Safety Monitoring of Machine Learning Perception Functions: a Survey | Paper Citation Record

Observation 6c20bfa8-5f98-410e-ac62-4d0c6a22615f · outbound

This paper cites Runtime Monitoring of Cyber-Physical Systems Using Data-driven Models.

Safety Monitoring of Machine Learning Perception Functions: a Survey Runtime Monitoring of Cyber-Physical Systems Using Data-driven Models

Reference 1

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Observation 551fd5a5-9306-44a0-8122-4f7004bfcb49 · outbound

This paper cites Autonomy for surgical robots: Concepts and paradigms.IEEE Transactions on Medical Robotics and Bionics.2019;1(2):65–76.

Safety Monitoring of Machine Learning Perception Functions: a Survey Autonomy for surgical robots: Concepts and paradigms.IEEE Transactions on Medical Robotics and Bionics.2019;1(2):65–76

Reference 2

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Observation 9e9d26c0-337e-4fb5-ae5f-6355e66d1c8d · outbound

This paper cites Certifying Emergency Landing for Safe Urban UA V.

Safety Monitoring of Machine Learning Perception Functions: a Survey Certifying Emergency Landing for Safe Urban UA V

Reference 3

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Observation 6e4faf36-4a19-4610-a36e-cd736811920e · outbound

This paper cites Intelligent Robotic Perception Systems.

Safety Monitoring of Machine Learning Perception Functions: a Survey Intelligent Robotic Perception Systems

Reference 4

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Observation bb00915f-cad5-40e2-bc31-c4fbc88780c2 · outbound

This paper cites Benchmarking deep reinforcement learning for continuous control.

Safety Monitoring of Machine Learning Perception Functions: a Survey Benchmarking deep reinforcement learning for continuous control

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Observation f45de080-cc07-497b-8252-2051d0fe34e3 · outbound

This paper cites Computer vision and deep learning techniques for pedestrian detection and tracking: A survey.

Safety Monitoring of Machine Learning Perception Functions: a Survey Computer vision and deep learning techniques for pedestrian detection and tracking: A survey

Reference 6

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Observation 4857e434-6968-46b3-8534-a2d2c9e94b90 · outbound

This paper cites Evaluation of runtime monitoring for UA V emergency landing.

Safety Monitoring of Machine Learning Perception Functions: a Survey Evaluation of runtime monitoring for UA V emergency landing

Reference 7

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Observation 9e9538cc-455c-447c-aeb7-ac2e4fda5e04 · outbound

This paper cites On the safety of machine learning: Cyber-physical systems, decision sciences, and data products.

Safety Monitoring of Machine Learning Perception Functions: a Survey On the safety of machine learning: Cyber-physical systems, decision sciences, and data products

Reference 8

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Observation f3a25848-f38b-4fba-91bd-941339dd7ba9 · outbound

This paper cites Machine learning safety: An overview.

Safety Monitoring of Machine Learning Perception Functions: a Survey Machine learning safety: An overview

Reference 9

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Observation 1c0998f6-0dfc-4f3c-991e-53fb78d9d083 · outbound

This paper cites Practical solutions for machine learning safety in autonomous vehicles.

Safety Monitoring of Machine Learning Perception Functions: a Survey Practical solutions for machine learning safety in autonomous vehicles

Reference 10

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Observation 595f837b-7d92-479d-bd87-2f6623f3955a · outbound

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

Safety Monitoring of Machine Learning Perception Functions: a Survey Dropout as a bayesian approximation: Representing model uncertainty in deep learning

Reference 11

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Observation 66803d0f-3e4a-4a4d-a2f6-8baf1315cf09 · outbound

This paper cites Basic concepts and taxonomy of dependable and secure computing.

Safety Monitoring of Machine Learning Perception Functions: a Survey Basic concepts and taxonomy of dependable and secure computing

Reference 12

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Observation dc54fec1-7c01-4921-9881-7f353c036cb4 · outbound

This paper cites SMOF: A safety monitoring framework for autonomous systems.

Safety Monitoring of Machine Learning Perception Functions: a Survey SMOF: A safety monitoring framework for autonomous systems

Reference 13

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Observation 62f24953-4072-4400-bf29-7b328c42823c · outbound

This paper cites Kernels for safety.

Safety Monitoring of Machine Learning Perception Functions: a Survey Kernels for safety

Reference 14

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Observation cf8a6288-3b5f-4fc9-a94c-26b1b5abbf05 · outbound

This paper cites A safety integrated architecture for an autonomous safety excavator.

Safety Monitoring of Machine Learning Perception Functions: a Survey A safety integrated architecture for an autonomous safety excavator

Reference 15

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Observation 794cb0f8-fa8d-4472-945d-d0eb29a52df5 · outbound

This paper cites The ranger robotic satellite servicer and its autonomous software-based safety system.

Safety Monitoring of Machine Learning Perception Functions: a Survey The ranger robotic satellite servicer and its autonomous software-based safety system

Reference 16

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Observation 1b494d2e-0711-4292-b1d1-a12e20434e53 · outbound

This paper cites Dependable execution control for autonomous robots.

Safety Monitoring of Machine Learning Perception Functions: a Survey Dependable execution control for autonomous robots

Reference 17

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Observation 57d47c86-84c9-4327-9857-3f899648eff8 · outbound

This paper cites Safe and sound.

Safety Monitoring of Machine Learning Perception Functions: a Survey Safe and sound

Reference 18

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Observation 7471b346-1a12-4689-afdf-1eb011c7d9bc · outbound

This paper cites The safety-bag expert system in the electronic railway interlocking system Elektra.

Safety Monitoring of Machine Learning Perception Functions: a Survey The safety-bag expert system in the electronic railway interlocking system Elektra

Reference 19

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Observation 69c14eb2-19e9-485d-a67a-211f02f724ed · outbound

This paper cites Towards the robotic co-worker.

Safety Monitoring of Machine Learning Perception Functions: a Survey Towards the robotic co-worker

Reference 20

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Observation dc3b9a14-6e8a-4da4-b34a-94b347aa75f9 · outbound

This paper cites Safety monitoring for autonomous systems: interactive elicitation of safety rules.

Safety Monitoring of Machine Learning Perception Functions: a Survey Safety monitoring for autonomous systems: interactive elicitation of safety rules

Reference 21

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Observation 1dc3d4e3-d9b0-447b-88ec-7a1cea8bab66 · outbound

This paper cites Machine learning for reliability engineering and safety applications: Review of current status and future opportunities.

Safety Monitoring of Machine Learning Perception Functions: a Survey Machine learning for reliability engineering and safety applications: Review of current status and future opportunities

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Observation 26a8f4f6-7ba8-424d-ad91-06360aecb0e3 · outbound

This paper cites Deep learning for safe autonomous driving: Current challenges and future directions.

Safety Monitoring of Machine Learning Perception Functions: a Survey Deep learning for safe autonomous driving: Current challenges and future directions

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Observation 131a9af4-0b3c-4e12-98f8-30d5834b2fe5 · outbound

This paper cites Deep learning-based applications for safety management in the AEC industry: A review.

Safety Monitoring of Machine Learning Perception Functions: a Survey Deep learning-based applications for safety management in the AEC industry: A review

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Observation 67d1c5f5-87f5-42ad-bde0-c4f5cf8d8bd0 · outbound

This paper cites Sensing and machine learning for automotive perception: A review.

Safety Monitoring of Machine Learning Perception Functions: a Survey Sensing and machine learning for automotive perception: A review

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Observation 23635b71-e881-4d86-83ff-0af6a3289529 · outbound

This paper cites Taxonomy of machine learning safety: A survey and primer.

Safety Monitoring of Machine Learning Perception Functions: a Survey Taxonomy of machine learning safety: A survey and primer

Reference 26

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Observation 7c37539f-a9b2-4e0f-b9c6-2dc949fa31f7 · outbound

This paper cites Run-Time Monitoring of Machine Learning for Robotic Perception: A Survey of Emerging Trends.

Safety Monitoring of Machine Learning Perception Functions: a Survey Run-Time Monitoring of Machine Learning for Robotic Perception: A Survey of Emerging Trends

Reference 27

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Observation 9d2dc608-3e97-4051-86eb-fc4376d4aa03 · outbound

This paper cites A survey on learning to reject.Proceedings of the IEEE.

Safety Monitoring of Machine Learning Perception Functions: a Survey A survey on learning to reject.Proceedings of the IEEE

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Observation 4f370ac0-bb80-4788-8ebe-eece96cd2a35 · outbound

This paper cites Out-Of-Distribution Detection Is Not All You Need.

Safety Monitoring of Machine Learning Perception Functions: a Survey Out-Of-Distribution Detection Is Not All You Need

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Observation 7b5f1822-765c-44b0-8c73-53e2115c07eb · outbound

This paper cites Review of data preprocessing techniques in data mining.

Safety Monitoring of Machine Learning Perception Functions: a Survey Review of data preprocessing techniques in data mining

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Observation 9ffbf0f5-7627-4c7d-8834-913fc6ca48c6 · outbound

This paper cites Training deep neural-networks based on unreliable labels.

Safety Monitoring of Machine Learning Perception Functions: a Survey Training deep neural-networks based on unreliable labels

Reference 31

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Observation 650aa1c4-77b5-49a1-bae6-59facb11415c · outbound

This paper cites The ml test score: A rubric for ml production readiness and technical debt reduction.

Safety Monitoring of Machine Learning Perception Functions: a Survey The ml test score: A rubric for ml production readiness and technical debt reduction

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Observation 2da64034-4e01-402f-a5e5-4e942a30368e · outbound

This paper cites Hidden technical debt in machine learning systems.

Safety Monitoring of Machine Learning Perception Functions: a Survey Hidden technical debt in machine learning systems

Reference 33

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Observation 3a18fcd9-d185-436c-8a01-aed6f988a196 · outbound

This paper cites A review of novelty detection.

Safety Monitoring of Machine Learning Perception Functions: a Survey A review of novelty detection

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Observation 70ab905f-f3eb-4514-8666-d6937f405135 · outbound

This paper cites Adversarial Attacks and Defences: A Survey.

Safety Monitoring of Machine Learning Perception Functions: a Survey Adversarial Attacks and Defences: A Survey

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Observation 0a807e5a-aab8-42dd-8e55-c01f95ae3b15 · outbound

This paper cites DOCTOR: A Simple Method for Detecting Misclassification Errors.

Safety Monitoring of Machine Learning Perception Functions: a Survey DOCTOR: A Simple Method for Detecting Misclassification Errors

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Observation 11097804-e632-4994-befe-6895fe3fe643 · outbound

This paper cites Benchmarking Safety Monitors for Image Classifiers with Machine Learning.

Safety Monitoring of Machine Learning Perception Functions: a Survey Benchmarking Safety Monitors for Image Classifiers with Machine Learning

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Observation 96f4b0c0-6c15-4b99-a9d1-3e7d7332f824 · outbound

This paper cites Towards Out-Of-Distribution Generalization: A Survey.

Safety Monitoring of Machine Learning Perception Functions: a Survey Towards Out-Of-Distribution Generalization: A Survey

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Observation ba974b53-f0ae-4c97-be73-487eb2aa3511 · outbound

This paper cites SiMOOD: Evolutionary Testing Simulation with Out-Of-Distribution Images.

Safety Monitoring of Machine Learning Perception Functions: a Survey SiMOOD: Evolutionary Testing Simulation with Out-Of-Distribution Images

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Observation e0f7a5ae-4257-4e9c-8987-2ca4586a1964 · outbound

This paper cites The Cityscapes Dataset for Semantic Urban Scene Understanding.

Safety Monitoring of Machine Learning Perception Functions: a Survey The Cityscapes Dataset for Semantic Urban Scene Understanding

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Observation 6f6c66a7-f454-40de-abd1-a0f4d78f3660 · outbound

This paper cites Segmentation transformer: Object-contextual representations for semantic segmentation.

Safety Monitoring of Machine Learning Perception Functions: a Survey Segmentation transformer: Object-contextual representations for semantic segmentation

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Observation d72efd3d-2d31-4513-afc7-e04bbdde3bc1 · outbound

This paper cites Imagenet: A large-scale hierarchical image database.

Safety Monitoring of Machine Learning Perception Functions: a Survey Imagenet: A large-scale hierarchical image database

Reference 42

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Observation ecee1b17-93c1-449c-921e-866bf5f685b5 · outbound

This paper cites Microsoft coco: Common objects in context.

Safety Monitoring of Machine Learning Perception Functions: a Survey Microsoft coco: Common objects in context

Reference 43

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Observation 68f3b953-837f-4884-8d67-ea57ae5bfcd0 · outbound

This paper cites Swin Transformer V2: Scaling Up Capacity and Resolution.

Safety Monitoring of Machine Learning Perception Functions: a Survey Swin Transformer V2: Scaling Up Capacity and Resolution

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Observation c91e06db-8e14-43f4-b046-c88e3dee58b5 · outbound

This paper cites Uncertainty in Machine Learning: A Safety Perspective on Autonomous Driving.

Safety Monitoring of Machine Learning Perception Functions: a Survey Uncertainty in Machine Learning: A Safety Perspective on Autonomous Driving

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Observation 0fab7548-6c6b-4307-b544-bea91f19e3c1 · outbound

This paper cites Generalized Out-of-Distribution Detection: A Survey.

Safety Monitoring of Machine Learning Perception Functions: a Survey Generalized Out-of-Distribution Detection: A Survey

Reference 46

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Observation ae94e501-5e88-4a6e-b99b-5411b6955862 · outbound

This paper cites Fishyscapes: A benchmark for safe semantic segmentation in autonomous driving.

Safety Monitoring of Machine Learning Perception Functions: a Survey Fishyscapes: A benchmark for safe semantic segmentation in autonomous driving

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Observation f8ef470a-73da-49b3-96cf-e29d04a5cc76 · outbound

This paper cites Performance measures for classification systems with rejection.

Safety Monitoring of Machine Learning Perception Functions: a Survey Performance measures for classification systems with rejection

Reference 48

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Observation a2e2d2e5-23b5-499f-b118-c259651c16e3 · outbound

This paper cites AMANDA: Semi-supervised Density-based Adaptive Model for Non-stationary Data with Extreme Verification Latency.Information Sciences.

Safety Monitoring of Machine Learning Perception Functions: a Survey AMANDA: Semi-supervised Density-based Adaptive Model for Non-stationary Data with Extreme Verification Latency.Information Sciences

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Observation 2a5300ce-bd2f-4575-8539-7e58f4bb77d7 · outbound

This paper cites Benchmarking Neural Network Robustness to Common Corruptions and Perturbations.

Safety Monitoring of Machine Learning Perception Functions: a Survey Benchmarking Neural Network Robustness to Common Corruptions and Perturbations

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Observation 5a4ff39e-eb42-44b9-938f-f7016b3d3266 · outbound

This paper cites Sensor fault detection and diagnosis for autonomous systems.

Safety Monitoring of Machine Learning Perception Functions: a Survey Sensor fault detection and diagnosis for autonomous systems

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Observation 21ce5f8f-5b48-40d3-bba6-c0d6fdbfa8d8 · outbound

This paper cites Active tuning of intrinsic camera parameters.

Safety Monitoring of Machine Learning Perception Functions: a Survey Active tuning of intrinsic camera parameters

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Observation e67578b4-3cd5-4c80-99f5-30d9bef8fac0 · outbound

This paper cites Deployment of backup sensors in wireless sensor networks for structural health monitoring.

Safety Monitoring of Machine Learning Perception Functions: a Survey Deployment of backup sensors in wireless sensor networks for structural health monitoring

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Observation 9b687d87-cc7e-40bd-8e9b-b297491bd546 · outbound

This paper cites Crash and disengagement data of autonomous vehicles on public roads in California.

Safety Monitoring of Machine Learning Perception Functions: a Survey Crash and disengagement data of autonomous vehicles on public roads in California

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Resolution: unresolved; no resolver link, observed 2026-08-11T19:45:43.204924Z

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Observation b9224916-9e48-45e7-b0c3-ac0611eb622f · outbound

This paper cites Survey of image denoising techniques.

Safety Monitoring of Machine Learning Perception Functions: a Survey Survey of image denoising techniques

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Observation 92e214a9-1d22-4a5d-84c9-34a470b2084c · outbound

This paper cites Deep learning on image denoising: An overview.Neural Networks.

Safety Monitoring of Machine Learning Perception Functions: a Survey Deep learning on image denoising: An overview.Neural Networks

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Observation 612d69d9-ee26-48f7-9459-c7d62b95d562 · outbound

This paper cites It’s Not All About Size: On the Role of Data Properties in Pedestrian Detection.

Safety Monitoring of Machine Learning Perception Functions: a Survey It’s Not All About Size: On the Role of Data Properties in Pedestrian Detection

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Observation 0e5bf7c7-bb89-4bbe-882b-354a39ac63f7 · outbound

This paper cites Autopilot Blamed for Tesla’s Crash Into Overturned Truck.

Safety Monitoring of Machine Learning Perception Functions: a Survey Autopilot Blamed for Tesla’s Crash Into Overturned Truck

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Observation bbb5be2d-8cfb-4567-98ef-f17164c3771e · outbound

This paper cites Attribute-aware pedestrian detection in a crowd.

Safety Monitoring of Machine Learning Perception Functions: a Survey Attribute-aware pedestrian detection in a crowd

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Observation 26428012-d5a0-467b-b867-1a1f30790d2a · outbound

This paper cites Threat of adversarial attacks on deep learning in computer vision: A survey.

Safety Monitoring of Machine Learning Perception Functions: a Survey Threat of adversarial attacks on deep learning in computer vision: A survey

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Observation a4bc61fd-c329-4ce2-9c42-c81684e228e9 · outbound

This paper cites Adversarial examples in the physical world.

Safety Monitoring of Machine Learning Perception Functions: a Survey Adversarial examples in the physical world

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Observation 04114367-9300-4c1d-a53e-58101d6db581 · outbound

This paper cites Attacking vision-based perception in end-to-end autonomous driving models.

Safety Monitoring of Machine Learning Perception Functions: a Survey Attacking vision-based perception in end-to-end autonomous driving models

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Observation 5efa815b-3284-45dd-a4fd-c8b8b37a8f53 · outbound

This paper cites Experience with model-based user-centered risk assessment for service robots.

Safety Monitoring of Machine Learning Perception Functions: a Survey Experience with model-based user-centered risk assessment for service robots

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-11T19:45:43.246142Z

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Observation 3f1e5e37-289d-4ff3-81be-caaec114e382 · outbound

This paper cites Compositional falsification of cyber-physical systems with machine learning components.

Safety Monitoring of Machine Learning Perception Functions: a Survey Compositional falsification of cyber-physical systems with machine learning components

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-11T19:45:43.251045Z

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Observation 3adada6d-194b-4b8b-acd1-8a06a7676710 · outbound

This paper cites Verifai: A toolkit for the formal design and analysis of artificial intelligence-based systems.

Safety Monitoring of Machine Learning Perception Functions: a Survey Verifai: A toolkit for the formal design and analysis of artificial intelligence-based systems

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-11T19:45:43.255782Z

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Observation f1fa5412-403b-4a82-a822-5f6f7889d01f · outbound

This paper cites A safety analysis method for perceptual components in automated driving.

Safety Monitoring of Machine Learning Perception Functions: a Survey A safety analysis method for perceptual components in automated driving

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-11T19:45:43.260576Z

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Observation dc63e859-0d10-4718-a745-d1233d9d9fe5 · outbound

This paper cites Efficient uncertainty estimation for semantic segmentation in videos.

Safety Monitoring of Machine Learning Perception Functions: a Survey Efficient uncertainty estimation for semantic segmentation in videos

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-11T19:45:43.265729Z

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Observation 5ca92306-4e1a-4c7c-bd67-811dbd0a00ec · outbound

This paper cites Calibrating uncertainty models for steering angle estimation.

Safety Monitoring of Machine Learning Perception Functions: a Survey Calibrating uncertainty models for steering angle estimation

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-11T19:45:43.270480Z

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Observation ae4428f2-3d9a-4a86-a002-61268db21f14 · outbound

This paper cites Uncertainty Estimation for Data-Driven Visual Odometry.

Safety Monitoring of Machine Learning Perception Functions: a Survey Uncertainty Estimation for Data-Driven Visual Odometry

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-11T19:45:43.275530Z

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Observation 4b8dbfb7-82a8-4dfe-9e0b-cc75f52fe9e2 · outbound

This paper cites Superpixel-based Knowledge Infusion in Deep Neural Networks for Image Classification.

Safety Monitoring of Machine Learning Perception Functions: a Survey Superpixel-based Knowledge Infusion in Deep Neural Networks for Image Classification

Reference 70

Resolution: verified exact; local_arxiv, observed 2026-08-11T19:45:44.155910Z

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Observation bcdd6101-1f4f-46ef-b882-b665f344e18e · outbound

This paper cites Learning semantic relationships for better action retrieval in images.

Safety Monitoring of Machine Learning Perception Functions: a Survey Learning semantic relationships for better action retrieval in images

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-11T19:45:43.285397Z

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Observation 227e6372-ac4c-4211-8c79-acc72b971c39 · outbound

This paper cites A semantic loss function for deep learning with symbolic knowledge.

Safety Monitoring of Machine Learning Perception Functions: a Survey A semantic loss function for deep learning with symbolic knowledge

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-11T19:45:43.291384Z

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Observation 0c9ab805-99e3-42cc-a558-cbfce21b511b · outbound

This paper cites Logic tensor networks for semantic image interpretation.

Safety Monitoring of Machine Learning Perception Functions: a Survey Logic tensor networks for semantic image interpretation

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-11T19:45:43.296039Z

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Observation 882fae43-bcbf-4c46-9414-daa04e27df30 · outbound

This paper cites Boosting with abstention.

Safety Monitoring of Machine Learning Perception Functions: a Survey Boosting with abstention

Reference 74

Resolution: unresolved; no resolver link, observed 2026-08-11T19:45:43.300976Z

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Observation 632fcd1c-889f-4e87-a49f-231df8a435e6 · outbound

This paper cites Selectivenet: A deep neural network with an integrated reject option.

Safety Monitoring of Machine Learning Perception Functions: a Survey Selectivenet: A deep neural network with an integrated reject option

Reference 75

Resolution: unresolved; no resolver link, observed 2026-08-11T19:45:43.305730Z

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Observation 25fcb905-85c2-409b-8048-e1efb4cd4c15 · outbound

This paper cites Automated evaluation of semantic segmentation robustness for autonomous driving.

Safety Monitoring of Machine Learning Perception Functions: a Survey Automated evaluation of semantic segmentation robustness for autonomous driving

Reference 76

Resolution: unresolved; no resolver link, observed 2026-08-11T19:45:43.310216Z

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Observation ace0a06c-53ef-4025-ad62-a81fc9ee5611 · outbound

This paper cites Failing to learn: Autonomously identifying perception failures for self-driving cars.

Safety Monitoring of Machine Learning Perception Functions: a Survey Failing to learn: Autonomously identifying perception failures for self-driving cars

Reference 77

Resolution: unresolved; no resolver link, observed 2026-08-11T19:45:43.314972Z

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Observation e618fa8c-05ee-4c24-add0-7a8108fcd05d · outbound

This paper cites A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks.

Safety Monitoring of Machine Learning Perception Functions: a Survey A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks

Reference 78

Resolution: unresolved; no resolver link, observed 2026-08-11T19:45:43.319593Z

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Observation 58593d7d-4700-422f-b0fa-235d0128c831 · outbound

This paper cites Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks.

Safety Monitoring of Machine Learning Perception Functions: a Survey Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks

Reference 79

Resolution: unresolved; no resolver link, observed 2026-08-11T19:45:43.324564Z

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Observation 70fd0c7b-69d4-4690-8b35-6c19bca600a6 · outbound

This paper cites Generalized odin: Detecting out-of-distribution image without learning from out-of- distribution data.

Safety Monitoring of Machine Learning Perception Functions: a Survey Generalized odin: Detecting out-of-distribution image without learning from out-of- distribution data

Reference 80

Resolution: unresolved; no resolver link, observed 2026-08-11T19:45:43.329662Z

Source-reported events for the cited work

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Observation 5813ccce-0df8-4e78-abff-399fa2ed7bc8 · outbound

This paper cites Model assertions for debugging machine learning.

Safety Monitoring of Machine Learning Perception Functions: a Survey Model assertions for debugging machine learning

Reference 81

Resolution: unresolved; no resolver link, observed 2026-08-11T19:45:43.334113Z

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Observation f27e2e59-3bb1-4ad7-871b-a6cbf100d872 · outbound

This paper cites Safety Validation of Autonomous Vehicles using Assertion Checking.

Safety Monitoring of Machine Learning Perception Functions: a Survey Safety Validation of Autonomous Vehicles using Assertion Checking

Reference 82

Resolution: verified exact; local_arxiv, observed 2026-08-11T19:45:44.123035Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation ea8d74bc-2161-4570-af00-6b83597ba31c · outbound

This paper cites Monitoring Object Detection Abnormalities via Data-Label and Post-Algorithm Abstractions.

Safety Monitoring of Machine Learning Perception Functions: a Survey Monitoring Object Detection Abnormalities via Data-Label and Post-Algorithm Abstractions

Reference 83

Resolution: unresolved; no resolver link, observed 2026-08-11T19:45:43.343325Z

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Observation 2a11d699-4563-496d-b3d2-bac909b4160c · outbound

This paper cites Robust Detection of Objects under Periodic Motion with Gaussian Process Filtering.

Safety Monitoring of Machine Learning Perception Functions: a Survey Robust Detection of Objects under Periodic Motion with Gaussian Process Filtering

Reference 84

Resolution: unresolved; no resolver link, observed 2026-08-11T19:45:43.348071Z

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Observation 4b9b6c80-cdf1-48ff-a29b-8b47642c87d4 · outbound

This paper cites A consensus novelty detection ensemble approach for anomaly detection in activities of daily living.

Safety Monitoring of Machine Learning Perception Functions: a Survey A consensus novelty detection ensemble approach for anomaly detection in activities of daily living

Reference 85

Resolution: unresolved; no resolver link, observed 2026-08-11T19:45:43.352953Z

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Observation b4afad87-f348-41b6-9180-707c1de79b17 · outbound

This paper cites Informed democracy: voting-based novelty detection for action recognition.

Safety Monitoring of Machine Learning Perception Functions: a Survey Informed democracy: voting-based novelty detection for action recognition

Reference 86

Resolution: unresolved; no resolver link, observed 2026-08-11T19:45:43.357442Z

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Observation 944a4947-4be1-4a8c-8ae7-725c2d5a5caa · outbound

This paper cites Real-Time Detectors for Digital and Physical Adversarial Inputs to Perception Systems.

Safety Monitoring of Machine Learning Perception Functions: a Survey Real-Time Detectors for Digital and Physical Adversarial Inputs to Perception Systems

Reference 87

Resolution: verified exact; local_arxiv, observed 2026-08-11T19:45:44.103233Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation f2a79b63-a360-4ed0-a22a-409cf6aab9b6 · outbound

This paper cites Adversarial sample detection for deep neural network through model mutation testing.

Safety Monitoring of Machine Learning Perception Functions: a Survey Adversarial sample detection for deep neural network through model mutation testing

Reference 88

Resolution: unresolved; no resolver link, observed 2026-08-11T19:45:43.366877Z

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Observation fe852062-0c72-4e3d-b6ff-faebd97e15ec · outbound

This paper cites Input Validation for Neural Networks via Runtime Local Robustness Verification.

Safety Monitoring of Machine Learning Perception Functions: a Survey Input Validation for Neural Networks via Runtime Local Robustness Verification

Reference 89

Resolution: unresolved; no resolver link, observed 2026-08-11T19:45:43.371368Z

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Observation 296d77b5-d1d8-42fd-b6c2-8370b25e9e21 · outbound

This paper cites Did you miss the sign? A false negative alarm system for traffic sign detectors.

Safety Monitoring of Machine Learning Perception Functions: a Survey Did you miss the sign? A false negative alarm system for traffic sign detectors

Reference 90

Resolution: unresolved; no resolver link, observed 2026-08-11T19:45:43.377102Z

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Observation 877b6811-b541-495b-8569-cbcae392e351 · outbound

This paper cites ReAct: Out-of-distribution Detection With Rectified Activations.

Safety Monitoring of Machine Learning Perception Functions: a Survey ReAct: Out-of-distribution Detection With Rectified Activations

Reference 91

Resolution: unresolved; no resolver link, observed 2026-08-11T19:45:43.382795Z

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Observation a7fbba01-9c14-404b-a73b-8f927c188663 · outbound

This paper cites Into the unknown: Active monitoring of neural networks.

Safety Monitoring of Machine Learning Perception Functions: a Survey Into the unknown: Active monitoring of neural networks

Reference 92

Resolution: unresolved; no resolver link, observed 2026-08-11T19:45:43.387598Z

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Observation 17dec146-962e-4220-8ced-896cb53e5a4d · outbound

This paper cites Runtime monitoring neuron activation patterns.

Safety Monitoring of Machine Learning Perception Functions: a Survey Runtime monitoring neuron activation patterns

Reference 93

Resolution: unresolved; no resolver link, observed 2026-08-11T19:45:43.392545Z

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Observation abf7e4a0-6b78-446b-94ed-316e562784e7 · outbound

This paper cites Outside the Box: Abstraction-Based Monitoring of Neural Networks.

Safety Monitoring of Machine Learning Perception Functions: a Survey Outside the Box: Abstraction-Based Monitoring of Neural Networks

Reference 94

Resolution: unresolved; no resolver link, observed 2026-08-11T19:45:43.398107Z

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Observation fa4fcdcf-33d5-4cfd-af44-4eac50817aa4 · outbound

This paper cites SENA: Similarity-based Error-checking of Neural Activations.

Safety Monitoring of Machine Learning Perception Functions: a Survey SENA: Similarity-based Error-checking of Neural Activations

Reference 95

Resolution: unresolved; no resolver link, observed 2026-08-11T19:45:43.402955Z

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Observation ecdc8631-46bd-47fb-862e-b8e0471d970f · outbound

This paper cites Customizable Reference Runtime Monitoring of Neural Networks using Resolution Boxes.

Safety Monitoring of Machine Learning Perception Functions: a Survey Customizable Reference Runtime Monitoring of Neural Networks using Resolution Boxes

Reference 96

Resolution: verified exact; local_arxiv, observed 2026-08-11T19:45:44.068052Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation c08c631f-64d6-4f5e-adf7-1b7383a7ee2b · outbound

This paper cites Dissector: Input validation for deep learning applications by crossing-layer dissection.

Safety Monitoring of Machine Learning Perception Functions: a Survey Dissector: Input validation for deep learning applications by crossing-layer dissection

Reference 97

Resolution: unresolved; no resolver link, observed 2026-08-11T19:45:43.412723Z

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Observation 11be282d-bf04-4ca5-9a77-3915000b851e · outbound

This paper cites FACER: A universal framework for detecting anomalous operation of deep neural networks.

Safety Monitoring of Machine Learning Perception Functions: a Survey FACER: A universal framework for detecting anomalous operation of deep neural networks

Reference 98

Resolution: unresolved; no resolver link, observed 2026-08-11T19:45:43.417305Z

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Observation adc64601-323f-4d2a-ad1e-29ea0ff8891a · outbound

This paper cites A simple unified framework for detecting out-of-distribution samples and adversarial attacks.

Safety Monitoring of Machine Learning Perception Functions: a Survey A simple unified framework for detecting out-of-distribution samples and adversarial attacks

Reference 99

Resolution: unresolved; no resolver link, observed 2026-08-11T19:45:43.425445Z

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Observation 38dd41b1-4ee7-4520-8cfa-58b2e953cdbf · outbound

This paper cites Task-Aware Novelty Detection for Visual-based Deep Learning in Autonomous Systems.

Safety Monitoring of Machine Learning Perception Functions: a Survey Task-Aware Novelty Detection for Visual-based Deep Learning in Autonomous Systems

Reference 100

Resolution: unresolved; no resolver link, observed 2026-08-11T19:45:43.434308Z

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