Mathias Lechner

Graduate School
Henzinger_Thomas Group

31 Publications

Mark all

[31]
2023 | Conference Paper | IST-REx-ID: 13142 | OA
Chatterjee, K., Henzinger, T. A., Lechner, M., & Zikelic, D. (2023). A learner-verifier framework for neural network controllers and certificates of stochastic systems. In Tools and Algorithms for the Construction and Analysis of Systems (Vol. 13993, pp. 3–25). Paris, France: Springer Nature. https://doi.org/10.1007/978-3-031-30823-9_1
[Published Version] View | Files available | DOI
 
[30]
2023 | Journal Article | IST-REx-ID: 12704 | OA
Lechner, M., Amini, A., Rus, D., & Henzinger, T. A. (2023). Revisiting the adversarial robustness-accuracy tradeoff in robot learning. IEEE Robotics and Automation Letters. Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/LRA.2023.3240930
[Published Version] View | Files available | DOI | WoS | arXiv
 
[29]
2023 | Conference Paper | IST-REx-ID: 14242 | OA
Lechner, M., Zikelic, D., Chatterjee, K., Henzinger, T. A., & Rus, D. (2023). Quantization-aware interval bound propagation for training certifiably robust quantized neural networks. In Proceedings of the 37th AAAI Conference on Artificial Intelligence (Vol. 37, pp. 14964–14973). Washington, DC, United States: Association for the Advancement of Artificial Intelligence. https://doi.org/10.1609/aaai.v37i12.26747
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[28]
2023 | Conference Paper | IST-REx-ID: 14559
Ansaripour, M., Chatterjee, K., Henzinger, T. A., Lechner, M., & Zikelic, D. (2023). Learning provably stabilizing neural controllers for discrete-time stochastic systems. In 21st International Symposium on Automated Technology for Verification and Analysis (Vol. 14215, pp. 357–379). Singapore, Singapore: Springer Nature. https://doi.org/10.1007/978-3-031-45329-8_17
View | DOI
 
[27]
2023 | Conference Paper | IST-REx-ID: 14830
Zikelic, D., Lechner, M., Henzinger, T. A., & Chatterjee, K. (2023). Learning control policies for stochastic systems with reach-avoid guarantees. In Proceedings of the 37th AAAI Conference on Artificial Intelligence (Vol. 37, pp. 11926–11935). Washington, DC, United States: Association for the Advancement of Artificial Intelligence. https://doi.org/10.1609/aaai.v37i10.26407
[Preprint] View | Files available | DOI | arXiv
 
[26]
2023 | Conference Paper | IST-REx-ID: 15023 | OA
Zikelic, D., Lechner, M., Verma, A., Chatterjee, K., & Henzinger, T. A. (2023). Compositional policy learning in stochastic control systems with formal guarantees. In 37th Conference on Neural Information Processing Systems. New Orleans, LO, United States.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[25]
2022 | Conference Paper | IST-REx-ID: 12010 | OA
Brunnbauer, A., Berducci, L., Brandstatter, A., Lechner, M., Hasani, R., Rus, D., & Grosu, R. (2022). Latent imagination facilitates zero-shot transfer in autonomous racing. In 2022 International Conference on Robotics and Automation (pp. 7513–7520). Philadelphia, PA, United States: IEEE. https://doi.org/10.1109/ICRA46639.2022.9811650
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[24]
2022 | Preprint | IST-REx-ID: 11366 | OA
Lechner, M., Amini, A., Rus, D., & Henzinger, T. A. (n.d.). Revisiting the adversarial robustness-accuracy tradeoff in robot learning. arXiv. https://doi.org/10.48550/arXiv.2204.07373
[Preprint] View | Files available | DOI | Download Preprint (ext.) | arXiv
 
[23]
2022 | Journal Article | IST-REx-ID: 12147 | OA
Hasani, R., Lechner, M., Amini, A., Liebenwein, L., Ray, A., Tschaikowski, M., … Rus, D. (2022). Closed-form continuous-time neural networks. Nature Machine Intelligence. Springer Nature. https://doi.org/10.1038/s42256-022-00556-7
[Published Version] View | Files available | DOI | WoS | arXiv
 
[22]
2022 | Thesis | IST-REx-ID: 11362 | OA
Lechner, M. (2022). Learning verifiable representations. Institute of Science and Technology Austria. https://doi.org/10.15479/at:ista:11362
[Published Version] View | Files available | DOI
 
[21]
2022 | Journal Article | IST-REx-ID: 12510 | OA
Gruenbacher, S. A., Lechner, M., Hasani, R., Rus, D., Henzinger, T. A., Smolka, S. A., & Grosu, R. (2022). GoTube: Scalable statistical verification of continuous-depth models. Proceedings of the AAAI Conference on Artificial Intelligence. Association for the Advancement of Artificial Intelligence. https://doi.org/10.1609/aaai.v36i6.20631
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[20]
2022 | Journal Article | IST-REx-ID: 12511 | OA
Lechner, M., Zikelic, D., Chatterjee, K., & Henzinger, T. A. (2022). Stability verification in stochastic control systems via neural network supermartingales. Proceedings of the AAAI Conference on Artificial Intelligence. Association for the Advancement of Artificial Intelligence. https://doi.org/10.1609/aaai.v36i7.20695
[Preprint] View | Files available | DOI | Download Preprint (ext.) | arXiv
 
[19]
2022 | Preprint | IST-REx-ID: 14601 | OA
Zikelic, D., Lechner, M., Chatterjee, K., & Henzinger, T. A. (n.d.). Learning stabilizing policies in stochastic control systems. arXiv. https://doi.org/10.48550/arXiv.2205.11991
[Preprint] View | Files available | DOI | Download Preprint (ext.) | arXiv
 
[18]
2022 | Preprint | IST-REx-ID: 14600 | OA
Zikelic, D., Lechner, M., Henzinger, T. A., & Chatterjee, K. (n.d.). Learning control policies for stochastic systems with reach-avoid guarantees. arXiv. https://doi.org/10.48550/ARXIV.2210.05308
[Preprint] View | Files available | DOI | Download Preprint (ext.) | arXiv
 
[17]
2021 | Conference Paper | IST-REx-ID: 10669 | OA
Grunbacher, S., Hasani, R., Lechner, M., Cyranka, J., Smolka, S. A., & Grosu, R. (2021). On the verification of neural ODEs with stochastic guarantees. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 35, pp. 11525–11535). Virtual: AAAI Press.
[Published Version] View | Files available | Download Published Version (ext.) | arXiv
 
[16]
2021 | Conference Paper | IST-REx-ID: 10671 | OA
Hasani, R., Lechner, M., Amini, A., Rus, D., & Grosu, R. (2021). Liquid time-constant networks. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 35, pp. 7657–7666). Virtual: AAAI Press.
[Published Version] View | Files available | Download Published Version (ext.) | arXiv
 
[15]
2021 | Conference Paper | IST-REx-ID: 10668 | OA
Babaiee, Z., Hasani, R., Lechner, M., Rus, D., & Grosu, R. (2021). On-off center-surround receptive fields for accurate and robust image classification. In Proceedings of the 38th International Conference on Machine Learning (Vol. 139, pp. 478–489). Virtual: ML Research Press.
[Published Version] View | Files available | Download Published Version (ext.)
 
[14]
2021 | Conference Paper | IST-REx-ID: 10670 | OA
Vorbach, C. J., Hasani, R., Amini, A., Lechner, M., & Rus, D. (2021). Causal navigation by continuous-time neural networks. In 35th Conference on Neural Information Processing Systems. Virtual.
[Published Version] View | Files available | Download Published Version (ext.) | arXiv
 
[13]
2021 | Conference Paper | IST-REx-ID: 10665 | OA
Henzinger, T. A., Lechner, M., & Zikelic, D. (2021). Scalable verification of quantized neural networks. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 35, pp. 3787–3795). Virtual: AAAI Press.
[Published Version] View | Files available | Download Published Version (ext.) | arXiv
 
[12]
2021 | Conference Paper | IST-REx-ID: 10667 | OA
Lechner, M., Žikelić, Ð., Chatterjee, K., & Henzinger, T. A. (2021). Infinite time horizon safety of Bayesian neural networks. In 35th Conference on Neural Information Processing Systems. Virtual. https://doi.org/10.48550/arXiv.2111.03165
[Published Version] View | Files available | DOI | Download Published Version (ext.) | arXiv
 
[11]
2021 | Journal Article | IST-REx-ID: 10404 | OA
Sietzen, S., Lechner, M., Borowski, J., Hasani, R., & Waldner, M. (2021). Interactive analysis of CNN robustness. Computer Graphics Forum. Wiley. https://doi.org/10.1111/cgf.14418
[Preprint] View | DOI | Download Preprint (ext.) | WoS | arXiv
 
[10]
2021 | Conference Paper | IST-REx-ID: 10666 | OA
Lechner, M., Hasani, R., Grosu, R., Rus, D., & Henzinger, T. A. (2021). Adversarial training is not ready for robot learning. In 2021 IEEE International Conference on Robotics and Automation (pp. 4140–4147). Xi’an, China. https://doi.org/10.1109/ICRA48506.2021.9561036
View | Files available | DOI | Download None (ext.) | WoS | arXiv
 
[9]
2020 | Conference Paper | IST-REx-ID: 10673 | OA
Hasani, R., Lechner, M., Amini, A., Rus, D., & Grosu, R. (2020). A natural lottery ticket winner: Reinforcement learning with ordinary neural circuits. In Proceedings of the 37th International Conference on Machine Learning (pp. 4082–4093). Virtual.
[Published Version] View | Files available | Download Published Version (ext.)
 
[8]
2020 | Conference Paper | IST-REx-ID: 9103 | OA
Gruenbacher, S., Cyranka, J., Lechner, M., Islam, M. A., Smolka, S. A., & Grosu, R. (2020). Lagrangian reachtubes: The next generation. In Proceedings of the 59th IEEE Conference on Decision and Control (Vol. 2020, pp. 1556–1563). Jeju Islang, Korea (South): IEEE. https://doi.org/10.1109/CDC42340.2020.9304042
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[7]
2020 | Conference Paper | IST-REx-ID: 10672 | OA
Lechner, M. (2020). Learning representations for binary-classification without backpropagation. In 8th International Conference on Learning Representations. Virtual ; Addis Ababa, Ethiopia: ICLR.
[Published Version] View | Files available | Download Published Version (ext.)
 
[6]
2020 | Conference Paper | IST-REx-ID: 7808 | OA
Giacobbe, M., Henzinger, T. A., & Lechner, M. (2020). How many bits does it take to quantize your neural network? In International Conference on Tools and Algorithms for the Construction and Analysis of Systems (Vol. 12079, pp. 79–97). Dublin, Ireland: Springer Nature. https://doi.org/10.1007/978-3-030-45237-7_5
[Published Version] View | Files available | DOI
 
[5]
2020 | Conference Paper | IST-REx-ID: 8194 | OA
Baranowski, M., He, S., Lechner, M., Nguyen, T. S., & Rakamarić, Z. (2020). An SMT theory of fixed-point arithmetic. In Automated Reasoning (Vol. 12166, pp. 13–31). Paris, France: Springer Nature. https://doi.org/10.1007/978-3-030-51074-9_2
[Published Version] View | DOI | Download Published Version (ext.) | WoS
 
[4]
2020 | Journal Article | IST-REx-ID: 8679
Lechner, M., Hasani, R., Amini, A., Henzinger, T. A., Rus, D., & Grosu, R. (2020). Neural circuit policies enabling auditable autonomy. Nature Machine Intelligence. Springer Nature. https://doi.org/10.1038/s42256-020-00237-3
View | Files available | DOI | WoS
 
[3]
2020 | Conference Paper | IST-REx-ID: 8704 | OA
Lechner, M., Hasani, R., Rus, D., & Grosu, R. (2020). Gershgorin loss stabilizes the recurrent neural network compartment of an end-to-end robot learning scheme. In Proceedings - IEEE International Conference on Robotics and Automation (pp. 5446–5452). Paris, France: IEEE. https://doi.org/10.1109/ICRA40945.2020.9196608
[Submitted Version] View | Files available | DOI | WoS
 
[2]
2019 | Conference Paper | IST-REx-ID: 6888 | OA
Lechner, M., Hasani, R., Zimmer, M., Henzinger, T. A., & Grosu, R. (2019). Designing worm-inspired neural networks for interpretable robotic control. In Proceedings - IEEE International Conference on Robotics and Automation (Vol. 2019–May). Montreal, QC, Canada: IEEE. https://doi.org/10.1109/icra.2019.8793840
[Submitted Version] View | Files available | DOI
 
[1]
2019 | Conference Paper | IST-REx-ID: 6985 | OA
Hasani, R., Amini, A., Lechner, M., Naser, F., Grosu, R., & Rus, D. (2019). Response characterization for auditing cell dynamics in long short-term memory networks. In Proceedings of the International Joint Conference on Neural Networks. Budapest, Hungary: IEEE. https://doi.org/10.1109/ijcnn.2019.8851954
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
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31 Publications

Mark all

[31]
2023 | Conference Paper | IST-REx-ID: 13142 | OA
Chatterjee, K., Henzinger, T. A., Lechner, M., & Zikelic, D. (2023). A learner-verifier framework for neural network controllers and certificates of stochastic systems. In Tools and Algorithms for the Construction and Analysis of Systems (Vol. 13993, pp. 3–25). Paris, France: Springer Nature. https://doi.org/10.1007/978-3-031-30823-9_1
[Published Version] View | Files available | DOI
 
[30]
2023 | Journal Article | IST-REx-ID: 12704 | OA
Lechner, M., Amini, A., Rus, D., & Henzinger, T. A. (2023). Revisiting the adversarial robustness-accuracy tradeoff in robot learning. IEEE Robotics and Automation Letters. Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/LRA.2023.3240930
[Published Version] View | Files available | DOI | WoS | arXiv
 
[29]
2023 | Conference Paper | IST-REx-ID: 14242 | OA
Lechner, M., Zikelic, D., Chatterjee, K., Henzinger, T. A., & Rus, D. (2023). Quantization-aware interval bound propagation for training certifiably robust quantized neural networks. In Proceedings of the 37th AAAI Conference on Artificial Intelligence (Vol. 37, pp. 14964–14973). Washington, DC, United States: Association for the Advancement of Artificial Intelligence. https://doi.org/10.1609/aaai.v37i12.26747
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[28]
2023 | Conference Paper | IST-REx-ID: 14559
Ansaripour, M., Chatterjee, K., Henzinger, T. A., Lechner, M., & Zikelic, D. (2023). Learning provably stabilizing neural controllers for discrete-time stochastic systems. In 21st International Symposium on Automated Technology for Verification and Analysis (Vol. 14215, pp. 357–379). Singapore, Singapore: Springer Nature. https://doi.org/10.1007/978-3-031-45329-8_17
View | DOI
 
[27]
2023 | Conference Paper | IST-REx-ID: 14830
Zikelic, D., Lechner, M., Henzinger, T. A., & Chatterjee, K. (2023). Learning control policies for stochastic systems with reach-avoid guarantees. In Proceedings of the 37th AAAI Conference on Artificial Intelligence (Vol. 37, pp. 11926–11935). Washington, DC, United States: Association for the Advancement of Artificial Intelligence. https://doi.org/10.1609/aaai.v37i10.26407
[Preprint] View | Files available | DOI | arXiv
 
[26]
2023 | Conference Paper | IST-REx-ID: 15023 | OA
Zikelic, D., Lechner, M., Verma, A., Chatterjee, K., & Henzinger, T. A. (2023). Compositional policy learning in stochastic control systems with formal guarantees. In 37th Conference on Neural Information Processing Systems. New Orleans, LO, United States.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[25]
2022 | Conference Paper | IST-REx-ID: 12010 | OA
Brunnbauer, A., Berducci, L., Brandstatter, A., Lechner, M., Hasani, R., Rus, D., & Grosu, R. (2022). Latent imagination facilitates zero-shot transfer in autonomous racing. In 2022 International Conference on Robotics and Automation (pp. 7513–7520). Philadelphia, PA, United States: IEEE. https://doi.org/10.1109/ICRA46639.2022.9811650
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[24]
2022 | Preprint | IST-REx-ID: 11366 | OA
Lechner, M., Amini, A., Rus, D., & Henzinger, T. A. (n.d.). Revisiting the adversarial robustness-accuracy tradeoff in robot learning. arXiv. https://doi.org/10.48550/arXiv.2204.07373
[Preprint] View | Files available | DOI | Download Preprint (ext.) | arXiv
 
[23]
2022 | Journal Article | IST-REx-ID: 12147 | OA
Hasani, R., Lechner, M., Amini, A., Liebenwein, L., Ray, A., Tschaikowski, M., … Rus, D. (2022). Closed-form continuous-time neural networks. Nature Machine Intelligence. Springer Nature. https://doi.org/10.1038/s42256-022-00556-7
[Published Version] View | Files available | DOI | WoS | arXiv
 
[22]
2022 | Thesis | IST-REx-ID: 11362 | OA
Lechner, M. (2022). Learning verifiable representations. Institute of Science and Technology Austria. https://doi.org/10.15479/at:ista:11362
[Published Version] View | Files available | DOI
 
[21]
2022 | Journal Article | IST-REx-ID: 12510 | OA
Gruenbacher, S. A., Lechner, M., Hasani, R., Rus, D., Henzinger, T. A., Smolka, S. A., & Grosu, R. (2022). GoTube: Scalable statistical verification of continuous-depth models. Proceedings of the AAAI Conference on Artificial Intelligence. Association for the Advancement of Artificial Intelligence. https://doi.org/10.1609/aaai.v36i6.20631
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[20]
2022 | Journal Article | IST-REx-ID: 12511 | OA
Lechner, M., Zikelic, D., Chatterjee, K., & Henzinger, T. A. (2022). Stability verification in stochastic control systems via neural network supermartingales. Proceedings of the AAAI Conference on Artificial Intelligence. Association for the Advancement of Artificial Intelligence. https://doi.org/10.1609/aaai.v36i7.20695
[Preprint] View | Files available | DOI | Download Preprint (ext.) | arXiv
 
[19]
2022 | Preprint | IST-REx-ID: 14601 | OA
Zikelic, D., Lechner, M., Chatterjee, K., & Henzinger, T. A. (n.d.). Learning stabilizing policies in stochastic control systems. arXiv. https://doi.org/10.48550/arXiv.2205.11991
[Preprint] View | Files available | DOI | Download Preprint (ext.) | arXiv
 
[18]
2022 | Preprint | IST-REx-ID: 14600 | OA
Zikelic, D., Lechner, M., Henzinger, T. A., & Chatterjee, K. (n.d.). Learning control policies for stochastic systems with reach-avoid guarantees. arXiv. https://doi.org/10.48550/ARXIV.2210.05308
[Preprint] View | Files available | DOI | Download Preprint (ext.) | arXiv
 
[17]
2021 | Conference Paper | IST-REx-ID: 10669 | OA
Grunbacher, S., Hasani, R., Lechner, M., Cyranka, J., Smolka, S. A., & Grosu, R. (2021). On the verification of neural ODEs with stochastic guarantees. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 35, pp. 11525–11535). Virtual: AAAI Press.
[Published Version] View | Files available | Download Published Version (ext.) | arXiv
 
[16]
2021 | Conference Paper | IST-REx-ID: 10671 | OA
Hasani, R., Lechner, M., Amini, A., Rus, D., & Grosu, R. (2021). Liquid time-constant networks. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 35, pp. 7657–7666). Virtual: AAAI Press.
[Published Version] View | Files available | Download Published Version (ext.) | arXiv
 
[15]
2021 | Conference Paper | IST-REx-ID: 10668 | OA
Babaiee, Z., Hasani, R., Lechner, M., Rus, D., & Grosu, R. (2021). On-off center-surround receptive fields for accurate and robust image classification. In Proceedings of the 38th International Conference on Machine Learning (Vol. 139, pp. 478–489). Virtual: ML Research Press.
[Published Version] View | Files available | Download Published Version (ext.)
 
[14]
2021 | Conference Paper | IST-REx-ID: 10670 | OA
Vorbach, C. J., Hasani, R., Amini, A., Lechner, M., & Rus, D. (2021). Causal navigation by continuous-time neural networks. In 35th Conference on Neural Information Processing Systems. Virtual.
[Published Version] View | Files available | Download Published Version (ext.) | arXiv
 
[13]
2021 | Conference Paper | IST-REx-ID: 10665 | OA
Henzinger, T. A., Lechner, M., & Zikelic, D. (2021). Scalable verification of quantized neural networks. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 35, pp. 3787–3795). Virtual: AAAI Press.
[Published Version] View | Files available | Download Published Version (ext.) | arXiv
 
[12]
2021 | Conference Paper | IST-REx-ID: 10667 | OA
Lechner, M., Žikelić, Ð., Chatterjee, K., & Henzinger, T. A. (2021). Infinite time horizon safety of Bayesian neural networks. In 35th Conference on Neural Information Processing Systems. Virtual. https://doi.org/10.48550/arXiv.2111.03165
[Published Version] View | Files available | DOI | Download Published Version (ext.) | arXiv
 
[11]
2021 | Journal Article | IST-REx-ID: 10404 | OA
Sietzen, S., Lechner, M., Borowski, J., Hasani, R., & Waldner, M. (2021). Interactive analysis of CNN robustness. Computer Graphics Forum. Wiley. https://doi.org/10.1111/cgf.14418
[Preprint] View | DOI | Download Preprint (ext.) | WoS | arXiv
 
[10]
2021 | Conference Paper | IST-REx-ID: 10666 | OA
Lechner, M., Hasani, R., Grosu, R., Rus, D., & Henzinger, T. A. (2021). Adversarial training is not ready for robot learning. In 2021 IEEE International Conference on Robotics and Automation (pp. 4140–4147). Xi’an, China. https://doi.org/10.1109/ICRA48506.2021.9561036
View | Files available | DOI | Download None (ext.) | WoS | arXiv
 
[9]
2020 | Conference Paper | IST-REx-ID: 10673 | OA
Hasani, R., Lechner, M., Amini, A., Rus, D., & Grosu, R. (2020). A natural lottery ticket winner: Reinforcement learning with ordinary neural circuits. In Proceedings of the 37th International Conference on Machine Learning (pp. 4082–4093). Virtual.
[Published Version] View | Files available | Download Published Version (ext.)
 
[8]
2020 | Conference Paper | IST-REx-ID: 9103 | OA
Gruenbacher, S., Cyranka, J., Lechner, M., Islam, M. A., Smolka, S. A., & Grosu, R. (2020). Lagrangian reachtubes: The next generation. In Proceedings of the 59th IEEE Conference on Decision and Control (Vol. 2020, pp. 1556–1563). Jeju Islang, Korea (South): IEEE. https://doi.org/10.1109/CDC42340.2020.9304042
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[7]
2020 | Conference Paper | IST-REx-ID: 10672 | OA
Lechner, M. (2020). Learning representations for binary-classification without backpropagation. In 8th International Conference on Learning Representations. Virtual ; Addis Ababa, Ethiopia: ICLR.
[Published Version] View | Files available | Download Published Version (ext.)
 
[6]
2020 | Conference Paper | IST-REx-ID: 7808 | OA
Giacobbe, M., Henzinger, T. A., & Lechner, M. (2020). How many bits does it take to quantize your neural network? In International Conference on Tools and Algorithms for the Construction and Analysis of Systems (Vol. 12079, pp. 79–97). Dublin, Ireland: Springer Nature. https://doi.org/10.1007/978-3-030-45237-7_5
[Published Version] View | Files available | DOI
 
[5]
2020 | Conference Paper | IST-REx-ID: 8194 | OA
Baranowski, M., He, S., Lechner, M., Nguyen, T. S., & Rakamarić, Z. (2020). An SMT theory of fixed-point arithmetic. In Automated Reasoning (Vol. 12166, pp. 13–31). Paris, France: Springer Nature. https://doi.org/10.1007/978-3-030-51074-9_2
[Published Version] View | DOI | Download Published Version (ext.) | WoS
 
[4]
2020 | Journal Article | IST-REx-ID: 8679
Lechner, M., Hasani, R., Amini, A., Henzinger, T. A., Rus, D., & Grosu, R. (2020). Neural circuit policies enabling auditable autonomy. Nature Machine Intelligence. Springer Nature. https://doi.org/10.1038/s42256-020-00237-3
View | Files available | DOI | WoS
 
[3]
2020 | Conference Paper | IST-REx-ID: 8704 | OA
Lechner, M., Hasani, R., Rus, D., & Grosu, R. (2020). Gershgorin loss stabilizes the recurrent neural network compartment of an end-to-end robot learning scheme. In Proceedings - IEEE International Conference on Robotics and Automation (pp. 5446–5452). Paris, France: IEEE. https://doi.org/10.1109/ICRA40945.2020.9196608
[Submitted Version] View | Files available | DOI | WoS
 
[2]
2019 | Conference Paper | IST-REx-ID: 6888 | OA
Lechner, M., Hasani, R., Zimmer, M., Henzinger, T. A., & Grosu, R. (2019). Designing worm-inspired neural networks for interpretable robotic control. In Proceedings - IEEE International Conference on Robotics and Automation (Vol. 2019–May). Montreal, QC, Canada: IEEE. https://doi.org/10.1109/icra.2019.8793840
[Submitted Version] View | Files available | DOI
 
[1]
2019 | Conference Paper | IST-REx-ID: 6985 | OA
Hasani, R., Amini, A., Lechner, M., Naser, F., Grosu, R., & Rus, D. (2019). Response characterization for auditing cell dynamics in long short-term memory networks. In Proceedings of the International Joint Conference on Neural Networks. Budapest, Hungary: IEEE. https://doi.org/10.1109/ijcnn.2019.8851954
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
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