Paper Citation Record · LEDGER

Flow Stochastic Segmentation Networks

As of 17 August 2026, this Paper Citation Record lists 30 of 30 outbound references and 0 inbound Pith citation observations for arXiv:2507.18838.

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

pith.paper-citation-record.v1
2507.18838 v1

Coverage vector

measured 30 of 30 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T14:37:42.885308Z

measured 30 of 30 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+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

30 of 30 outbound references displayed

verified exact0
verified fuzzy0
unresolved30
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 9c59532e-f0da-4376-b957-44b052ef7859 · outbound

This paper cites In12th USENIX symposium on operating systems design and implementation (OSDI 16), pages 265–283, 2016.

Flow Stochastic Segmentation Networks In12th USENIX symposium on operating systems design and implementation (OSDI 16), pages 265–283, 2016

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-06T14:37:42.805568Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a3ed2cf2-a4f0-4e94-9c2f-83c1f2cef389 · outbound

This paper cites Build- ing normalizing flows with stochastic interpolants.

Flow Stochastic Segmentation Networks Build- ing normalizing flows with stochastic interpolants

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-06T14:37:42.808825Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0def5bb3-046b-48bd-867c-9baabad299b7 · outbound

This paper cites SegDiff: Image Segmentation with Diffusion Probabilistic Models.

Flow Stochastic Segmentation Networks SegDiff: Image Segmentation with Diffusion Probabilistic Models

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-06T14:37:42.811737Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5287835b-0cde-48af-9240-1a28c44a3426 · outbound

This paper cites an unresolved cited work.

Flow Stochastic Segmentation Networks Unresolved cited work

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-06T14:37:42.814729Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ede425d0-ec34-45bb-889f-3370c7ff4123 · outbound

This paper cites Average calibration error: A differentiable loss for improved reliability in image seg- mentation.

Flow Stochastic Segmentation Networks Average calibration error: A differentiable loss for improved reliability in image seg- mentation

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-06T14:37:42.817628Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4c843344-4e3f-4231-8b7d-32d43617c868 · outbound

This paper cites Phiseg: Capturing uncertainty in medical image segmentation.

Flow Stochastic Segmentation Networks Phiseg: Capturing uncertainty in medical image segmentation

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-06T14:37:42.820571Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5eb7516b-1692-49f6-8dae-4a06b6eff8fc · outbound

This paper cites Failure detection in medical image classification: A reality check and benchmarking testbed.Transactions on Machine Learning Research, 2022.

Flow Stochastic Segmentation Networks Failure detection in medical image classification: A reality check and benchmarking testbed.Transactions on Machine Learning Research, 2022

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-06T14:37:42.823277Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4911aaab-c80b-4f63-a409-697d36f066c4 · outbound

This paper cites FlowSDF: Flow Matching for Medical Image Segmentation Using Distance Transforms.

Flow Stochastic Segmentation Networks FlowSDF: Flow Matching for Medical Image Segmentation Using Distance Transforms

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-06T14:37:42.826148Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7e591809-f6e6-4fb0-a22e-3c56178654ff · outbound

This paper cites QUBIQ: Uncertainty Quantification for Biomedical Image Segmentation Challenge.

Flow Stochastic Segmentation Networks QUBIQ: Uncertainty Quantification for Biomedical Image Segmentation Challenge

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-06T14:37:42.828974Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fd0b8c2f-2d1b-4a8b-afe1-3ad68649d6a5 · outbound

This paper cites TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation.

Flow Stochastic Segmentation Networks TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-06T14:37:42.831789Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 34b1cfda-b95a-4f3e-8517-182e37f8ec14 · outbound

This paper cites an unresolved cited work.

Flow Stochastic Segmentation Networks Unresolved cited work

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-06T14:37:42.834573Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 85e3ca67-c3ad-4da8-8028-e7c46554971b · outbound

This paper cites Neural ordinary differential equations.

Flow Stochastic Segmentation Networks Neural ordinary differential equations

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-06T14:37:42.837037Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d25a4959-27dc-4cbb-bea3-26d32019a47a · outbound

This paper cites an unresolved cited work.

Flow Stochastic Segmentation Networks Unresolved cited work

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-06T14:37:42.839484Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 82cc9d32-0d86-4356-bec4-b3ac02b4ba11 · outbound

This paper cites an unresolved cited work.

Flow Stochastic Segmentation Networks Unresolved cited work

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-06T14:37:42.841877Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bbf6b192-b653-49b0-9710-46c261467b0a · outbound

This paper cites Deep bayesian self-training.Neural Computing and Applications, 32(9):4275–4291, 2020.

Flow Stochastic Segmentation Networks Deep bayesian self-training.Neural Computing and Applications, 32(9):4275–4291, 2020

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-06T14:37:42.844360Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 06f64d13-5309-40d5-b6ae-4fbce711c1a0 · outbound

This paper cites Introducing routing uncertainty in capsule networks.

Flow Stochastic Segmentation Networks Introducing routing uncertainty in capsule networks

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-06T14:37:42.846770Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e719d55c-3f81-4556-b53b-fd10de8f1da1 · outbound

This paper cites High fidelity image counterfac- tuals with probabilistic causal models.

Flow Stochastic Segmentation Networks High fidelity image counterfac- tuals with probabilistic causal models

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-06T14:37:42.849655Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b68efa24-f6dd-4f45-9d5a-24dbd237e457 · outbound

This paper cites Aleatory or epis- temic? does it matter?Structural safety, 31(2):105–112,.

Flow Stochastic Segmentation Networks Aleatory or epis- temic? does it matter?Structural safety, 31(2):105–112,

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-06T14:37:42.852007Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f7c9c655-a91d-4127-936c-0e09d3b4c8aa · outbound

This paper cites Diffusion models beat gans on image synthesis.Advances in neural information processing systems, 34:8780–8794, 2021.

Flow Stochastic Segmentation Networks Diffusion models beat gans on image synthesis.Advances in neural information processing systems, 34:8780–8794, 2021

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-06T14:37:42.854735Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4b8f36e7-267f-4246-bb64-1ac7fcf63820 · outbound

This paper cites NICE: Non-linear Independent Components Estimation.

Flow Stochastic Segmentation Networks NICE: Non-linear Independent Components Estimation

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-06T14:37:42.857145Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e0d2426e-2f57-4048-9fef-ad24db0ca999 · outbound

This paper cites Den- sity estimation using real NVP.

Flow Stochastic Segmentation Networks Den- sity estimation using real NVP

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-06T14:37:42.859985Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f737643a-6428-4e3f-913d-f5ffcfe414d5 · outbound

This paper cites Naesseth, Max Welling, and Jan-Willem van de Meent.

Flow Stochastic Segmentation Networks Naesseth, Max Welling, and Jan-Willem van de Meent

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-06T14:37:42.862568Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fcdc4e9b-a811-44eb-9ce7-40011d28b83a · outbound

This paper cites REFUGE2 Challenge: A Treasure Trove for Multi-Dimension Analysis and Evaluation in Glaucoma Screening.

Flow Stochastic Segmentation Networks REFUGE2 Challenge: A Treasure Trove for Multi-Dimension Analysis and Evaluation in Glaucoma Screening

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-06T14:37:42.865186Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 303ed670-416c-4e13-ae93-dedf7780287d · outbound

This paper cites Modeling multimodal aleatoric uncertainty in segmentation with mixture of stochastic experts.International Conference on Representation Learning (ICLR), 2022.

Flow Stochastic Segmentation Networks Modeling multimodal aleatoric uncertainty in segmentation with mixture of stochastic experts.International Conference on Representation Learning (ICLR), 2022

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-06T14:37:42.868107Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6016b642-dac5-473b-92c7-c40aecf6cbbf · outbound

This paper cites Modeling multimodal aleatoric uncertainty in segmentation with mixture of stochastic experts.

Flow Stochastic Segmentation Networks Modeling multimodal aleatoric uncertainty in segmentation with mixture of stochastic experts

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-06T14:37:42.870772Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3b0e7dea-d517-4fd9-b40a-4fd8a79b5201 · outbound

This paper cites Made: Masked autoencoder for distribution es- timation.

Flow Stochastic Segmentation Networks Made: Masked autoencoder for distribution es- timation

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-06T14:37:42.873370Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3740e955-ee51-4bf4-ae83-a93fe2ea4a6d · outbound

This paper cites an unresolved cited work.

Flow Stochastic Segmentation Networks Unresolved cited work

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-06T14:37:42.876099Z

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Unavailable: canonical work link unavailable.

Observation 62a25d21-fcfa-4e06-ac9c-8207b094f8e8 · outbound

This paper cites STARFlow: Scaling Latent Normalizing Flows for High-resolution Image Synthesis.

Flow Stochastic Segmentation Networks STARFlow: Scaling Latent Normalizing Flows for High-resolution Image Synthesis

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-06T14:37:42.878579Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 15f8466d-cf62-4edb-a3c1-be324223788d · outbound

This paper cites Mimicking the one-dimensional marginal dis- tributions of processes having an itˆo differential.Probability theory and related fields, 71(4):501–516, 1986.

Flow Stochastic Segmentation Networks Mimicking the one-dimensional marginal dis- tributions of processes having an itˆo differential.Probability theory and related fields, 71(4):501–516, 1986

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-06T14:37:42.882013Z

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Unavailable: canonical work link unavailable.

Observation 3982fbd7-c1c1-4f53-9972-52b1806c9f8c · outbound

This paper cites Keeping the neural networks simple by minimizing the description length of the weights.

Flow Stochastic Segmentation Networks Keeping the neural networks simple by minimizing the description length of the weights

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-06T14:37:42.885308Z

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Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.