outbound
This paper cites Score-based generative model learn manifold-like structures with constrained mixing.
Estimating Dataset Dimension via Singular Metrics under the Manifold Hypothesis: Application to Inverse Problems Score-based generative model learn manifold-like structures with constrained mixing
Reference 19
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:52:39.036583Z
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.
Observation ca214ed0-3e37-43c3-8f3c-869d28bb09ce · outbound
This paper cites 30 Appendix A.
Estimating Dataset Dimension via Singular Metrics under the Manifold Hypothesis: Application to Inverse Problems 30 Appendix A
Reference 20
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T18:52:38.803990Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 16b3b526-9ca8-4c1e-9cad-77912db8dc68 · outbound
This paper cites Manifold learn- ing benefits GANs.
Estimating Dataset Dimension via Singular Metrics under the Manifold Hypothesis: Application to Inverse Problems Manifold learn- ing benefits GANs
Reference 1954
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:52:39.082604Z
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.
Observation ca6510d0-2c60-4f11-a353-843b62dbb4ae · outbound
This paper cites Little, Jason Lee, Yoon-Mo Jung, and Mauro Maggioni.
Estimating Dataset Dimension via Singular Metrics under the Manifold Hypothesis: Application to Inverse Problems Little, Jason Lee, Yoon-Mo Jung, and Mauro Maggioni
Reference 1999
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:52:39.147917Z
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.
Observation eac34654-398b-4463-9433-1eb6c1617129 · outbound
This paper cites Caterini, Gabriel Loaiza-Ganem, Geoff Pleiss, and John P.
Estimating Dataset Dimension via Singular Metrics under the Manifold Hypothesis: Application to Inverse Problems Caterini, Gabriel Loaiza-Ganem, Geoff Pleiss, and John P
Reference 2001
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:52:39.195257Z
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.
Observation 00cabc30-5e2f-4362-8a4f-50617f428cca · outbound
This paper cites 22nd International Conference on Machine Learn- ing (ICML 2005).
Estimating Dataset Dimension via Singular Metrics under the Manifold Hypothesis: Application to Inverse Problems 22nd International Conference on Machine Learn- ing (ICML 2005)
Reference 2005
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:52:39.068101Z
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.
Observation eb9f4665-66e5-478e-bb6e-cb28be4320a6 · outbound
This paper cites Competitive Training of Mixtures of Independent Deep Generative Models.
Estimating Dataset Dimension via Singular Metrics under the Manifold Hypothesis: Application to Inverse Problems Competitive Training of Mixtures of Independent Deep Generative Models
Reference 2011
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-08-06T18:52:38.902979Z
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.
Observation 7a825ddb-b692-40f4-9836-c74df3d4d6dc · outbound
This paper cites A Geometric View of Data Complexity: Efficient Local Intrinsic Dimension Estimation with Diffusion Models.
Estimating Dataset Dimension via Singular Metrics under the Manifold Hypothesis: Application to Inverse Problems A Geometric View of Data Complexity: Efficient Local Intrinsic Dimension Estimation with Diffusion Models
Reference 2013
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T18:52:37.862387Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation d86b871d-1df6-4da4-a429-a841a7f6a877 · outbound
This paper cites In- trinsic dimension estimation: Relevant techniques and a benchmark framework.
Estimating Dataset Dimension via Singular Metrics under the Manifold Hypothesis: Application to Inverse Problems In- trinsic dimension estimation: Relevant techniques and a benchmark framework
Reference 2014
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:52:39.210871Z
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.
Observation 2165a3df-07de-4f68-8355-9253fd770a25 · outbound
This paper cites Variational deep embedding: an unsupervised and generative approach to clus- tering.
Estimating Dataset Dimension via Singular Metrics under the Manifold Hypothesis: Application to Inverse Problems Variational deep embedding: an unsupervised and generative approach to clus- tering
Reference 2015
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:52:39.179286Z
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.
Observation 91455ca6-0042-4334-a86a-c010f0faee8b · outbound
This paper cites [Lip99] Alan H.
Estimating Dataset Dimension via Singular Metrics under the Manifold Hypothesis: Application to Inverse Problems [Lip99] Alan H
Reference 2017
- Resolution
- verified exact
- doi, observed 2026-08-06T18:52:38.880357Z
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.
Observation 38444ce2-ed7f-4586-89a1-967b19f5b2d8 · outbound
This paper cites A Modular Deep Learning-based Approach for Diffuse Optical Tomography Reconstruction.
Estimating Dataset Dimension via Singular Metrics under the Manifold Hypothesis: Application to Inverse Problems A Modular Deep Learning-based Approach for Diffuse Optical Tomography Reconstruction
Reference 2018
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-08-06T18:52:38.943149Z
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.
Observation 7dab9990-939c-4628-b6c5-ad38c3ab6dff · outbound
This paper cites Pel- legrini, Ralf S.
Estimating Dataset Dimension via Singular Metrics under the Manifold Hypothesis: Application to Inverse Problems Pel- legrini, Ralf S
Reference 2021
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:52:39.226532Z
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.
Observation e00ff147-cefa-40bf-a0e4-d24ffccded2e · outbound
This paper cites [KSE18] Mahyar Khayatkhoei, Maneesh Singh, and Ahmed Elgammal.
Estimating Dataset Dimension via Singular Metrics under the Manifold Hypothesis: Application to Inverse Problems [KSE18] Mahyar Khayatkhoei, Maneesh Singh, and Ahmed Elgammal
Reference 2022
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:52:39.163708Z
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.
Observation 7972e0b5-f357-42d0-82f9-6ec8e9520800 · outbound
This paper cites Effectiveness of correlation and information measures for synthesis of recurrent algorithms for estimating spatial deformations of video sequences.
Estimating Dataset Dimension via Singular Metrics under the Manifold Hypothesis: Application to Inverse Problems Effectiveness of correlation and information measures for synthesis of recurrent algorithms for estimating spatial deformations of video sequences
Reference 2024
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:52:39.131882Z
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.
Pith citing papers
No inbound Pith citation observations are available.