Paper Citation Record · LEDGER

SUICA: Learning Super-high Dimensional Sparse Implicit Neural Representations for Spatial Transcriptomics

As of 19 August 2026, this Paper Citation Record lists 13 of 13 outbound references and 1 inbound Pith citation observation for arXiv:2412.01124.

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

pith.paper-citation-record.v1
2412.01124 v2

Coverage vector

measured 13 of 13 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T04:45:42.123586Z

measured 14 of 14 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T15:25:12.546022Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-07T15:25:12.818325Z

Reference resolution

13 of 13 outbound references displayed

verified exact0
verified fuzzy9
unresolved3
parse uncertain0
malformed identifier1
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 8a77cb44-c6cc-4a04-a966-19344bf14a49 · outbound

This paper cites Towards Deeper Understanding of Variational Autoencoding Models.

SUICA: Learning Super-high Dimensional Sparse Implicit Neural Representations for Spatial Transcriptomics Towards Deeper Understanding of Variational Autoencoding Models

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-12T04:45:42.074565Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0c04329b-7625-449e-ac55-4c97db9182fd · outbound

This paper cites MSE: ×10−2.

SUICA: Learning Super-high Dimensional Sparse Implicit Neural Representations for Spatial Transcriptomics MSE: ×10−2

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:45:42.302859Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation e0241bf0-5698-4615-93c8-ccf8a9a7f22b · outbound

This paper cites MAE/MSE: ×10−2.

SUICA: Learning Super-high Dimensional Sparse Implicit Neural Representations for Spatial Transcriptomics MAE/MSE: ×10−2

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:45:42.289364Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 318a81e8-e1b9-4196-9f78-9bcc368f4e5b · outbound

This paper cites Note that for Visium-Human Brain, there is no annotation of cell type for the evaluation of ARI, while reference ARI is 0.428 for Mouse Brain.

SUICA: Learning Super-high Dimensional Sparse Implicit Neural Representations for Spatial Transcriptomics Note that for Visium-Human Brain, there is no annotation of cell type for the evaluation of ARI, while reference ARI is 0.428 for Mouse Brain

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:45:42.258662Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation e13703fa-733a-48d5-94cc-abb19b71fc29 · outbound

This paper cites These issues might lead to ambiguity in gene expression that is not wanted.

SUICA: Learning Super-high Dimensional Sparse Implicit Neural Representations for Spatial Transcriptomics These issues might lead to ambiguity in gene expression that is not wanted

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:45:42.274752Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 8ae06817-a900-4a00-9d19-5590d7fa4be4 · outbound

This paper cites % rep- resents the proportion of the spots used for training while test set remains 20%.

SUICA: Learning Super-high Dimensional Sparse Implicit Neural Representations for Spatial Transcriptomics % rep- resents the proportion of the spots used for training while test set remains 20%

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:45:42.178296Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 247bd55e-6110-432c-bbec-4796b64c912c · outbound

This paper cites an unresolved cited work.

SUICA: Learning Super-high Dimensional Sparse Implicit Neural Representations for Spatial Transcriptomics Unresolved cited work

Reference 11

Resolution: unresolved; raw_fallback, observed 2026-08-12T04:45:42.209463Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation bebf625d-dee0-4c4d-acea-f4925d35f270 · outbound

This paper cites MAE/MSE: ×10−1.

SUICA: Learning Super-high Dimensional Sparse Implicit Neural Representations for Spatial Transcriptomics MAE/MSE: ×10−1

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:45:42.193942Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation c6cc90e0-d43f-4b73-8f3e-6ee99ba45067 · outbound

This paper cites Convolutional Occupancy Networks.

SUICA: Learning Super-high Dimensional Sparse Implicit Neural Representations for Spatial Transcriptomics Convolutional Occupancy Networks

Reference 2017

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:45:42.346889Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 1ecedcd8-1d64-4fe7-ade3-db297709af72 · outbound

This paper cites an unresolved cited work.

SUICA: Learning Super-high Dimensional Sparse Implicit Neural Representations for Spatial Transcriptomics Unresolved cited work

Reference 2020

Resolution: unresolved; raw_fallback, observed 2026-08-12T04:45:42.241503Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation cbe630e4-9cb1-49d2-866e-b7041716a56d · outbound

This paper cites H., Li, W., Vercauteren, T., Ourselin, S., and Jorge Cardoso, M.

SUICA: Learning Super-high Dimensional Sparse Implicit Neural Representations for Spatial Transcriptomics H., Li, W., Vercauteren, T., Ourselin, S., and Jorge Cardoso, M

Reference 2021

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:45:42.332766Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 7896dc9e-1cc3-4e91-bc5a-d744b129be90 · outbound

This paper cites an unresolved cited work.

SUICA: Learning Super-high Dimensional Sparse Implicit Neural Representations for Spatial Transcriptomics Unresolved cited work

Reference 2023

Resolution: malformed identifier; raw_fallback, observed 2026-08-12T04:45:42.317724Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 6bf788b7-02e3-40e4-9764-756bccb029bb · outbound

This paper cites The progressive training paradigm also guarantees that each module is doing the assigned job, keeping the coupling at minimum.

SUICA: Learning Super-high Dimensional Sparse Implicit Neural Representations for Spatial Transcriptomics The progressive training paradigm also guarantees that each module is doing the assigned job, keeping the coupling at minimum

Reference 2024

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:45:42.224897Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Pith citing papers

Observation 1f939a2f-2c03-4294-96b2-42e7572f94d8 · inbound

Continuous Representation Methods, Theories, and Applications: An Overview and Perspectives cites this paper.

Continuous Representation Methods, Theories, and Applications: An Overview and Perspectives SUICA: Learning Super-high Dimensional Sparse Implicit Neural Representations for Spatial Transcriptomics

Reference 14

Resolution: verified exact; local_arxiv, observed 2026-08-07T15:25:12.936464Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.