Paper Citation Record · LEDGER

ShapeEmbed: a self-supervised learning framework for 2D contour quantification

As of 20 August 2026, this Paper Citation Record lists 62 of 62 outbound references and 2 inbound Pith citation observations for arXiv:2507.01009.

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

pith.paper-citation-record.v1
2507.01009 v1

Coverage vector

measured 62 of 62 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T21:06:11.102994Z

measured 64 of 64 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 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-05T17:27:26.841134Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-05T17:27:26.952007Z

Reference resolution

62 of 62 outbound references displayed

verified exact2
verified fuzzy40
unresolved20
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 95dde69d-8e7d-4c2b-8074-a25e9f8666ea · outbound

This paper cites Identification of everyday objects on the basis of silhouette and outline versions.

ShapeEmbed: a self-supervised learning framework for 2D contour quantification Identification of everyday objects on the basis of silhouette and outline versions

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:06:13.952950Z

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 ebc2b33d-19b8-455d-8db9-961aff0742c5 · outbound

This paper cites Statistical shape analysis: with applications in R, volume 995.

ShapeEmbed: a self-supervised learning framework for 2D contour quantification Statistical shape analysis: with applications in R, volume 995

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:06:13.930163Z

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 b6bd5597-0184-4847-a92d-4892b13c62d2 · outbound

This paper cites Biology and physics of cell shape changes in development.

ShapeEmbed: a self-supervised learning framework for 2D contour quantification Biology and physics of cell shape changes in development

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:06:13.913524Z

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 f8069e5d-4f0f-4766-a903-1e15a2606d3c · outbound

This paper cites Decoding information in cell shape.

ShapeEmbed: a self-supervised learning framework for 2D contour quantification Decoding information in cell shape

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:06:13.894114Z

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 9752f1ce-9243-47d4-b90d-bdaa65efc042 · outbound

This paper cites Cell and nucleus shape as an indicator of tissue fluidity in carcinoma.

ShapeEmbed: a self-supervised learning framework for 2D contour quantification Cell and nucleus shape as an indicator of tissue fluidity in carcinoma

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:06:13.873620Z

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 05463ac2-7029-4a51-a97f-0c0990df92e4 · outbound

This paper cites Morphofeatures for unsupervised exploration of cell types, tissues, and organs in volume electron microscopy.

ShapeEmbed: a self-supervised learning framework for 2D contour quantification Morphofeatures for unsupervised exploration of cell types, tissues, and organs in volume electron microscopy

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:06:13.856415Z

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 6dd3e1eb-55bf-4f5d-b090-0c26025c76d3 · outbound

This paper cites Image-based multivariate profiling of drug responses from single cells.

ShapeEmbed: a self-supervised learning framework for 2D contour quantification Image-based multivariate profiling of drug responses from single cells

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:06:13.834461Z

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 ffccf935-3db9-4bd4-a882-b8188239064b · outbound

This paper cites Visualizing cellular imaging data using phenoplot.

ShapeEmbed: a self-supervised learning framework for 2D contour quantification Visualizing cellular imaging data using phenoplot

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:06:13.815686Z

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 2fab1cc0-23e0-4b53-9ffa-65f5b6ca433e · outbound

This paper cites Comparison of quantitative methods for cell-shape analysis.

ShapeEmbed: a self-supervised learning framework for 2D contour quantification Comparison of quantitative methods for cell-shape analysis

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:06:13.797304Z

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 06fdff75-4752-4bdf-baa3-3e391da8792d · outbound

This paper cites Reducing the dimensionality of data with neural networks.

ShapeEmbed: a self-supervised learning framework for 2D contour quantification Reducing the dimensionality of data with neural networks

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-06T21:06:10.827833Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 67a13633-2f58-403e-896e-8f6faa3b429e · outbound

This paper cites Auto-Encoding Variational Bayes.

ShapeEmbed: a self-supervised learning framework for 2D contour quantification Auto-Encoding Variational Bayes

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-06T21:06:10.832699Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation eb554706-958d-4706-99a9-24d16e6500ab · outbound

This paper cites Quantitative comparison of principal component analysis and unsupervised deep learning using variational autoencoders for shape analysis of motile cells.

ShapeEmbed: a self-supervised learning framework for 2D contour quantification Quantitative comparison of principal component analysis and unsupervised deep learning using variational autoencoders for shape analysis of motile cells

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:06:13.765974Z

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 028358db-ee03-4b17-8445-8688d65fa8f3 · outbound

This paper cites Evaluation of methods for generative modeling of cell and nuclear shape.

ShapeEmbed: a self-supervised learning framework for 2D contour quantification Evaluation of methods for generative modeling of cell and nuclear shape

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:06:13.747401Z

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 88cbf116-7947-4e33-804b-f15b296f6333 · outbound

This paper cites Kendall shape-vae: Learning shapes in a generative framework.

ShapeEmbed: a self-supervised learning framework for 2D contour quantification Kendall shape-vae: Learning shapes in a generative framework

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:06:13.730705Z

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 23c8f0d2-b44a-4b03-821c-76b411f43e87 · outbound

This paper cites Continuous kendall shape variational autoencoders.

ShapeEmbed: a self-supervised learning framework for 2D contour quantification Continuous kendall shape variational autoencoders

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:06:13.710876Z

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 38c89b2a-b912-4fdd-b6f9-8092719e619d · outbound

This paper cites Euclidean distance matrices: essential theory, algorithms, and applications.

ShapeEmbed: a self-supervised learning framework for 2D contour quantification Euclidean distance matrices: essential theory, algorithms, and applications

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:06:13.686894Z

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 45310871-251a-4789-a885-4890fe7741fc · outbound

This paper cites Multidimensional scaling.

ShapeEmbed: a self-supervised learning framework for 2D contour quantification Multidimensional scaling

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:06:13.667809Z

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 d067d0af-2e6c-4a14-be58-a60aec0b5a31 · outbound

This paper cites Multiscale distance matrix for fast plant leaf recognition.

ShapeEmbed: a self-supervised learning framework for 2D contour quantification Multiscale distance matrix for fast plant leaf recognition

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:06:13.650746Z

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 a5120298-4cf0-4002-85e9-11fe28af338e · outbound

This paper cites Wesd--weighted spectral distance for measuring shape dissimilarity.

ShapeEmbed: a self-supervised learning framework for 2D contour quantification Wesd--weighted spectral distance for measuring shape dissimilarity

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:06:13.630610Z

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 5c7be7c3-65a7-4e3d-a1bc-202776406f22 · outbound

This paper cites Cajal enables analysis and integration of single-cell morphological data using metric geometry.

ShapeEmbed: a self-supervised learning framework for 2D contour quantification Cajal enables analysis and integration of single-cell morphological data using metric geometry

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:06:13.613973Z

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 689ee5df-21c1-49ce-b436-f501d7325cb5 · outbound

This paper cites Highly accurate protein structure prediction with alphafold.

ShapeEmbed: a self-supervised learning framework for 2D contour quantification Highly accurate protein structure prediction with alphafold

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-06T21:06:10.890488Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6fe18a30-981e-4cf4-8325-d9d45a5b7fde · outbound

This paper cites S ch\"onberger, J uan Nunez-Iglesias , F ran c ois B oulogne, J oshua D.

ShapeEmbed: a self-supervised learning framework for 2D contour quantification S ch\"onberger, J uan Nunez-Iglesias , F ran c ois B oulogne, J oshua D

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-06T21:06:10.895784Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8a6b3cd8-0ec5-4885-b62c-3a48522ab9ae · outbound

This paper cites Quantitative morphological signatures define local signaling networks regulating cell morphology.

ShapeEmbed: a self-supervised learning framework for 2D contour quantification Quantitative morphological signatures define local signaling networks regulating cell morphology

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:06:13.582996Z

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 0dc85d5f-2c19-4aff-9114-3d85e59a4f36 · outbound

This paper cites Identification of phenotype-specific networks from paired gene expression--cell shape imaging data.

ShapeEmbed: a self-supervised learning framework for 2D contour quantification Identification of phenotype-specific networks from paired gene expression--cell shape imaging data

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:06:13.564532Z

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 9fe231da-6867-46aa-a571-860c5d728c39 · outbound

This paper cites Shape discrimination using fourier descriptors.

ShapeEmbed: a self-supervised learning framework for 2D contour quantification Shape discrimination using fourier descriptors

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:06:13.543044Z

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 25a1a7bd-3cf8-4b54-b584-714e61897bf0 · outbound

This paper cites Elliptic fourier features of a closed contour.

ShapeEmbed: a self-supervised learning framework for 2D contour quantification Elliptic fourier features of a closed contour

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-06T21:06:10.925033Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 277d351f-e5b7-4ebe-8cd2-a650432fd5d1 · outbound

This paper cites A simple framework for contrastive learning of visual representations.

ShapeEmbed: a self-supervised learning framework for 2D contour quantification A simple framework for contrastive learning of visual representations

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:06:13.440811Z

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 a69aadf9-e0e8-44ba-b658-4365e074466f · outbound

This paper cites Momentum contrast for unsupervised visual representation learning.

ShapeEmbed: a self-supervised learning framework for 2D contour quantification Momentum contrast for unsupervised visual representation learning

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-06T21:06:10.936790Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6633cf35-d6bf-4138-9335-38cd6bfe068a · outbound

This paper cites Emerging properties in self-supervised vision transformers.

ShapeEmbed: a self-supervised learning framework for 2D contour quantification Emerging properties in self-supervised vision transformers

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-06T21:06:10.941375Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation df4500fb-bb19-48a8-a051-24a61486d7d1 · outbound

This paper cites Masked autoencoders are scalable vision learners.

ShapeEmbed: a self-supervised learning framework for 2D contour quantification Masked autoencoders are scalable vision learners

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-06T21:06:10.945479Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0d80ff15-18f1-4e07-90b5-e8a9fa835be1 · outbound

This paper cites Jamieson, Erik S.

ShapeEmbed: a self-supervised learning framework for 2D contour quantification Jamieson, Erik S

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:06:13.229724Z

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 d6709183-0da5-4486-a76f-c018f1447956 · outbound

This paper cites Orientation-invariant autoencoders learn robust representations for shape profiling of cells and organelles.

ShapeEmbed: a self-supervised learning framework for 2D contour quantification Orientation-invariant autoencoders learn robust representations for shape profiling of cells and organelles

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-06T21:06:10.955291Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cbe77ea8-c2ab-46f7-af16-6584a842e5fd · outbound

This paper cites Invariant Shape Representation Learning For Image Classification.

ShapeEmbed: a self-supervised learning framework for 2D contour quantification Invariant Shape Representation Learning For Image Classification

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-06T21:06:10.960172Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 01394467-45f0-4df2-9ee4-325400763d44 · outbound

This paper cites A rotation-invariant framework for deep point cloud analysis.

ShapeEmbed: a self-supervised learning framework for 2D contour quantification A rotation-invariant framework for deep point cloud analysis

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:06:13.073651Z

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 e8fff341-3099-4a8d-b832-9712fd06fda8 · outbound

This paper cites Riconv++: Effective rotation invariant convolutions for 3d point clouds deep learning.

ShapeEmbed: a self-supervised learning framework for 2D contour quantification Riconv++: Effective rotation invariant convolutions for 3d point clouds deep learning

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:06:12.910805Z

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 2c3d9798-c0ad-450b-8873-98ad04451ecf · outbound

This paper cites A closer look at rotation-invariant deep point cloud analysis.

ShapeEmbed: a self-supervised learning framework for 2D contour quantification A closer look at rotation-invariant deep point cloud analysis

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:06:12.827015Z

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 dd9f5559-a921-4602-8515-b93e1ffc73d4 · outbound

This paper cites Ri-mae: Rotation-invariant masked autoencoders for self-supervised point cloud representation learning.

ShapeEmbed: a self-supervised learning framework for 2D contour quantification Ri-mae: Rotation-invariant masked autoencoders for self-supervised point cloud representation learning

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:06:12.719117Z

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 1c4f4b3f-293e-4d00-bbed-f47a1a13cfbd · outbound

This paper cites Self-supervised learning of rotation-invariant 3d point set features using transformer and its self-distillation.

ShapeEmbed: a self-supervised learning framework for 2D contour quantification Self-supervised learning of rotation-invariant 3d point set features using transformer and its self-distillation

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:06:12.591108Z

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 ed7f9c1c-d112-4b3c-b765-26a1fea4d06f · outbound

This paper cites General $E(2)$-Equivariant Steerable CNNs.

ShapeEmbed: a self-supervised learning framework for 2D contour quantification General $E(2)$-Equivariant Steerable CNNs

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-06T21:06:10.988603Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3de86166-088f-41f4-9bf9-1151437c00d6 · outbound

This paper cites A high resolution 3d surface construction algorithm.

ShapeEmbed: a self-supervised learning framework for 2D contour quantification A high resolution 3d surface construction algorithm

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:06:12.456242Z

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 f08a66f4-14d0-44dc-be7c-60ad35c670bc · outbound

This paper cites Representation learning: A review and new perspectives.

ShapeEmbed: a self-supervised learning framework for 2D contour quantification Representation learning: A review and new perspectives

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-06T21:06:10.998723Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5dc617e2-98cf-48ef-801d-1cc375b59997 · outbound

This paper cites Deep residual learning for image recognition.

ShapeEmbed: a self-supervised learning framework for 2D contour quantification Deep residual learning for image recognition

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:06:12.339248Z

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 ee80b71a-e69a-41e1-a2b7-339c8d25c808 · outbound

This paper cites How shift equivariance impacts metric learning for instance segmentation.

ShapeEmbed: a self-supervised learning framework for 2D contour quantification How shift equivariance impacts metric learning for instance segmentation

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:06:12.259632Z

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 f600df35-3372-476f-afa3-e7fe459e329e · outbound

This paper cites beta-vae: Learning basic visual concepts with a constrained variational framework.

ShapeEmbed: a self-supervised learning framework for 2D contour quantification beta-vae: Learning basic visual concepts with a constrained variational framework

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:06:12.126550Z

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 c0c7f850-be39-49f2-adcb-afe451fe4873 · outbound

This paper cites The mnist database of handwritten digit images for machine learning research.

ShapeEmbed: a self-supervised learning framework for 2D contour quantification The mnist database of handwritten digit images for machine learning research

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-06T21:06:11.020957Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1f86d1a5-5b03-45f9-9f8e-161bb7232572 · outbound

This paper cites B enchmarking image database for shape recognition techniques.

ShapeEmbed: a self-supervised learning framework for 2D contour quantification B enchmarking image database for shape recognition techniques

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:06:11.992489Z

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 0af029fe-fc9a-4cad-aa4d-5df1e44ccb34 · outbound

This paper cites Annotated high-throughput microscopy image sets for validation.

ShapeEmbed: a self-supervised learning framework for 2D contour quantification Annotated high-throughput microscopy image sets for validation

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-06T21:06:11.031112Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 571342ec-dd6c-46bd-9dbe-373d283bef6b · outbound

This paper cites Phillip, Kyu-Sang Han, Wei-Chiang Chen, Denis Wirtz, and Pei-Hsun Wu.

ShapeEmbed: a self-supervised learning framework for 2D contour quantification Phillip, Kyu-Sang Han, Wei-Chiang Chen, Denis Wirtz, and Pei-Hsun Wu

Reference 48

Resolution: verified exact; doi, observed 2026-08-06T21:06:11.166984Z

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 d79c5557-b7b9-49b9-b9c6-704ee04fe9e4 · outbound

This paper cites Logistic Regression, pages 243--250.

ShapeEmbed: a self-supervised learning framework for 2D contour quantification Logistic Regression, pages 243--250

Reference 49

Resolution: verified exact; doi, observed 2026-08-06T21:06:11.149945Z

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 95c393b5-e439-4824-86c6-5546119e83f5 · outbound

This paper cites Chai, Wee Sun Lee, and Hai Leong Chieu.

ShapeEmbed: a self-supervised learning framework for 2D contour quantification Chai, Wee Sun Lee, and Hai Leong Chieu

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:06:11.875393Z

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 49b29ab2-3f5c-44a5-b06a-ddb9188d1508 · outbound

This paper cites Shape distributions.

ShapeEmbed: a self-supervised learning framework for 2D contour quantification Shape distributions

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:06:11.784548Z

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 b3ccfba2-38a0-4ca4-a06d-6810adb724e8 · outbound

This paper cites Pytorch: An imperative style, high-performance deep learning library.

ShapeEmbed: a self-supervised learning framework for 2D contour quantification Pytorch: An imperative style, high-performance deep learning library

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:06:11.680609Z

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 e35a5d2b-0c2d-4476-ae1c-94157b69597c · outbound

This paper cites Adam: A Method for Stochastic Optimization.

ShapeEmbed: a self-supervised learning framework for 2D contour quantification Adam: A Method for Stochastic Optimization

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-06T21:06:11.063411Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 308dca8d-46da-41bd-bb98-f2526f2deb5d · outbound

This paper cites an unresolved cited work.

ShapeEmbed: a self-supervised learning framework for 2D contour quantification Unresolved cited work

Reference 54

Resolution: unresolved; raw_fallback, observed 2026-08-06T21:06:11.603974Z

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 e6c4f8df-a7f3-45d2-8cd1-12e6bd2cc252 · outbound

This paper cites Model assisted survey sampling.

ShapeEmbed: a self-supervised learning framework for 2D contour quantification Model assisted survey sampling

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:06:11.578464Z

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 848b919a-d32e-48b4-8a8f-2d144ebd60f1 · outbound

This paper cites an unresolved cited work.

ShapeEmbed: a self-supervised learning framework for 2D contour quantification Unresolved cited work

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-06T21:06:11.076268Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 61dac766-7a34-44b2-b4f0-f1bdefb6ec37 · outbound

This paper cites Visualizing data using t-SNE.

ShapeEmbed: a self-supervised learning framework for 2D contour quantification Visualizing data using t-SNE

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-06T21:06:11.080550Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ce751b7a-7f9e-492d-9a0a-80baf2334721 · outbound

This paper cites An image analysis toolbox for high-throughput c.

ShapeEmbed: a self-supervised learning framework for 2D contour quantification An image analysis toolbox for high-throughput c

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:06:11.538170Z

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 730a32a8-927c-4cc1-a93b-84a95e551da9 · outbound

This paper cites Smrt analysis of mtoc and nuclear positioning reveals the role of eb1 and lic1 in single-cell polarization.

ShapeEmbed: a self-supervised learning framework for 2D contour quantification Smrt analysis of mtoc and nuclear positioning reveals the role of eb1 and lic1 in single-cell polarization

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:06:11.512543Z

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 d1aa42ae-979b-4699-adbf-eec9b7196ed8 · outbound

This paper cites Open-source deep-learning software for bioimage segmentation.

ShapeEmbed: a self-supervised learning framework for 2D contour quantification Open-source deep-learning software for bioimage segmentation

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:06:11.481042Z

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 dfde5411-1a44-4a3f-8400-cbf49323a19c · outbound

This paper cites Cellpose: a generalist algorithm for cellular segmentation.

ShapeEmbed: a self-supervised learning framework for 2D contour quantification Cellpose: a generalist algorithm for cellular segmentation

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-06T21:06:11.098375Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0f9abc86-60df-4068-a734-2752c81eb92c · outbound

This paper cites Oliphant, Matt Haberland, Tyler Reddy, David Cournapeau, Evgeni Burovski, Pearu Peterson, Warren Weckesser, Jonathan Bright, St \'e fan J.

ShapeEmbed: a self-supervised learning framework for 2D contour quantification Oliphant, Matt Haberland, Tyler Reddy, David Cournapeau, Evgeni Burovski, Pearu Peterson, Warren Weckesser, Jonathan Bright, St \'e fan J

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-06T21:06:11.102994Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation 882456bb-9e65-4cf8-a307-12a77b7e2110 · inbound

Attention Mechanism in Randomized Time Warping cites this paper.

Attention Mechanism in Randomized Time Warping ShapeEmbed: a self-supervised learning framework for 2D contour quantification

Reference 33

Resolution: verified exact; local_arxiv, observed 2026-08-05T17:27:26.958900Z

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 5b1cb883-b629-4ad6-adf6-daf0ba9cc233 · inbound

The Push-Forward Transform for Continuous and Robust Comparison of Dynamic Shapes cites this paper.

The Push-Forward Transform for Continuous and Robust Comparison of Dynamic Shapes ShapeEmbed: a self-supervised learning framework for 2D contour quantification

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-04T09:40:31.717198Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.