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Paper Citation Record · LEDGER

Towards Nonlinear Disentanglement in Natural Data with Temporal Sparse Coding

As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:2007.10930.

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

pith.paper-citation-record.v1
2007.10930 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 8 of 8 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T22:38:05.939042Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T14:27:03.019672Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 7f614457-bace-4d3a-afcf-0aad5d4f5021 · inbound

Information Subtraction: Learning Representations for Conditional Entropy cites this paper.

Information Subtraction: Learning Representations for Conditional Entropy Towards Nonlinear Disentanglement in Natural Data with Temporal Sparse Coding

Reference 6

Resolution
unresolved
no resolver link, observed 2026-08-10T22:38:05.939042Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T22:38:05.939042Z digest=sha256:a98b1f0b914d6ebd4afa8e2649f2547fc386c4e3414a83e962f2eb79546a714b

Observation ba322dac-e285-4679-a2aa-3f0d270d11e2 · inbound

Learning General Causal Structures with Hidden Dynamic Process for Climate Analysis cites this paper.

Learning General Causal Structures with Hidden Dynamic Process for Climate Analysis Towards Nonlinear Disentanglement in Natural Data with Temporal Sparse Coding

Reference 35

Resolution
unresolved
no resolver link, observed 2026-08-10T17:16:27.300699Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T17:16:27.300699Z digest=sha256:02dabc44fa1745dfca02a25859b527c5b3687b7cce35872d95178e26b4016da9

Observation afaae60d-0995-4d97-b45c-f8d87e53465b · inbound

Mechanistic Independence: A Principle for Identifiable Disentangled Representations cites this paper.

Mechanistic Independence: A Principle for Identifiable Disentangled Representations Towards Nonlinear Disentanglement in Natural Data with Temporal Sparse Coding

Reference 10

Resolution
verified exact
arxiv_id, observed 2026-05-18T13:32:37.864555Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-18T13:31:41.141199Z digest=sha256:c36b227f224030651d5a1406876241fee77c624fdafec46b9b4c9125cbdb5c22

Observation 3336de24-b7a6-4c6b-8851-a358a7e00f42 · inbound

Provable Affine Identifiability of Nonlinear CCA under Latent Distributional Priors cites this paper.

Provable Affine Identifiability of Nonlinear CCA under Latent Distributional Priors Towards Nonlinear Disentanglement in Natural Data with Temporal Sparse Coding

Reference 33

Resolution
unresolved
no resolver link, observed 2026-08-04T11:30:51.306921Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T11:30:51.306921Z digest=sha256:0c2b4273a01bc154539161e997963bb9b7647d204e0ffa595f305825a1aff56f

Observation c95f8bf2-ae64-4c43-9e12-08929687049f · inbound

Cloning Deterministic Worlds: The Critical Role of Latent Geometry in Long-Horizon World Models cites this paper.

Cloning Deterministic Worlds: The Critical Role of Latent Geometry in Long-Horizon World Models Towards Nonlinear Disentanglement in Natural Data with Temporal Sparse Coding

Reference 14

Resolution
metadata mismatch
arxiv_id, observed 2026-05-18T02:50:48.267915Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-18T02:50:32.870296Z digest=sha256:9b9774504fc38825b14584af908516c8046b7b3215e22a682542aa4b102d7994

Observation 2740d289-0fd9-4a98-8df1-199cf2d642d5 · inbound

There Will Be a Scientific Theory of Deep Learning cites this paper.

There Will Be a Scientific Theory of Deep Learning Towards Nonlinear Disentanglement in Natural Data with Temporal Sparse Coding

Reference 9

Resolution
metadata mismatch
arxiv_id, observed 2026-05-11T15:21:08.916177Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-05-09T20:11:17.616190Z digest=sha256:5981f248c197bbc767bb9008eee8fec098a98a70c887348e840dc15509a73e74

Observation f04e751c-ce0e-43c6-b9f4-4cf67cc9b3e1 · inbound

MOSAIC: Module Discovery via Sparse Additive Identifiable Causal Learning for Scientific Time Series cites this paper.

MOSAIC: Module Discovery via Sparse Additive Identifiable Causal Learning for Scientific Time Series Towards Nonlinear Disentanglement in Natural Data with Temporal Sparse Coding

Reference 65

Resolution
metadata mismatch
arxiv_id, observed 2026-05-11T18:16:12.020820Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-05-08T16:19:53.680007Z digest=sha256:4043d0f21c5fc4a50155be68214ff7784cf4fb01b68b415bbfe796e54819e485

Observation c6ed99f2-70e1-4dcf-b855-239b04dd6460 · inbound

MoVA: Learning Asymmetric Dual Projections for Modular Long Video-Text Alignment cites this paper.

MoVA: Learning Asymmetric Dual Projections for Modular Long Video-Text Alignment Towards Nonlinear Disentanglement in Natural Data with Temporal Sparse Coding

Reference 24

Resolution
metadata mismatch
arxiv_id, observed 2026-07-02T14:27:03.021275Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-07-02T14:25:04.811472Z digest=sha256:97618528895a3baec42993f5e4c0f847af56110ce12b40624b9c457b115492b8