default search action
Google
Google Scholar
Semantic Scholar
Internet Archive Scholar
CiteSeerX
ORCIDTaylor T. Johnson
Person information
- affiliation: Vanderbilt University, Nashville, TN, USA
Refine list
refinements active!
zoomed in on ?? of ?? records
view refined list in
export refined list as
showing all ?? records
2020 – today
- 2024
[j26]Joel A. Rosenfeld
, Benjamin P. Russo, Rushikesh Kamalapurkar, Taylor T. Johnson:
The Occupation Kernel Method for Nonlinear System Identification. SIAM J. Control. Optim. 62(3): 1643-1668 (2024)- [c101]Ziyan An, Taylor T. Johnson, Meiyi Ma:
Formal Logic Enabled Personalized Federated Learning through Property Inference. AAAI 2024: 10882-10890 - [c100]Preston K. Robinette, Diego Manzanas Lopez, Serena Serbinowska, Kevin Leach, Taylor T. Johnson:
Case Study: Neural Network Malware Detection Verification for Feature and Image Datasets. FormaliSE@ICSE 2024: 127-137 - [c99]Tianshu Bao, Hua Wei, Junyi Ji, Daniel B. Work, Taylor T. Johnson:
Spatial-Temporal PDE Networks for Traffic Flow Forecasting. ECML/PKDD (10) 2024: 166-182 - [e2]Guy Avni, Mirco Giacobbe, Taylor T. Johnson, Guy Katz, Anna Lukina, Nina Narodytska, Christian Schilling:
AI Verification - First International Symposium, SAIV 2024, Montreal, QC, Canada, July 22-23, 2024, Proceedings. Lecture Notes in Computer Science 14846, Springer 2024, ISBN 978-3-031-65111-3 [contents] - [i36]Ziyan An, Taylor T. Johnson, Meiyi Ma:
Formal Logic Enabled Personalized Federated Learning Through Property Inference. CoRR abs/2401.07448 (2024) - [i35]Preston K. Robinette, Diego Manzanas Lopez, Serena Serbinowska, Kevin Leach, Taylor T. Johnson:
Case Study: Neural Network Malware Detection Verification for Feature and Image Datasets. CoRR abs/2404.05703 (2024) - [i34]Tianshu Bao, Hengrong Du, Weiming Xiang, Taylor T. Johnson:
A New Hybrid Automaton Framework with Partial Differential Equation Dynamics. CoRR abs/2404.11900 (2024) - 2023
- [j25]Christopher Brix, Mark Niklas Müller, Stanley Bak, Taylor T. Johnson, Changliu Liu:
First three years of the international verification of neural networks competition (VNN-COMP). Int. J. Softw. Tools Technol. Transf. 25(3): 329-339 (2023) - [j24]Luan Viet Nguyen, Hoang-Dung Tran, Taylor T. Johnson, Vijay Gupta:
Decentralized Safe Control for Distributed Cyber-Physical Systems Using Real-Time Reachability Analysis. IEEE Trans. Control. Netw. Syst. 10(3): 1234-1244 (2023) - [c98]Diego Manzanas Lopez, Matthias Althoff, Marcelo Forets, Taylor T. Johnson, Tobias Ladner, Christian Schilling:
ARCH-COMP23 Category Report: Artificial Intelligence and Neural Network Control Systems (AINNCS) for Continuous and Hybrid Systems Plants. ARCH 2023: 89-125 - [c97]Taylor T. Johnson:
ARCH-COMP23 Repeatability Evaluation Report. ARCH 2023: 189-195 - [c96]Diego Manzanas Lopez, Sung Woo Choi, Hoang-Dung Tran, Taylor T. Johnson:
NNV 2.0: The Neural Network Verification Tool. CAV (2) 2023: 397-412 - [c95]Preston K. Robinette, Hanchen D. Wang, Nishan Shehadeh, Daniel Moyer, Taylor T. Johnson:
SUDS: Sanitizing Universal and Dependent Steganography. ECAI 2023: 1978-1985 - [c94]Hoang-Dung Tran, Diego Manzanas Lopez, Taylor T. Johnson:
Tutorial: Neural Network and Autonomous Cyber-Physical Systems Formal Verification for Trustworthy AI and Safe Autonomy. EMSOFT 2023: 1-2 - [c93]Neelanjana Pal, Diego Manzanas Lopez, Taylor T. Johnson:
Robustness Verification of Deep Neural Networks Using Star-Based Reachability Analysis with Variable-Length Time Series Input. FMICS 2023: 170-188 - [c92]Preston K. Robinette, Nathaniel P. Hamilton, Taylor T. Johnson:
Self-Preserving Genetic Algorithms for Safe Learning in Discrete Action Spaces. ICCPS 2023: 110-119 - [c91]Preston K. Robinette, Nathaniel P. Hamilton, Taylor T. Johnson:
DEMO: Self-Preserving Genetic Algorithms vs. Safe Reinforcement Learning in Discrete Action Spaces. ICCPS 2023: 278-279 - [c90]Ziyan An, Xia Wang, Taylor T. Johnson, Jonathan Sprinkle, Meiyi Ma:
Runtime Monitoring of Accidents in Driving Recordings with Multi-type Logic in Empirical Models. RV 2023: 376-388 - [c89]Daniel Neider, Taylor T. Johnson:
Track C1: Safety Verification of Deep Neural Networks (DNNs). AISoLA 2023: 217-224 - [c88]Preston K. Robinette, Diego Manzanas Lopez, Taylor T. Johnson:
Benchmark: Neural Network Malware Classification. AISoLA 2023: 291-298 - [c87]Neelanjana Pal, Seojin Lee, Taylor T. Johnson:
Benchmark: Formal Verification of Semantic Segmentation Neural Networks. AISoLA 2023: 311-330 - [c86]Diego Manzanas Lopez, Taylor T. Johnson:
Empirical Analysis of Benchmark Generation for the Verification of Neural Network Image Classifiers. AISoLA 2023: 331-347 - [c85]Sergiy Bogomolov, Taylor T. Johnson, Diego Manzanas Lopez, Patrick Musau, Paulius Stankaitis:
Online Reachability Analysis and Space Convexification for Autonomous Racing. FMAS@iFM 2023: 95-112 - [c84]Neelanjana Pal, Taylor T. Johnson:
Formal Verification of Long Short-Term Memory based Audio Classifiers: A Star based Approach. FMAS@iFM 2023: 162-179 - [i33]Christopher Brix, Mark Niklas Müller, Stanley Bak, Taylor T. Johnson, Changliu Liu:
First Three Years of the International Verification of Neural Networks Competition (VNN-COMP). CoRR abs/2301.05815 (2023) - [i32]Neelanjana Pal, Diego Manzanas Lopez, Taylor T. Johnson:
Robustness Verification of Deep Neural Networks using Star-Based Reachability Analysis with Variable-Length Time Series Input. CoRR abs/2307.13907 (2023) - [i31]Yiqi Zhao, Ziyan An, Meiyi Ma, Taylor T. Johnson:
EduSAT: A Pedagogical Tool for Theory and Applications of Boolean Satisfiability. CoRR abs/2308.07890 (2023) - [i30]Preston K. Robinette, Hanchen D. Wang, Nishan Shehadeh, Daniel Moyer, Taylor T. Johnson:
SUDS: Sanitizing Universal and Dependent Steganography. CoRR abs/2309.13467 (2023) - [i29]Preston K. Robinette, Daniel Moyer, Taylor T. Johnson:
Monsters in the Dark: Sanitizing Hidden Threats with Diffusion Models. CoRR abs/2310.06951 (2023) - [i28]Xia Wang, Anda Liang, Jonathan Sprinkle, Taylor T. Johnson:
Robustness Verification for Knowledge-Based Logic of Risky Driving Scenes. CoRR abs/2312.16364 (2023) - [i27]Christopher Brix, Stanley Bak, Changliu Liu, Taylor T. Johnson:
The Fourth International Verification of Neural Networks Competition (VNN-COMP 2023): Summary and Results. CoRR abs/2312.16760 (2023) - 2022
- [j23]Hoang-Dung Tran, Weiming Xiang, Taylor T. Johnson:
Verification Approaches for Learning-Enabled Autonomous Cyber-Physical Systems. IEEE Des. Test 39(1): 24-34 (2022) - [j22]Joel A. Rosenfeld, Rushikesh Kamalapurkar, L. Forest Gruss, Taylor T. Johnson:
Dynamic Mode Decomposition for Continuous Time Systems with the Liouville Operator. J. Nonlinear Sci. 32(1): 5 (2022) - [j21]Hoang-Dung Tran, Luan Viet Nguyen, Patrick Musau, Weiming Xiang, Taylor T. Johnson:
Real-Time Verification for Distributed Cyber-Physical Systems. Leibniz Trans. Embed. Syst. 8(2): 07:1-07:19 (2022) - [j20]Xiaodong Yang, Omar Ali Beg, Matthew Kenigsberg, Taylor T. Johnson:
A Framework for Identification and Validation of Affine Hybrid Automata from Input-Output Traces. ACM Trans. Cyber Phys. Syst. 6(2): 13:1-13:24 (2022) - [c83]Diego Manzanas Lopez, Matthias Althoff, Luis Benet, Xin Chen, Jiameng Fan, Marcelo Forets, Chao Huang, Taylor T. Johnson, Tobias Ladner, Wenchao Li, Christian Schilling, Qi Zhu:
ARCH-COMP22 Category Report: Artificial Intelligence and Neural Network Control Systems (AINNCS) for Continuous and Hybrid Systems Plants. ARCH@SAFECOMP 2022: 142-184 - [c82]Taylor T. Johnson:
ARCH-COMP22 Repeatability Evaluation Report. ARCH@SAFECOMP 2022: 222-230 - [c81]Xiaodong Yang, Tom Yamaguchi, Hoang-Dung Tran, Bardh Hoxha, Taylor T. Johnson, Danil V. Prokhorov:
Neural Network Repair with Reachability Analysis. FORMATS 2022: 221-236 - [c80]Diego Manzanas Lopez, Patrick Musau, Nathaniel Hamilton, Taylor T. Johnson:
Reachability Analysis of a General Class of Neural Ordinary Differential Equations. FORMATS 2022: 258-277 - [c79]Patrick Musau, Nathaniel Hamilton, Diego Manzanas Lopez, Preston Robinette, Taylor T. Johnson:
On Using Real-Time Reachability for the Safety Assurance of Machine Learning Controllers. ICAA 2022: 1-10 - [c78]Nathaniel Hamilton, Patrick Musau, Diego Manzanas Lopez, Taylor T. Johnson:
Zero-Shot Policy Transfer in Autonomous Racing: Reinforcement Learning vs Imitation Learning. ICAA 2022: 11-20 - [c77]Nathaniel Hamilton, Preston Robinette, Taylor T. Johnson:
Training Agents to Satisfy Timed and Untimed Signal Temporal Logic Specifications with Reinforcement Learning. SEFM 2022: 190-206 - [c76]Bernard Serbinowski, Taylor T. Johnson:
BehaVerify: Verifying Temporal Logic Specifications for Behavior Trees. SEFM 2022: 307-323 - [c75]Tianshu Bao, Shengyu Chen, Taylor T. Johnson, Peyman Givi, Shervin Sammak, Xiaowei Jia:
Physics guided neural networks for spatio-temporal super-resolution of turbulent flows. UAI 2022: 118-128 - [i26]Patrick Musau, Nathaniel Hamilton, Diego Manzanas Lopez, Preston Robinette, Taylor T. Johnson:
An Empirical Analysis of the Use of Real-Time Reachability for the Safety Assurance of Autonomous Vehicles. CoRR abs/2205.01419 (2022) - [i25]Nathaniel Hamilton, Kyle Dunlap, Taylor T. Johnson, Kerianne L. Hobbs:
Ablation Study of How Run Time Assurance Impacts the Training and Performance of Reinforcement Learning Agents. CoRR abs/2207.04117 (2022) - [i24]Diego Manzanas Lopez, Patrick Musau, Nathaniel Hamilton, Taylor T. Johnson:
Reachability Analysis of a General Class of Neural Ordinary Differential Equations. CoRR abs/2207.06531 (2022) - [i23]Serena Serafina Serbinowska, Taylor T. Johnson:
BehaVerify: Verifying Temporal Logic Specifications for Behavior Trees. CoRR abs/2208.05360 (2022) - [i22]Mark Niklas Müller, Christopher Brix, Stanley Bak, Changliu Liu, Taylor T. Johnson:
The Third International Verification of Neural Networks Competition (VNN-COMP 2022): Summary and Results. CoRR abs/2212.10376 (2022) - 2021
- [j19]Omar Ali Beg, Luan Viet Nguyen, Taylor T. Johnson, Ali Davoudi:
Cyber-Physical Anomaly Detection in Microgrids Using Time-Frequency Logic Formalism. IEEE Access 9: 20012-20021 (2021) - [j18]Hoang-Dung Tran, Neelanjana Pal, Diego Manzanas Lopez, Patrick Musau, Xiaodong Yang, Luan Viet Nguyen, Weiming Xiang, Stanley Bak, Taylor T. Johnson:
Verification of piecewise deep neural networks: a star set approach with zonotope pre-filter. Formal Aspects Comput. 33(4-5): 519-545 (2021) - [j17]Weiming Xiang, Hoang-Dung Tran, Xiaodong Yang, Taylor T. Johnson:
Reachable Set Estimation for Neural Network Control Systems: A Simulation-Guided Approach. IEEE Trans. Neural Networks Learn. Syst. 32(5): 1821-1830 (2021) - [c74]Joel A. Rosenfeld, Rushikesh Kamalapurkar, L. Forest Gruss, Taylor T. Johnson:
On Occupation Kernels, Liouville Operators, and Dynamic Mode Decomposition. ACC 2021: 3957-3962 - [c73]Taylor T. Johnson, Diego Manzanas Lopez, Luis Benet, Marcelo Forets, Sebastián Guadalupe, Christian Schilling, Radoslav Ivanov, Taylor J. Carpenter, James Weimer, Insup Lee:
ARCH-COMP21 Category Report: Artificial Intelligence and Neural Network Control Systems (AINNCS) for Continuous and Hybrid Systems Plants. ARCH@ADHS 2021: 90-119 - [c72]Taylor T. Johnson:
ARCH-COMP21 Repeatability Evaluation Report. ARCH@ADHS 2021: 153-160 - [c71]Hoang-Dung Tran, Neelanjana Pal, Patrick Musau, Diego Manzanas Lopez, Nathaniel Hamilton, Xiaodong Yang, Stanley Bak, Taylor T. Johnson:
Robustness Verification of Semantic Segmentation Neural Networks Using Relaxed Reachability. CAV (1) 2021: 263-286 - [c70]Xiaodong Yang, Taylor T. Johnson, Hoang-Dung Tran, Tomoya Yamaguchi, Bardh Hoxha, Danil V. Prokhorov:
Reachability analysis of deep ReLU neural networks using facet-vertex incidence. HSCC 2021: 18:1-18:7 - [c69]Tianshu Bao, Xiaowei Jia, Jacob Zwart, Jeffrey M. Sadler, Alison P. Appling, Samantha Oliver, Taylor T. Johnson:
Partial Differential Equation Driven Dynamic Graph Networks for Predicting Stream Water Temperature. ICDM 2021: 11-20 - [c68]Luan Viet Nguyen, Wesam Haddad, Taylor T. Johnson:
Model Checking for Rectangular Hybrid Systems: A Quantified Encoding Approach. SNR 2021: 9-23 - [c67]Neelanjana Pal, Taylor T. Johnson:
Work In Progress: Safety and Robustness Verification of Autoencoder-Based Regression Models using the NNV Tool. SNR 2021: 79-88 - [i21]Xiaodong Yang, Tomoya Yamaguchi, Hoang-Dung Tran, Bardh Hoxha, Taylor T. Johnson, Danil V. Prokhorov:
Reachability Analysis of Convolutional Neural Networks. CoRR abs/2106.12074 (2021) - [i20]Xiaodong Yang, Tom Yamaguchi, Hoang-Dung Tran, Bardh Hoxha, Taylor T. Johnson, Danil V. Prokhorov:
Neural Network Repair with Reachability Analysis. CoRR abs/2108.04214 (2021) - [i19]Stanley Bak, Changliu Liu, Taylor T. Johnson:
The Second International Verification of Neural Networks Competition (VNN-COMP 2021): Summary and Results. CoRR abs/2109.00498 (2021) - 2020
- [c66]Taylor T. Johnson, Diego Manzanas Lopez, Patrick Musau, Hoang-Dung Tran, Elena Botoeva, Francesco Leofante, Amir Maleki, Chelsea Sidrane, Jiameng Fan, Chao Huang:
ARCH-COMP20 Category Report: Artificial Intelligence and Neural Network Control Systems (AINNCS) for Continuous and Hybrid Systems Plants. ARCH 2020: 107-139 - [c65]Taylor T. Johnson:
ARCH-COMP20 Repeatability Evaluation Report. ARCH 2020: 175-183 - [c64]Hoang-Dung Tran, Xiaodong Yang, Diego Manzanas Lopez, Patrick Musau, Luan Viet Nguyen, Weiming Xiang, Stanley Bak, Taylor T. Johnson:
NNV: The Neural Network Verification Tool for Deep Neural Networks and Learning-Enabled Cyber-Physical Systems. CAV (1) 2020: 3-17 - [c63]Hoang-Dung Tran, Stanley Bak, Weiming Xiang, Taylor T. Johnson:
Verification of Deep Convolutional Neural Networks Using ImageStars. CAV (1) 2020: 18-42 - [c62]Stanley Bak, Hoang-Dung Tran, Kerianne Hobbs, Taylor T. Johnson:
Improved Geometric Path Enumeration for Verifying ReLU Neural Networks. CAV (1) 2020: 66-96 - [c61]Shafiul Azam Chowdhury, Sohil Lal Shrestha, Taylor T. Johnson, Christoph Csallner:
SLEMI: finding simulink compiler bugs through equivalence modulo input (EMI). ICSE (Companion Volume) 2020: 1-4 - [c60]Shafiul Azam Chowdhury, Sohil Lal Shrestha, Taylor T. Johnson, Christoph Csallner:
SLEMI: equivalence modulo input (EMI) based mutation of CPS models for finding compiler bugs in Simulink. ICSE 2020: 335-346 - [c59]Diego Manzanas Lopez, Patrick Musau, Nathaniel Hamilton, Hoang-Dung Tran, Taylor T. Johnson:
Case Study: Safety Verification of an Unmanned Underwater Vehicle. SP (Workshops) 2020: 189-195 - [i18]Xiaodong Yang, Hoang-Dung Tran, Weiming Xiang, Taylor T. Johnson:
Reachability Analysis for Feed-Forward Neural Networks using Face Lattices. CoRR abs/2003.01226 (2020) - [i17]Hoang-Dung Tran, Stanley Bak, Weiming Xiang, Taylor T. Johnson:
Verification of Deep Convolutional Neural Networks Using ImageStars. CoRR abs/2004.05511 (2020) - [i16]Hoang-Dung Tran, Xiaodong Yang, Diego Manzanas Lopez, Patrick Musau, Luan Viet Nguyen, Weiming Xiang, Stanley Bak, Taylor T. Johnson:
NNV: The Neural Network Verification Tool for Deep Neural Networks and Learning-Enabled Cyber-Physical Systems. CoRR abs/2004.05519 (2020) - [i15]Weiming Xiang, Hoang-Dung Tran, Xiaodong Yang, Taylor T. Johnson:
Reachable Set Estimation for Neural Network Control Systems: A Simulation-Guided Approach. CoRR abs/2004.12273 (2020)
2010 – 2019
- 2019
- [j16]Andrew Sogokon, Paul B. Jackson, Taylor T. Johnson:
Verifying Safety and Persistence in Hybrid Systems Using Flowpipes and Continuous Invariants. J. Autom. Reason. 63(4): 1005-1029 (2019) - [j15]Stanley Bak, Omar Ali Beg, Sergiy Bogomolov, Taylor T. Johnson, Luan Viet Nguyen, Christian Schilling:
Hybrid automata: from verification to implementation. Int. J. Softw. Tools Technol. Transf. 21(1): 87-104 (2019) - [j14]Weiming Xiang, Hoang-Dung Tran, Taylor T. Johnson:
Nonconservative Lifted Convex Conditions for Stability of Discrete-Time Switched Systems Under Minimum Dwell-Time Constraint. IEEE Trans. Autom. Control. 64(8): 3407-3414 (2019) - [j13]Hoang-Dung Tran, Feiyang Cai, Diego Manzanas Lopez, Patrick Musau, Taylor T. Johnson, Xenofon D. Koutsoukos:
Safety Verification of Cyber-Physical Systems with Reinforcement Learning Control. ACM Trans. Embed. Comput. Syst. 18(5s): 105:1-105:22 (2019) - [j12]Omar Ali Beg, Luan Viet Nguyen, Taylor T. Johnson, Ali Davoudi:
Signal Temporal Logic-Based Attack Detection in DC Microgrids. IEEE Trans. Smart Grid 10(4): 3585-3595 (2019) - [c58]Joel A. Rosenfeld, Rushikesh Kamalapurkar, Benjamin Russo, Taylor T. Johnson:
Occupation Kernels and Densely Defined Liouville Operators for System Identification. CDC 2019: 6455-6460 - [c57]Charles Hartsell, Nagabhushan Mahadevan, Shreyas Ramakrishna, Abhishek Dubey, Theodore Bapty, Taylor T. Johnson, Xenofon D. Koutsoukos, Janos Sztipanovits, Gabor Karsai:
Model-based design for CPS with learning-enabled components. DESTION@CPSIoTWeek 2019: 1-9 - [c56]Tamás Kecskés, Patrik Meijer, Taylor T. Johnson, Marcus Lucas:
Demo: a design studio for verification tools. DESTION@CPSIoTWeek 2019: 60-61 - [c55]Diego Manzanas Lopez, Patrick Musau, Hoang-Dung Tran, Souradeep Dutta, Taylor J. Carpenter, Radoslav Ivanov, Taylor T. Johnson:
ARCH-COMP19 Category Report: Artificial Intelligence and Neural Network Control Systems (AINNCS) for Continuous and Hybrid Systems Plants. ARCH@CPSIoTWeek 2019: 103-119 - [c54]Taylor T. Johnson:
ARCH-COMP19 Repeatability Evaluation Report. ARCH@CPSIoTWeek 2019: 162-169 - [c53]Diego Manzanas Lopez, Patrick Musau, Hoang-Dung Tran, Taylor T. Johnson:
Verification of Closed-loop Systems with Neural Network Controllers. ARCH@CPSIoTWeek 2019: 201-210 - [c52]Hoang-Dung Tran, Diego Manzanas Lopez, Patrick Musau, Xiaodong Yang, Luan Viet Nguyen, Weiming Xiang, Taylor T. Johnson:
Star-Based Reachability Analysis of Deep Neural Networks. FM 2019: 670-686 - [c51]Hoang-Dung Tran, Luan Viet Nguyen, Nathaniel Hamilton, Weiming Xiang, Taylor T. Johnson:
Reachability Analysis for High-Index Linear Differential Algebraic Equations. FORMATS 2019: 160-177 - [c50]Hoang-Dung Tran, Luan Viet Nguyen, Patrick Musau, Weiming Xiang, Taylor T. Johnson:
Decentralized Real-Time Safety Verification for Distributed Cyber-Physical Systems. FORTE 2019: 261-277 - [c49]Stanley Bak, Hoang-Dung Tran, Taylor T. Johnson:
Numerical verification of affine systems with up to a billion dimensions. HSCC 2019: 23-32 - [c48]Stephen A. Rees, Tamás Kecskés, Patrik Meijer, Taylor T. Johnson, Katie Dey, Paulo Tabuada, Marcus Lucas:
Cyber-physical systems virtual organization: Active resources: enabling reproducibility, improving accessibility, and lowering the barrier to entry. ICCPS 2019: 340-341 - [c47]Hoang-Dung Tran, Patrick Musau, Diego Manzanas Lopez, Xiaodong Yang, Luan Viet Nguyen, Weiming Xiang, Taylor T. Johnson:
Parallelizable reachability analysis algorithms for feed-forward neural networks. FormaliSE@ICSE 2019: 31-40 - [c46]Charles Hartsell, Nagabhushan Mahadevan, Shreyas Ramakrishna, Abhishek Dubey, Theodore Bapty, Taylor T. Johnson, Xenofon D. Koutsoukos, Janos Sztipanovits, Gabor Karsai:
CPS Design with Learning-Enabled Components: A Case Study. RSP 2019: 57-63 - [i14]Hoang-Dung Tran, Luan Viet Nguyen, Patrick Musau, Weiming Xiang, Taylor T. Johnson:
Real-Time Verification for Distributed Cyber-Physical Systems. CoRR abs/1909.09087 (2019) - 2018
- [j11]Weiming Xiang, Hoang-Dung Tran, Taylor T. Johnson:
Robust Exponential Stability and Disturbance Attenuation for Discrete-Time Switched Systems Under Arbitrary Switching. IEEE Trans. Autom. Control. 63(5): 1450-1456 (2018) - [j10]Luan Viet Nguyen, Khaza Anuarul Hoque, Stanley Bak, Steven Drager, Taylor T. Johnson:
Cyber-Physical Specification Mismatches. ACM Trans. Cyber Phys. Syst. 2(4): 23:1-23:26 (2018) - [j9]Weiming Xiang, Hoang-Dung Tran, Taylor T. Johnson:
Output Reachable Set Estimation and Verification for Multilayer Neural Networks. IEEE Trans. Neural Networks Learn. Syst. 29(11): 5777-5783 (2018) - [c45]Luan Viet Nguyen, Bardh Hoxha, Taylor T. Johnson, Georgios Fainekos:
Mission Planning for Multiple Vehicles with Temporal Specifications using UxAS. ADHS 2018: 67-72 - [c44]Taylor T. Johnson:
ARCH-COMP18 Repeatability Evaluation Report. ARCH@ADHS 2018: 128-134 - [c43]Hoang-Dung Tran, Weiming Xiang, Stanley Bak, Taylor T. Johnson:
Reachability Analysis for One Dimensional Linear Parabolic Equations. ADHS 2018: 133-138 - [c42]Patrick Musau, Diego Manzanas Lopez, Hoang-Dung Tran, Taylor T. Johnson:
Linear Differential-Algebraic Equations (Benchmark Proposal). ARCH@ADHS 2018: 174-184 - [c41]Hoang-Dung Tran, Tianshu Bao, Taylor T. Johnson:
Discrete-Space Analysis of Partial Differential Equations. ARCH@ADHS 2018: 185-195 - [c40]Patrick Musau, Taylor T. Johnson:
Verification of Continuous Time Recurrent Neural Networks (Benchmark Proposal). ARCH@ADHS 2018: 196-207 - [c39]Weiming Xiang, Hoang-Dung Tran, Joel A. Rosenfeld, Taylor T. Johnson:
Reachable Set Estimation and Safety Verification for Piecewise Linear Systems with Neural Network Controllers. ACC 2018: 1574-1579 - [c38]Shafiul Azam Chowdhury, Lina Sera Varghese, Soumik Mohian, Taylor T. Johnson, Christoph Csallner:
A curated corpus of simulink models for model-based empirical studies. SEsCPS@ICSE 2018: 45-48 - [c37]Shafiul Azam Chowdhury, Soumik Mohian, Sidharth Mehra, Siddhant Gawsane, Taylor T. Johnson, Christoph Csallner:
Automatically finding bugs in a commercial cyber-physical system development tool chain with SLforge. ICSE 2018: 981-992 - [e1]Goran Frehse, Matthias Althoff, Sergiy Bogomolov, Taylor T. Johnson:
ARCH18. 5th International Workshop on Applied Verification of Continuous and Hybrid Systems, ARCH@ADHS 2018, Oxford, UK, July 13, 2018. EPiC Series in Computing 54, EasyChair 2018 [contents] - [i13]Weiming Xiang, Diego Manzanas Lopez, Patrick Musau, Taylor T. Johnson:
Reachable Set Estimation and Verification for Neural Network Models of Nonlinear Dynamic Systems. CoRR abs/1802.03557 (2018) - [i12]Weiming Xiang, Hoang-Dung Tran, Joel A. Rosenfeld, Taylor T. Johnson:
Reachable Set Estimation and Safety Verification for Piecewise Linear Systems with Neural Network Controllers. CoRR abs/1802.06981 (2018) - [i11]Stanley Bak, Hoang-Dung Tran, Taylor T. Johnson:
Numerical Verification of Affine Systems with up to a Billion Dimensions. CoRR abs/1804.01583 (2018) - [i10]