Paper Citation Record · LEDGER

Data Sanity Check for Deep Learning Systems via Learnt Assertions

As of 23 August 2026, this Paper Citation Record lists 33 of 33 outbound references and 0 inbound Pith citation observations for arXiv:1909.03835.

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

pith.paper-citation-record.v1
1909.03835 v3

Coverage vector

measured 33 of 33 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-14T04:41:20.824751Z

measured 33 of 33 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+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

33 of 33 outbound references displayed

verified exact1
verified fuzzy27
unresolved5
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation adbf7af7-cbb6-436c-a237-40caf75af1a6 · outbound

This paper cites Real time road edges detection and road signs recognition,.

Data Sanity Check for Deep Learning Systems via Learnt Assertions Real time road edges detection and road signs recognition,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:41:24.399628Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 064213c1-1558-40f1-a836-2b55db14b14c · outbound

This paper cites Guest editorial deep learning in medical imaging: Overview and future promise of an exciting new technique,.

Data Sanity Check for Deep Learning Systems via Learnt Assertions Guest editorial deep learning in medical imaging: Overview and future promise of an exciting new technique,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:41:24.297061Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 8da45b8c-f8ac-441e-860f-a789c890276a · outbound

This paper cites Testing advanced driver assistance systems using multi-objective search and neural networks,.

Data Sanity Check for Deep Learning Systems via Learnt Assertions Testing advanced driver assistance systems using multi-objective search and neural networks,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:41:24.266319Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation b035224d-86b7-4487-b236-39f1c927f4df · outbound

This paper cites A domain strategy for computer program testing,.

Data Sanity Check for Deep Learning Systems via Learnt Assertions A domain strategy for computer program testing,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:41:24.191977Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 4646c20b-e436-4bb9-89f3-647f070f6764 · outbound

This paper cites Mitv: multiple- implementation testing of user-input validators for web applications,.

Data Sanity Check for Deep Learning Systems via Learnt Assertions Mitv: multiple- implementation testing of user-input validators for web applications,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:41:24.054754Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 50a6e03c-e3de-44e7-9eb4-46058626ad8c · outbound

This paper cites Perturbation-based user-input- validation testing of web applications,.

Data Sanity Check for Deep Learning Systems via Learnt Assertions Perturbation-based user-input- validation testing of web applications,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:41:23.914870Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 2f92fadf-29fc-4457-8a26-a1bf5437956f · outbound

This paper cites Semi-valid input coverage for fuzz testing,.

Data Sanity Check for Deep Learning Systems via Learnt Assertions Semi-valid input coverage for fuzz testing,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:41:23.774758Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 124edf24-fa57-46fd-8f3d-0c6f454c382c · outbound

This paper cites Bugs as deviant behavior: A general approach to inferring errors in systems code,.

Data Sanity Check for Deep Learning Systems via Learnt Assertions Bugs as deviant behavior: A general approach to inferring errors in systems code,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:41:23.620105Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 4a541ffe-caa8-401e-a8fd-6607f9ecc353 · outbound

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

Data Sanity Check for Deep Learning Systems via Learnt Assertions Reducing the dimensionality of data with neural networks,

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-14T04:41:20.134747Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c246abbc-76a1-4c6d-8702-1de23bfb0286 · outbound

This paper cites Detection of trafﬁc signs in real-world images: The German Trafﬁc Sign Detection Benchmark,.

Data Sanity Check for Deep Learning Systems via Learnt Assertions Detection of trafﬁc signs in real-world images: The German Trafﬁc Sign Detection Benchmark,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:41:23.390217Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation b2b9bfbc-3341-4a0f-8dda-7801b237ff44 · outbound

This paper cites Scalable triangulation-based logo recognition,.

Data Sanity Check for Deep Learning Systems via Learnt Assertions Scalable triangulation-based logo recognition,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:41:23.264838Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 4c85f95b-9d88-4647-b7c1-b6e171c0f1d7 · outbound

This paper cites Gradient-based learning applied to document recognition,.

Data Sanity Check for Deep Learning Systems via Learnt Assertions Gradient-based learning applied to document recognition,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:41:23.202966Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation ee9f17be-9cbf-430f-81e9-bf3b83d36ab4 · outbound

This paper cites Imagenet classiﬁcation with deep convolutional neural networks,.

Data Sanity Check for Deep Learning Systems via Learnt Assertions Imagenet classiﬁcation with deep convolutional neural networks,

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-14T04:41:20.293913Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 67c7ba28-5f3d-469b-a7e5-931bb00a4787 · outbound

This paper cites Very Deep Convolutional Networks for Large-Scale Image Recognition.

Data Sanity Check for Deep Learning Systems via Learnt Assertions Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-14T04:41:20.314741Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e437af4c-e761-444d-b440-9890f9c1794b · outbound

This paper cites Increased software reliability through input validation analysis and testing,.

Data Sanity Check for Deep Learning Systems via Learnt Assertions Increased software reliability through input validation analysis and testing,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:41:23.014746Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation e90a27bd-cb7b-4c4e-ba97-cd2f9edc66dd · outbound

This paper cites Security testing of web applications: A research plan,.

Data Sanity Check for Deep Learning Systems via Learnt Assertions Security testing of web applications: A research plan,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:41:22.903698Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation b305d7e2-48bc-48cc-a395-d9a217ac2c46 · outbound

This paper cites Semantic differential repair for input validation and sanitization,.

Data Sanity Check for Deep Learning Systems via Learnt Assertions Semantic differential repair for input validation and sanitization,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:41:22.797241Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 347f35d4-1c19-4300-9fe7-8b87edf26697 · outbound

This paper cites Using parse tree validation to prevent SQL injection attacks,.

Data Sanity Check for Deep Learning Systems via Learnt Assertions Using parse tree validation to prevent SQL injection attacks,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:41:22.664754Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 49a3dc7a-79a0-49d9-b772-10e43e2cff02 · outbound

This paper cites Saner: Composing static and dynamic anal- ysis to validate sanitization in web applications,.

Data Sanity Check for Deep Learning Systems via Learnt Assertions Saner: Composing static and dynamic anal- ysis to validate sanitization in web applications,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:41:22.545137Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 22e2156a-5f40-41a9-adf2-d8d126f0cef8 · outbound

This paper cites Predicting common web application vulnerabilities from input validation and sanitization code patterns,.

Data Sanity Check for Deep Learning Systems via Learnt Assertions Predicting common web application vulnerabilities from input validation and sanitization code patterns,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:41:22.444750Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation bb4011fb-8787-41d3-9d34-d27c3e6c5c37 · outbound

This paper cites Preventing input validation vulnerabilities in web applications through automated type analysis,.

Data Sanity Check for Deep Learning Systems via Learnt Assertions Preventing input validation vulnerabilities in web applications through automated type analysis,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:41:22.325299Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation ac0df6b7-0cc5-47e1-ab3d-dcb9872040fd · outbound

This paper cites Web application intrusion detection system for input validation attack,.

Data Sanity Check for Deep Learning Systems via Learnt Assertions Web application intrusion detection system for input validation attack,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:41:22.208745Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 94eb60ba-9df4-4b6a-a87c-524e3d26e1f2 · outbound

This paper cites Testing deep neural networks,.

Data Sanity Check for Deep Learning Systems via Learnt Assertions Testing deep neural networks,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:41:22.128158Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation b21be7fa-2d81-4088-bf46-abfd1c64529b · outbound

This paper cites TensorFuzz: Debugging Neural Networks with Coverage-Guided Fuzzing.

Data Sanity Check for Deep Learning Systems via Learnt Assertions TensorFuzz: Debugging Neural Networks with Coverage-Guided Fuzzing

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-14T04:41:20.556585Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7ccd2543-5e1f-4fbd-8d7b-ff351cf5151f · outbound

This paper cites MODE: automated neural network model debugging via state differential analysis and input selection,.

Data Sanity Check for Deep Learning Systems via Learnt Assertions MODE: automated neural network model debugging via state differential analysis and input selection,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:41:22.044738Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation c61cbfd3-e102-4e76-b507-aa8c44f8fe51 · outbound

This paper cites DeepSafe: A Data-driven Approach for Checking Adversarial Robustness in Neural Networks.

Data Sanity Check for Deep Learning Systems via Learnt Assertions DeepSafe: A Data-driven Approach for Checking Adversarial Robustness in Neural Networks

Reference 26

Resolution: verified exact; local_arxiv, observed 2026-08-14T04:41:21.024749Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 66817e8f-3d5a-46a9-9b5e-f5102a395bb0 · outbound

This paper cites Deepmutation: Mutation testing of deep learning systems,.

Data Sanity Check for Deep Learning Systems via Learnt Assertions Deepmutation: Mutation testing of deep learning systems,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:41:21.938453Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation b1a2db65-8826-4029-8ac6-269ed779dda5 · outbound

This paper cites Deepgauge: multi- granularity testing criteria for deep learning systems,.

Data Sanity Check for Deep Learning Systems via Learnt Assertions Deepgauge: multi- granularity testing criteria for deep learning systems,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:41:21.815322Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation dff4f841-7eb2-4cf8-864b-1961ebd6408c · outbound

This paper cites Concolic testing for deep neural networks,.

Data Sanity Check for Deep Learning Systems via Learnt Assertions Concolic testing for deep neural networks,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:41:21.703889Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation f54a864f-fe5a-4609-8de5-7554aab5e945 · outbound

This paper cites Deeproad: Gan-based metamorphic testing and input validation framework for autonomous driving systems,.

Data Sanity Check for Deep Learning Systems via Learnt Assertions Deeproad: Gan-based metamorphic testing and input validation framework for autonomous driving systems,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:41:21.606409Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation ee8b91cc-71d9-4d03-a3b7-6e0bda68175a · outbound

This paper cites Deepxplore: Automated whitebox testing of deep learning systems,.

Data Sanity Check for Deep Learning Systems via Learnt Assertions Deepxplore: Automated whitebox testing of deep learning systems,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:41:21.494745Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 1a40b245-79bd-4113-9107-57c87f3f6602 · outbound

This paper cites Deeptest: automated testing of deep-neural-network-driven autonomous cars,.

Data Sanity Check for Deep Learning Systems via Learnt Assertions Deeptest: automated testing of deep-neural-network-driven autonomous cars,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:41:21.403131Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation eb3e146f-69b7-4961-a01e-5c93c4577707 · outbound

This paper cites Testing Deep Neural Networks.

Data Sanity Check for Deep Learning Systems via Learnt Assertions Testing Deep Neural Networks

Reference 2018

Resolution: unresolved; no resolver link, observed 2026-08-14T04:41:20.522035Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.