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1809.06573
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Runtime Monitoring Neuron Activation Patterns
18 September 2018
Chih-Hong Cheng
Georg Nührenberg
Hirotoshi Yasuoka
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Papers citing
"Runtime Monitoring Neuron Activation Patterns"
46 / 46 papers shown
Runtime Safety Monitoring of Deep Neural Networks for Perception: A Survey
Albert Schotschneider
Svetlana Pavlitska
Johann Marius Zöllner
AAML
AI4CE
291
1
0
08 Nov 2025
The Achilles' Heel of LLMs: How Altering a Handful of Neurons Can Cripple Language Abilities
Zixuan Qin
Kunlin Lyu
Qingchen Yu
Yifan Sun
Zhaoxin Fan
AAML
173
2
0
11 Oct 2025
DeepProv: Behavioral Characterization and Repair of Neural Networks via Inference Provenance Graph Analysis
Firas Ben Hmida
Abderrahmen Amich
Ata Kaboudi
Birhanu Eshete
AAML
GNN
232
0
0
30 Sep 2025
Out-of-Distribution Detection via Channelwise Feature Aggregation in Neural Network-Based Receivers
Marko Tuononen
Duy Vu
Dani Korpi
Vesa Starck
Ville Hautamäki
Ville Hautamäki
513
1
0
21 May 2025
Explaining Unreliable Perception in Automated Driving: A Fuzzy-based Monitoring Approach
Aniket Salvi
Gereon Weiss
Mario Trapp
462
0
0
20 May 2025
Enhancing System Self-Awareness and Trust of AI: A Case Study in Trajectory Prediction and Planning
Lars Ullrich
Zurab Mujirishvili
Knut Graichen
281
2
0
25 Apr 2025
Revisiting Out-of-Distribution Detection in Real-time Object Detection: From Benchmark Pitfalls to a New Mitigation Paradigm
Weicheng He
Changshun Wu
Chih-Hong Cheng
Xiaowei Huang
Saddek Bensalem
OODD
482
0
0
10 Mar 2025
Landscape of AI safety concerns - A methodology to support safety assurance for AI-based autonomous systems
International Conference on System Reliability and Safety (ICSRS), 2024
Ronald Schnitzer
Lennart Kilian
Simon Roessner
Konstantinos Theodorou
Sonja Zillner
296
3
0
18 Dec 2024
A Systematic Review of Edge Case Detection in Automated Driving: Methods, Challenges and Future Directions
Saeed Rahmani
Sabine Rieder
Erwin de Gelder
Marcel Sonntag
Jorge Lorente Mallada
Sytze Kalisvaart
Vahid Hashemi
Simeon C. Calvert
314
5
0
11 Oct 2024
Gaussian-Based and Outside-the-Box Runtime Monitoring Join Forces
Runtime Verification (RV), 2024
Vahid Hashemi
Jan Křetínský
Sabine Rieder
Torsten Schön
Jan Vorhoff
240
2
0
08 Oct 2024
Unveiling AI's Blind Spots: An Oracle for In-Domain, Out-of-Domain, and Adversarial Errors
Shuangpeng Han
Mengmi Zhang
1.0K
3
0
03 Oct 2024
Monitizer: Automating Design and Evaluation of Neural Network Monitors
International Conference on Computer Aided Verification (CAV), 2024
Muqsit Azeem
Marta Grobelna
Sudeep Kanav
Jan Křetínský
Stefanie Mohr
Sabine Rieder
275
2
0
16 May 2024
Online Safety Analysis for LLMs: a Benchmark, an Assessment, and a Path Forward
Xuan Xie
Yuheng Huang
Zhehua Zhou
Yuheng Huang
Da Song
Lei Ma
OffRL
433
12
0
12 Apr 2024
A Low-cost Strategic Monitoring Approach for Scalable and Interpretable Error Detection in Deep Neural Networks
International Conference on Computer Safety, Reliability, and Security (SAFECOMP), 2023
Florian Geissler
S. Qutub
Michael Paulitsch
Karthik Pattabiraman
289
7
0
31 Oct 2023
LUNA: A Model-Based Universal Analysis Framework for Large Language Models
IEEE Transactions on Software Engineering (TSE), 2023
Da Song
Xuan Xie
Yuheng Huang
Derui Zhu
Yuheng Huang
Felix Juefei Xu
Lei Ma
ALM
395
11
0
22 Oct 2023
Runtime Monitoring DNN-Based Perception
Runtime Verification (RV), 2023
Chih-Hong Cheng
Michael Luttenberger
Rongjie Yan
226
4
0
06 Oct 2023
SMARLA: A Safety Monitoring Approach for Deep Reinforcement Learning Agents
IEEE Transactions on Software Engineering (TSE), 2023
Amirhossein Zolfagharian
Manel Abdellatif
Lionel C. Briand
S. Ramesh
403
14
0
03 Aug 2023
Detecting Out-of-distribution Objects Using Neuron Activation Patterns
European Conference on Artificial Intelligence (ECAI), 2023
Bartlomiej Olber
Krystian Radlak
K. Chachula
Jakub Lyskawa
Piotr Fratczak
OODD
156
0
0
31 Jul 2023
What, Indeed, is an Achievable Provable Guarantee for Learning-Enabled Safety Critical Systems
Saddek Bensalem
Chih-Hong Cheng
Wei Huang
Xiaowei Huang
Changshun Wu
Xingyu Zhao
AAML
306
11
0
20 Jul 2023
Preventing Errors in Person Detection: A Part-Based Self-Monitoring Framework
Franziska Schwaiger
A. Matic
Karsten Roscher
Stephan Günnemann
206
0
0
10 Jul 2023
A Survey of Safety and Trustworthiness of Large Language Models through the Lens of Verification and Validation
Artificial Intelligence Review (AIR), 2023
Xiaowei Huang
Wenjie Ruan
Wei Huang
Gao Jin
Yizhen Dong
...
Sihao Wu
Peipei Xu
Dengyu Wu
André Freitas
Mustafa A. Mustafa
ALM
429
171
0
19 May 2023
An investigation of challenges encountered when specifying training data and runtime monitors for safety critical ML applications
Hans-Martin Heyn
E. Knauss
Iswarya Malleswaran
Shruthi Dinakaran
306
9
0
31 Jan 2023
Detection of out-of-distribution samples using binary neuron activation patterns
Computer Vision and Pattern Recognition (CVPR), 2022
Bartlomiej Olber
Krystian Radlak
A. Popowicz
Michal Szczepankiewicz
K. Chachula
OODD
286
19
0
29 Dec 2022
Runtime Monitoring for Out-of-Distribution Detection in Object Detection Neural Networks
World Congress on Formal Methods (FM), 2022
V. Hashemi
Jan Křetínský
Sabine Rieder
J. Schmidt
OODD
205
9
0
15 Dec 2022
Out-Of-Distribution Detection Is Not All You Need
AAAI Conference on Artificial Intelligence (AAAI), 2022
Joris Guérin
Kevin Delmas
Raul Sena Ferreira
Jérémie Guiochet
OODD
361
42
0
29 Nov 2022
Unifying Evaluation of Machine Learning Safety Monitors
IEEE International Symposium on Software Reliability Engineering (ISSRE), 2022
Joris Guérin
Raul Sena Ferreira
Kevin Delmas
Jérémie Guiochet
218
16
0
31 Aug 2022
Evaluation of Runtime Monitoring for UAV Emergency Landing
IEEE International Conference on Robotics and Automation (ICRA), 2022
Joris Guérin
Kevin Delmas
Jérémie Guiochet
183
12
0
07 Feb 2022
Safe AI -- How is this Possible?
Harald Ruess
Simon Burton
289
0
0
25 Jan 2022
Benchmarking Safety Monitors for Image Classifiers with Machine Learning
Raul Sena Ferreira
J. Arlat
Jérémie Guiochet
H. Waeselynck
246
27
0
04 Oct 2021
How to Certify Machine Learning Based Safety-critical Systems? A Systematic Literature Review
International Conference on Automated Software Engineering (ASE), 2021
Florian Tambon
Gabriel Laberge
Le An
Amin Nikanjam
Paulina Stevia Nouwou Mindom
Y. Pequignot
Foutse Khomh
G. Antoniol
E. Merlo
François Laviolette
590
87
0
26 Jul 2021
Customizable Reference Runtime Monitoring of Neural Networks using Resolution Boxes
Runtime Verification (RV), 2021
Changshun Wu
Yliès Falcone
Saddek Bensalem
402
14
0
25 Apr 2021
Monitoring Object Detection Abnormalities via Data-Label and Post-Algorithm Abstractions
IEEE/RJS International Conference on Intelligent RObots and Systems (IROS), 2021
Yuhang Chen
Chih-Hong Cheng
Jun Yan
Rongjie Yan
179
7
0
29 Mar 2021
Abstraction and Symbolic Execution of Deep Neural Networks with Bayesian Approximation of Hidden Features
Nicolas Berthier
Amany Alshareef
James Sharp
S. Schewe
Xiaowei Huang
197
10
0
05 Mar 2021
Run-Time Monitoring of Machine Learning for Robotic Perception: A Survey of Emerging Trends
IEEE Access (IEEE Access), 2021
Q. Rahman
Peter Corke
Feras Dayoub
OOD
529
66
0
05 Jan 2021
Application of the Neural Network Dependability Kit in Real-World Environments
Amit Sahu
Noelia Vállez
Rosana Rodríguez-Bobada
Mohamad Alhaddad
Omar Moured
G. Neugschwandtner
199
2
0
14 Dec 2020
Provably-Robust Runtime Monitoring of Neuron Activation Patterns
Design, Automation and Test in Europe (DATE), 2020
Chih-Hong Cheng
AAML
348
15
0
24 Nov 2020
Continuous Safety Verification of Neural Networks
Chih-Hong Cheng
Rongjie Yan
316
12
0
12 Oct 2020
Into the Unknown: Active Monitoring of Neural Networks
Runtime Verification (RV), 2020
Anna Lukina
Christian Schilling
T. Henzinger
AAML
551
29
0
14 Sep 2020
Online Monitoring for Neural Network Based Monocular Pedestrian Pose Estimation
Arjun Gupta
Luca Carlone
3DH
241
9
0
11 May 2020
Detection and Mitigation of Rare Subclasses in Deep Neural Network Classifiers
International Conference on Artificial Intelligence Testing (ICAIT), 2019
Colin Paterson
R. Calinescu
Chiara Picardi
268
4
0
28 Nov 2019
Outside the Box: Abstraction-Based Monitoring of Neural Networks
European Conference on Artificial Intelligence (ECAI), 2019
T. Henzinger
Anna Lukina
Christian Schilling
AAML
434
68
0
20 Nov 2019
Organization of machine learning based product development as per ISO 26262 and ISO/PAS 21448
Krystian Radlak
Michal Szczepankiewicz
Tim Jones
Piotr Serwa
180
1
0
07 Oct 2019
Engineering problems in machine learning systems
Hiroshi Kuwajima
Hirotoshi Yasuoka
Toshihiro Nakae
308
3
0
01 Apr 2019
Architecting Dependable Learning-enabled Autonomous Systems: A Survey
Chih-Hong Cheng
Dhiraj Gulati
Rongjie Yan
153
4
0
27 Feb 2019
A Survey of Safety and Trustworthiness of Deep Neural Networks: Verification, Testing, Adversarial Attack and Defence, and Interpretability
Xiaowei Huang
Daniel Kroening
Wenjie Ruan
Marta Kwiatkowska
Youcheng Sun
Emese Thamo
Min Wu
Xinping Yi
AAML
649
52
0
18 Dec 2018
nn-dependability-kit: Engineering Neural Networks for Safety-Critical Autonomous Driving Systems
Chih-Hong Cheng
Chung-Hao Huang
Georg Nührenberg
220
11
0
16 Nov 2018
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