Paper Citation Record · LEDGER

Neural Architecture Distributions: A New Paradigm for Stochastic Segmentation

As of 17 August 2026, this Paper Citation Record lists 43 of 43 outbound references and 0 inbound Pith citation observations for arXiv:2606.21061.

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

pith.paper-citation-record.v1
2606.21061 v1

Coverage vector

measured 43 of 43 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-06-26T14:56:52.316735Z

measured 43 of 43 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+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

43 of 43 outbound references displayed

verified exact4
verified fuzzy0
unresolved39
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 44275007-80d7-4a68-9148-55a37345b773 · outbound

This paper cites an unresolved cited work.

Neural Architecture Distributions: A New Paradigm for Stochastic Segmentation Unresolved cited work

Reference 1

Resolution: unresolved; no resolver link, observed 2026-06-26T14:56:52.316735Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 65facaf2-cc89-4a17-a9dd-4250a272afd1 · outbound

This paper cites Stochastic segmentation with conditional categorical diffusion models.

Neural Architecture Distributions: A New Paradigm for Stochastic Segmentation Stochastic segmentation with conditional categorical diffusion models

Reference 2

Resolution: unresolved; no resolver link, observed 2026-06-26T14:56:52.316735Z

Source-reported events for the cited work

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Observation 89cb85a4-bf3d-4a19-a4e2-5c851711450a · outbound

This paper cites Stochastic segmentation networks: modelling spatially correlated aleatoric uncertainty.

Neural Architecture Distributions: A New Paradigm for Stochastic Segmentation Stochastic segmentation networks: modelling spatially correlated aleatoric uncertainty

Reference 3

Resolution: unresolved; no resolver link, observed 2026-06-26T14:56:52.316735Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a933ade6-76a9-48b1-98b3-1f3d4761ec8a · outbound

This paper cites U-net: Convolutional networks for biomedical image segmentation.

Neural Architecture Distributions: A New Paradigm for Stochastic Segmentation U-net: Convolutional networks for biomedical image segmentation

Reference 4

Resolution: unresolved; no resolver link, observed 2026-06-26T14:56:52.316735Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 074e4b48-633d-4840-864b-db0cfa1fbf2c · outbound

This paper cites Unet++: A nested u-net architecture for medical image segmentation.

Neural Architecture Distributions: A New Paradigm for Stochastic Segmentation Unet++: A nested u-net architecture for medical image segmentation

Reference 5

Resolution: unresolved; no resolver link, observed 2026-06-26T14:56:52.316735Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8d3b143a-fdfd-4886-be3c-96b1a5f0a6c7 · outbound

This paper cites Swin-unet: Unet-like pure transformer for medical image segmentation.

Neural Architecture Distributions: A New Paradigm for Stochastic Segmentation Swin-unet: Unet-like pure transformer for medical image segmentation

Reference 6

Resolution: unresolved; no resolver link, observed 2026-06-26T14:56:52.316735Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 665f1e2c-e4ac-44f4-a070-7d5faeaafb4b · outbound

This paper cites an unresolved cited work.

Neural Architecture Distributions: A New Paradigm for Stochastic Segmentation Unresolved cited work

Reference 7

Resolution: unresolved; no resolver link, observed 2026-06-26T14:56:52.316735Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 07d08230-89cb-464b-8317-f8fca2304e2e · outbound

This paper cites GMM-based V AE model with normalising flow for effective stochastic segmentation.

Neural Architecture Distributions: A New Paradigm for Stochastic Segmentation GMM-based V AE model with normalising flow for effective stochastic segmentation

Reference 8

Resolution: unresolved; no resolver link, observed 2026-06-26T14:56:52.316735Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 231f66ac-abf0-48a0-81a7-3e766ee1d69c · outbound

This paper cites Normalizing flows for probabilistic modeling and inference.J.

Neural Architecture Distributions: A New Paradigm for Stochastic Segmentation Normalizing flows for probabilistic modeling and inference.J

Reference 9

Resolution: unresolved; no resolver link, observed 2026-06-26T14:56:52.316735Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c7c101c6-8c73-495a-8cc1-bef050b6dd34 · outbound

This paper cites an unresolved cited work.

Neural Architecture Distributions: A New Paradigm for Stochastic Segmentation Unresolved cited work

Reference 10

Resolution: unresolved; no resolver link, observed 2026-06-26T14:56:52.316735Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0379e871-ecba-46b6-baba-3ca86df79245 · outbound

This paper cites Denoising diffusion probabilistic models.

Neural Architecture Distributions: A New Paradigm for Stochastic Segmentation Denoising diffusion probabilistic models

Reference 11

Resolution: unresolved; no resolver link, observed 2026-06-26T14:56:52.316735Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5247e74a-e22e-47d0-85c2-2715c1bc18de · outbound

This paper cites an unresolved cited work.

Neural Architecture Distributions: A New Paradigm for Stochastic Segmentation Unresolved cited work

Reference 12

Resolution: unresolved; no resolver link, observed 2026-06-26T14:56:52.316735Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation be5d8b03-54eb-4ace-8648-4db6a80cf67e · outbound

This paper cites Dropout as a bayesian approximation: Representing model uncertainty in deep learning.

Neural Architecture Distributions: A New Paradigm for Stochastic Segmentation Dropout as a bayesian approximation: Representing model uncertainty in deep learning

Reference 13

Resolution: unresolved; no resolver link, observed 2026-06-26T14:56:52.316735Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5736b6d2-00c7-4585-b325-dea579c4c58a · outbound

This paper cites Simple and scalable predictive uncertainty estimation using deep ensembles.

Neural Architecture Distributions: A New Paradigm for Stochastic Segmentation Simple and scalable predictive uncertainty estimation using deep ensembles

Reference 14

Resolution: unresolved; no resolver link, observed 2026-06-26T14:56:52.316735Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7400ff03-6bf9-4ef9-845e-46f88d0d4f54 · outbound

This paper cites Baumgartner, Kerem C.

Neural Architecture Distributions: A New Paradigm for Stochastic Segmentation Baumgartner, Kerem C

Reference 15

Resolution: unresolved; no resolver link, observed 2026-06-26T14:56:52.316735Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7929d7a8-4c1c-43b2-a38f-65ca6c056299 · outbound

This paper cites A Hierarchical Probabilistic U-Net for Modeling Multi-Scale Ambiguities.

Neural Architecture Distributions: A New Paradigm for Stochastic Segmentation A Hierarchical Probabilistic U-Net for Modeling Multi-Scale Ambiguities

Reference 16

Resolution: verified exact; local_arxiv, observed 2026-07-04T05:59:38.339860Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 9acf2d4d-9be1-4e53-8cd4-ebc5f858df77 · outbound

This paper cites Uncertainty quantification in medical image segmentation with normalizing flows.

Neural Architecture Distributions: A New Paradigm for Stochastic Segmentation Uncertainty quantification in medical image segmentation with normalizing flows

Reference 17

Resolution: unresolved; no resolver link, observed 2026-06-26T14:56:52.316735Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 41835249-c2f7-44b0-b56f-93b2a9f717b2 · outbound

This paper cites an unresolved cited work.

Neural Architecture Distributions: A New Paradigm for Stochastic Segmentation Unresolved cited work

Reference 18

Resolution: unresolved; no resolver link, observed 2026-06-26T14:56:52.316735Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 58699b95-6075-4757-8bb6-5a5501807928 · outbound

This paper cites Flow stochastic segmentation networks.

Neural Architecture Distributions: A New Paradigm for Stochastic Segmentation Flow stochastic segmentation networks

Reference 19

Resolution: unresolved; no resolver link, observed 2026-06-26T14:56:52.316735Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 16affae3-eac6-4182-b12e-39f72ae2ddff · outbound

This paper cites an unresolved cited work.

Neural Architecture Distributions: A New Paradigm for Stochastic Segmentation Unresolved cited work

Reference 20

Resolution: unresolved; no resolver link, observed 2026-06-26T14:56:52.316735Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bd7f0fa1-7261-4756-a14f-55f32062d71a · outbound

This paper cites an unresolved cited work.

Neural Architecture Distributions: A New Paradigm for Stochastic Segmentation Unresolved cited work

Reference 21

Resolution: unresolved; no resolver link, observed 2026-06-26T14:56:52.316735Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a35dd13e-2d95-42af-bdb7-6a38a2e3beb2 · outbound

This paper cites DARTS: Differentiable architecture search.

Neural Architecture Distributions: A New Paradigm for Stochastic Segmentation DARTS: Differentiable architecture search

Reference 22

Resolution: unresolved; no resolver link, observed 2026-06-26T14:56:52.316735Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3fadf598-9748-4b45-b4b6-d7dabadd1532 · outbound

This paper cites SNAS: stochastic neural architecture search.

Neural Architecture Distributions: A New Paradigm for Stochastic Segmentation SNAS: stochastic neural architecture search

Reference 23

Resolution: unresolved; no resolver link, observed 2026-06-26T14:56:52.316735Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation eca2a22d-e27f-4f55-ab5e-620263a72dea · outbound

This paper cites Efficient neural architecture search via parameters sharing.

Neural Architecture Distributions: A New Paradigm for Stochastic Segmentation Efficient neural architecture search via parameters sharing

Reference 24

Resolution: unresolved; no resolver link, observed 2026-06-26T14:56:52.316735Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 709ef135-bab1-40af-a82a-13f75be64ff3 · outbound

This paper cites Once for all: Train one network and specialize it for efficient deployment.

Neural Architecture Distributions: A New Paradigm for Stochastic Segmentation Once for all: Train one network and specialize it for efficient deployment

Reference 25

Resolution: unresolved; no resolver link, observed 2026-06-26T14:56:52.316735Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3962c157-2c32-4f04-bd46-ddd924d91814 · outbound

This paper cites Categorical reparameterization with gumbel-softmax.

Neural Architecture Distributions: A New Paradigm for Stochastic Segmentation Categorical reparameterization with gumbel-softmax

Reference 26

Resolution: unresolved; no resolver link, observed 2026-06-26T14:56:52.316735Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1698950b-3d88-4684-b1b4-cf58e6905d25 · outbound

This paper cites The cityscapes dataset for semantic urban scene understanding.

Neural Architecture Distributions: A New Paradigm for Stochastic Segmentation The cityscapes dataset for semantic urban scene understanding

Reference 27

Resolution: unresolved; no resolver link, observed 2026-06-26T14:56:52.316735Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ce6e49b5-617d-4ba0-a58b-d553d4323426 · outbound

This paper cites Road crack detection using deep convolutional neural network.

Neural Architecture Distributions: A New Paradigm for Stochastic Segmentation Road crack detection using deep convolutional neural network

Reference 28

Resolution: unresolved; no resolver link, observed 2026-06-26T14:56:52.316735Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d8aad082-ad59-479a-95bd-5ec4bffe4abc · outbound

This paper cites Mind marginal non-crack regions: Clustering- inspired representation learning for crack segmentation.

Neural Architecture Distributions: A New Paradigm for Stochastic Segmentation Mind marginal non-crack regions: Clustering- inspired representation learning for crack segmentation

Reference 29

Resolution: unresolved; no resolver link, observed 2026-06-26T14:56:52.316735Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f0f4cbf5-4d12-4c6e-80f6-79825192481f · outbound

This paper cites Calibrated adversarial refinement for stochastic semantic segmentation.

Neural Architecture Distributions: A New Paradigm for Stochastic Segmentation Calibrated adversarial refinement for stochastic semantic segmentation

Reference 30

Resolution: unresolved; no resolver link, observed 2026-06-26T14:56:52.316735Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7e1daede-9d15-4df0-969f-4fbd2940fdbe · outbound

This paper cites A probabilistic model for controlling diversity and accuracy of ambiguous medical image segmentation.

Neural Architecture Distributions: A New Paradigm for Stochastic Segmentation A probabilistic model for controlling diversity and accuracy of ambiguous medical image segmentation

Reference 31

Resolution: unresolved; no resolver link, observed 2026-06-26T14:56:52.316735Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 22804556-9a14-4608-8aa2-db8db9f7fe4d · outbound

This paper cites Pixelseg: Pixel-by-pixel stochastic semantic segmentation for ambiguous medical images.

Neural Architecture Distributions: A New Paradigm for Stochastic Segmentation Pixelseg: Pixel-by-pixel stochastic semantic segmentation for ambiguous medical images

Reference 32

Resolution: unresolved; no resolver link, observed 2026-06-26T14:56:52.316735Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 53e6a70d-a633-4954-bbd5-1d349ca4c2fb · outbound

This paper cites Modeling Multimodal Aleatoric Uncertainty in Segmentation with Mixture of Stochastic Experts.

Neural Architecture Distributions: A New Paradigm for Stochastic Segmentation Modeling Multimodal Aleatoric Uncertainty in Segmentation with Mixture of Stochastic Experts

Reference 33

Resolution: verified exact; arxiv_id, observed 2026-07-04T05:59:38.344107Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 83133e6f-d6ef-4f69-87a4-222e6cc6bf80 · outbound

This paper cites Analog Bits: Generating Discrete Data using Diffusion Models with Self-Conditioning.

Neural Architecture Distributions: A New Paradigm for Stochastic Segmentation Analog Bits: Generating Discrete Data using Diffusion Models with Self-Conditioning

Reference 34

Resolution: verified exact; arxiv_id, observed 2026-07-04T05:59:38.353919Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation dbf5dc19-4ec6-408b-972b-71ca4d8ae01e · outbound

This paper cites Rethinking Atrous Convolution for Semantic Image Segmentation.

Neural Architecture Distributions: A New Paradigm for Stochastic Segmentation Rethinking Atrous Convolution for Semantic Image Segmentation

Reference 35

Resolution: verified exact; local_arxiv, observed 2026-07-04T05:59:38.349156Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 87ed8b33-37ad-412c-b240-b9acf4f83543 · outbound

This paper cites Unified perceptual parsing for scene understanding.

Neural Architecture Distributions: A New Paradigm for Stochastic Segmentation Unified perceptual parsing for scene understanding

Reference 36

Resolution: unresolved; no resolver link, observed 2026-06-26T14:56:52.316735Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 93d3a3b7-30f7-4bfc-906d-fe4583414e65 · outbound

This paper cites Deep high-resolution representation learning for visual recognition.IEEE transactions on pattern analysis and machine intelligence, 43(10):3349–3364, 2020.

Neural Architecture Distributions: A New Paradigm for Stochastic Segmentation Deep high-resolution representation learning for visual recognition.IEEE transactions on pattern analysis and machine intelligence, 43(10):3349–3364, 2020

Reference 37

Resolution: unresolved; no resolver link, observed 2026-06-26T14:56:52.316735Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a2f9e9a0-a7a9-4f2a-ad68-c6af294c33c9 · outbound

This paper cites Swin transformer: Hierarchical vision transformer using shifted windows.

Neural Architecture Distributions: A New Paradigm for Stochastic Segmentation Swin transformer: Hierarchical vision transformer using shifted windows

Reference 38

Resolution: unresolved; no resolver link, observed 2026-06-26T14:56:52.316735Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation da01293f-22be-4307-8f80-211effe4061c · outbound

This paper cites Beyond the pixel-wise loss for topology-aware delineation.

Neural Architecture Distributions: A New Paradigm for Stochastic Segmentation Beyond the pixel-wise loss for topology-aware delineation

Reference 39

Resolution: unresolved; no resolver link, observed 2026-06-26T14:56:52.316735Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1751cc52-aa84-4cea-a99c-cf50ce256977 · outbound

This paper cites Topology-preserving deep image segmentation.

Neural Architecture Distributions: A New Paradigm for Stochastic Segmentation Topology-preserving deep image segmentation

Reference 40

Resolution: unresolved; no resolver link, observed 2026-06-26T14:56:52.316735Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b62f66a7-f376-4d11-a773-096ca83b59ed · outbound

This paper cites Recurrent u-net for resource- constrained segmentation.

Neural Architecture Distributions: A New Paradigm for Stochastic Segmentation Recurrent u-net for resource- constrained segmentation

Reference 41

Resolution: unresolved; no resolver link, observed 2026-06-26T14:56:52.316735Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e82cccc3-48ec-49b9-a102-a7556bed089c · outbound

This paper cites Crackformer: Transformer network for fine-grained crack detection.

Neural Architecture Distributions: A New Paradigm for Stochastic Segmentation Crackformer: Transformer network for fine-grained crack detection

Reference 42

Resolution: unresolved; no resolver link, observed 2026-06-26T14:56:52.316735Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 88218766-2cf7-4b7c-bc98-df3c26f51991 · outbound

This paper cites Joint topology-preserving and feature- refinement network for curvilinear structure segmentation.

Neural Architecture Distributions: A New Paradigm for Stochastic Segmentation Joint topology-preserving and feature- refinement network for curvilinear structure segmentation

Reference 43

Resolution: unresolved; no resolver link, observed 2026-06-26T14:56:52.316735Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.