Paper Citation Record · LEDGER

Gradient-based Explanations for Deep Learning Survival Models

As of 11 August 2026, this Paper Citation Record lists 42 of 42 outbound references and 1 inbound Pith citation observation for arXiv:2502.04970.

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

pith.paper-citation-record.v1
2502.04970 v1

Coverage vector

measured 42 of 42 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-08T20:51:47.618470Z

measured 43 of 43 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 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T23:37:01.343477Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-07T23:37:01.532664Z

Reference resolution

42 of 42 outbound references displayed

verified exact7
verified fuzzy9
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation ddf67e9d-24db-4d44-98b3-aa00eab28881 · outbound

This paper cites write newline.

Gradient-based Explanations for Deep Learning Survival Models write newline

Reference 1

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Observation 67d7fd89-6415-417e-a898-152416480dc2 · outbound

This paper cites Gradient-based attribution methods.

Gradient-based Explanations for Deep Learning Survival Models Gradient-based attribution methods

Reference 2

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Observation 04ab6f2a-5e03-416d-a566-30b2d6e785ea · outbound

This paper cites M., Du, Y., Guendouz, Y., Wei, L., Mazo, C., Becker, B.

Gradient-based Explanations for Deep Learning Survival Models M., Du, Y., Guendouz, Y., Wei, L., Mazo, C., Becker, B

Reference 3

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Observation 182bbe66-ef19-4b5f-bed4-7ed0ed0eb073 · outbound

This paper cites Generating survival times to simulate cox proportional hazards models.

Gradient-based Explanations for Deep Learning Survival Models Generating survival times to simulate cox proportional hazards models

Reference 4

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Observation 8bf642ea-0052-41f0-94c6-17608aca0938 · outbound

This paper cites L., Wolfe, R., Moreno-Betancur, M., and Crowther, M.

Gradient-based Explanations for Deep Learning Survival Models L., Wolfe, R., Moreno-Betancur, M., and Crowther, M

Reference 5

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Observation 9a021dfa-b143-419c-b7bf-d55658f1ae23 · outbound

This paper cites C., Lundberg, S.

Gradient-based Explanations for Deep Learning Survival Models C., Lundberg, S

Reference 6

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Observation 0700c0b4-d2e6-4bfa-ad25-19b1745ddff6 · outbound

This paper cites an unresolved cited work.

Gradient-based Explanations for Deep Learning Survival Models Unresolved cited work

Reference 7

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Observation ab224b68-086b-40f1-85e7-324401561f4a · outbound

This paper cites J., Shu, M., Bekiranov, S., Zang, C., and Zhang, A.

Gradient-based Explanations for Deep Learning Survival Models J., Shu, M., Bekiranov, S., Zang, C., and Zhang, A

Reference 8

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Gradient-based Explanations for Deep Learning Survival Models Unresolved cited work

Reference 9

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Gradient-based Explanations for Deep Learning Survival Models Unresolved cited work

Reference 10

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Observation eea0afca-d65d-4da3-b069-700a11ae2a0b · outbound

This paper cites D., Sturmfels, P., Lundberg, S.

Gradient-based Explanations for Deep Learning Survival Models D., Sturmfels, P., Lundberg, S

Reference 11

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This paper cites C., Gromicho, M., de Carvalho, M., Vinga, S., and Carvalho, A.

Gradient-based Explanations for Deep Learning Survival Models C., Gromicho, M., de Carvalho, M., Vinga, S., and Carvalho, A

Reference 12

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Observation 0623967e-5e2a-4bfe-aac7-42bf1f0d9e0c · outbound

This paper cites H., and Kang, M.

Gradient-based Explanations for Deep Learning Survival Models H., and Kang, M

Reference 13

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This paper cites C., Tsaku, N.

Gradient-based Explanations for Deep Learning Survival Models C., Tsaku, N

Reference 14

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Observation ac5a50fa-2f03-4246-b6b7-c3d7193f1d96 · outbound

This paper cites Deep residual learning for image recognition.

Gradient-based Explanations for Deep Learning Survival Models Deep residual learning for image recognition

Reference 15

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Observation 77a72ceb-313f-4b9a-ba56-982b8904f509 · outbound

This paper cites Fast axiomatic attribution for neural networks.

Gradient-based Explanations for Deep Learning Survival Models Fast axiomatic attribution for neural networks

Reference 16

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Observation 7d06c4de-82b1-45a0-b41b-02571a6386b7 · outbound

This paper cites L., Shaham, U., Cloninger, A., Bates, J., Jiang, T., and Kluger, Y.

Gradient-based Explanations for Deep Learning Survival Models L., Shaham, U., Cloninger, A., Bates, J., Jiang, T., and Kluger, Y

Reference 17

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Observation 66d39622-d038-4eef-aa03-badab75b38f4 · outbound

This paper cites and Wright, M.

Gradient-based Explanations for Deep Learning Survival Models and Wright, M

Reference 18

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Observation 8c221599-6cd7-4c08-84e1-5e2ebcf1f536 · outbound

This paper cites and Wright, M.

Gradient-based Explanations for Deep Learning Survival Models and Wright, M

Reference 19

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Observation 47891c0b-8603-4839-8933-11eb99603efd · outbound

This paper cites S., Utkin, L.

Gradient-based Explanations for Deep Learning Survival Models S., Utkin, L

Reference 20

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Observation 5381b706-76cd-4fe2-92a8-78b196a623f7 · outbound

This paper cites SurvSHAP (t) : Time-dependent explanations of machine learning survival models.

Gradient-based Explanations for Deep Learning Survival Models SurvSHAP (t) : Time-dependent explanations of machine learning survival models

Reference 21

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Observation 96d19b10-3749-435d-b602-c7b6f2dc369f · outbound

This paper cites Time-to-event prediction with neural networks and Cox regression.

Gradient-based Explanations for Deep Learning Survival Models Time-to-event prediction with neural networks and Cox regression

Reference 22

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This paper cites H., Krzyziński, M., Spytek, M., Baniecki, H., Biecek, P., and Wright, M.

Gradient-based Explanations for Deep Learning Survival Models H., Krzyziński, M., Spytek, M., Baniecki, H., Biecek, P., and Wright, M

Reference 23

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Observation 7a8cd9d5-3192-418a-a86d-03891704be59 · outbound

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Gradient-based Explanations for Deep Learning Survival Models Unresolved cited work

Reference 24

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This paper cites Synthetic benchmarks for scientific research in explainable machine learning.

Gradient-based Explanations for Deep Learning Survival Models Synthetic benchmarks for scientific research in explainable machine learning

Reference 25

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Gradient-based Explanations for Deep Learning Survival Models Unresolved cited work

Reference 26

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This paper cites A., Barnholtz-Sloan, J.

Gradient-based Explanations for Deep Learning Survival Models A., Barnholtz-Sloan, J

Reference 27

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This paper cites Explaining nonlinear classification decisions with deep taylor decomposition.

Gradient-based Explanations for Deep Learning Survival Models Explaining nonlinear classification decisions with deep taylor decomposition

Reference 28

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This paper cites W., Vollmuth, P., Foltyn-Dumitru, M., Sahm, F., Ahn, S.

Gradient-based Explanations for Deep Learning Survival Models W., Vollmuth, P., Foltyn-Dumitru, M., Sahm, F., Ahn, S

Reference 29

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

Gradient-based Explanations for Deep Learning Survival Models Pytorch: An imperative style, high-performance deep learning library

Reference 30

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Gradient-based Explanations for Deep Learning Survival Models Unresolved cited work

Reference 31

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This paper cites why should i trust you?.

Gradient-based Explanations for Deep Learning Survival Models why should i trust you?

Reference 32

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This paper cites Not Just a Black Box: Learning Important Features Through Propagating Activation Differences.

Gradient-based Explanations for Deep Learning Survival Models Not Just a Black Box: Learning Important Features Through Propagating Activation Differences

Reference 33

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Gradient-based Explanations for Deep Learning Survival Models Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps

Reference 34

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Gradient-based Explanations for Deep Learning Survival Models SmoothGrad: removing noise by adding noise

Reference 35

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This paper cites Axiomatic attribution for deep networks.

Gradient-based Explanations for Deep Learning Survival Models Axiomatic attribution for deep networks

Reference 36

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Observation 952e077f-5c00-401e-85b0-7997cc6c0eaa · outbound

This paper cites Capsurv: Capsule network for survival analysis with whole slide pathological images.

Gradient-based Explanations for Deep Learning Survival Models Capsurv: Capsule network for survival analysis with whole slide pathological images

Reference 37

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This paper cites The importance of interpretability and visualization in machine learning for applications in medicine and health care.

Gradient-based Explanations for Deep Learning Survival Models The importance of interpretability and visualization in machine learning for applications in medicine and health care

Reference 38

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Observation b546d794-25c9-4b12-9937-65b1a0992bdb · outbound

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Gradient-based Explanations for Deep Learning Survival Models and Longo, L

Reference 39

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This paper cites Deep learning for survival analysis: A review.

Gradient-based Explanations for Deep Learning Survival Models Deep learning for survival analysis: A review

Reference 40

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Gradient-based Explanations for Deep Learning Survival Models Unresolved cited work

Reference 41

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Observation 5a8fda77-7e2d-45af-a7c3-1bba586bc5b1 · outbound

This paper cites Deep convolutional neural network for survival analysis with pathological images.

Gradient-based Explanations for Deep Learning Survival Models Deep convolutional neural network for survival analysis with pathological images

Reference 42

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Pith citing papers

Observation aba82160-9121-4974-a925-45a1ce2a5618 · inbound

DejAIvu: Identifying and Explaining AI Art on the Web in Real-Time with Saliency Maps cites this paper.

DejAIvu: Identifying and Explaining AI Art on the Web in Real-Time with Saliency Maps Gradient-based Explanations for Deep Learning Survival Models

Reference 12

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