Mathias Lechner

Graduate School
Henzinger_Thomas Group

24 Publications

Mark all

[24]
2023 | Journal Article | IST-REx-ID: 12704 | OA
Revisiting the adversarial robustness-accuracy tradeoff in robot learning
M. Lechner, A. Amini, D. Rus, T.A. Henzinger, IEEE Robotics and Automation Letters 8 (2023) 1595–1602.
View | Files available | DOI | arXiv
 
[23]
2022 | Conference Paper | IST-REx-ID: 12010 | OA
Latent imagination facilitates zero-shot transfer in autonomous racing
A. Brunnbauer, L. Berducci, A. Brandstatter, M. Lechner, R. Hasani, D. Rus, R. Grosu, in:, 2022 International Conference on Robotics and Automation, IEEE, 2022, pp. 7513–7520.
View | DOI | Download Preprint (ext.) | arXiv
 
[22]
2022 | Journal Article | IST-REx-ID: 12147 | OA
Closed-form continuous-time neural networks
R. Hasani, M. Lechner, A. Amini, L. Liebenwein, A. Ray, M. Tschaikowski, G. Teschl, D. Rus, Nature Machine Intelligence 4 (2022) 992–1003.
View | Files available | DOI | arXiv
 
[21]
2022 | Journal Article | IST-REx-ID: 12510
GoTube: Scalable statistical verification of continuous-depth models
S.A. Gruenbacher, M. Lechner, R. Hasani, D. Rus, T.A. Henzinger, S.A. Smolka, R. Grosu, Proceedings of the AAAI Conference on Artificial Intelligence 36 (2022) 6755–6764.
View | DOI | arXiv
 
[20]
2022 | Journal Article | IST-REx-ID: 12511
Stability verification in stochastic control systems via neural network supermartingales
M. Lechner, D. Zikelic, K. Chatterjee, T.A. Henzinger, Proceedings of the AAAI Conference on Artificial Intelligence 36 (2022) 7326–7336.
View | DOI | arXiv
 
[19]
2022 | Thesis | IST-REx-ID: 11362 | OA
Learning verifiable representations
M. Lechner, Learning Verifiable Representations, ISTA, 2022.
View | Files available | DOI
 
[18]
2022 | Preprint | IST-REx-ID: 11366 | OA
Revisiting the adversarial robustness-accuracy tradeoff in robot learning
M. Lechner, A. Amini, D. Rus, T.A. Henzinger, ArXiv (n.d.).
View | Files available | DOI | Download Preprint (ext.) | arXiv
 
[17]
2021 | Journal Article | IST-REx-ID: 10404 | OA
Interactive analysis of CNN robustness
S. Sietzen, M. Lechner, J. Borowski, R. Hasani, M. Waldner, Computer Graphics Forum 40 (2021) 253–264.
View | DOI | Download Preprint (ext.) | arXiv
 
[16]
2021 | Conference Paper | IST-REx-ID: 10669 | OA
On the verification of neural ODEs with stochastic guarantees
S. Grunbacher, R. Hasani, M. Lechner, J. Cyranka, S.A. Smolka, R. Grosu, in:, Proceedings of the AAAI Conference on Artificial Intelligence, AAAI Press, 2021, pp. 11525–11535.
View | Files available | Download Published Version (ext.) | arXiv
 
[15]
2021 | Conference Paper | IST-REx-ID: 10671 | OA
Liquid time-constant networks
R. Hasani, M. Lechner, A. Amini, D. Rus, R. Grosu, in:, Proceedings of the AAAI Conference on Artificial Intelligence, AAAI Press, 2021, pp. 7657–7666.
View | Files available | Download Published Version (ext.) | arXiv
 
[14]
2021 | Conference Paper | IST-REx-ID: 10668 | OA
On-off center-surround receptive fields for accurate and robust image classification
Z. Babaiee, R. Hasani, M. Lechner, D. Rus, R. Grosu, in:, Proceedings of the 38th International Conference on Machine Learning, ML Research Press, 2021, pp. 478–489.
View | Files available | Download Published Version (ext.)
 
[13]
2021 | Conference Paper | IST-REx-ID: 10670 | OA
Causal navigation by continuous-time neural networks
C.J. Vorbach, R. Hasani, A. Amini, M. Lechner, D. Rus, in:, 35th Conference on Neural Information Processing Systems, 2021.
View | Files available | Download Published Version (ext.) | arXiv
 
[12]
2021 | Conference Paper | IST-REx-ID: 10665 | OA
Scalable verification of quantized neural networks
T.A. Henzinger, M. Lechner, D. Zikelic, in:, Proceedings of the AAAI Conference on Artificial Intelligence, AAAI Press, 2021, pp. 3787–3795.
View | Files available | Download Published Version (ext.) | arXiv
 
[11]
2021 | Conference Paper | IST-REx-ID: 10667 | OA
Infinite time horizon safety of Bayesian neural networks
M. Lechner, Ð. Žikelić, K. Chatterjee, T.A. Henzinger, in:, 35th Conference on Neural Information Processing Systems, 2021.
View | Files available | DOI | Download Published Version (ext.) | arXiv
 
[10]
2021 | Conference Paper | IST-REx-ID: 10666 | OA
Adversarial training is not ready for robot learning
M. Lechner, R. Hasani, R. Grosu, D. Rus, T.A. Henzinger, in:, 2021 IEEE International Conference on Robotics and Automation, 2021, pp. 4140–4147.
View | Files available | DOI | Download None (ext.) | arXiv
 
[9]
2020 | Conference Paper | IST-REx-ID: 10673 | OA
A natural lottery ticket winner: Reinforcement learning with ordinary neural circuits
R. Hasani, M. Lechner, A. Amini, D. Rus, R. Grosu, in:, Proceedings of the 37th International Conference on Machine Learning, 2020, pp. 4082–4093.
View | Files available | Download Published Version (ext.)
 
[8]
2020 | Conference Paper | IST-REx-ID: 10672 | OA
Learning representations for binary-classification without backpropagation
M. Lechner, in:, 8th International Conference on Learning Representations, ICLR, 2020.
View | Files available | Download Published Version (ext.)
 
[7]
2020 | Conference Paper | IST-REx-ID: 7808 | OA
How many bits does it take to quantize your neural network?
M. Giacobbe, T.A. Henzinger, M. Lechner, in:, International Conference on Tools and Algorithms for the Construction and Analysis of Systems, Springer Nature, 2020, pp. 79–97.
View | Files available | DOI
 
[6]
2020 | Conference Paper | IST-REx-ID: 8194 | OA
An SMT theory of fixed-point arithmetic
M. Baranowski, S. He, M. Lechner, T.S. Nguyen, Z. Rakamarić, in:, Automated Reasoning, Springer Nature, 2020, pp. 13–31.
View | DOI | Download Published Version (ext.)
 
[5]
2020 | Journal Article | IST-REx-ID: 8679
Neural circuit policies enabling auditable autonomy
M. Lechner, R. Hasani, A. Amini, T.A. Henzinger, D. Rus, R. Grosu, Nature Machine Intelligence 2 (2020) 642–652.
View | Files available | DOI
 
[4]
2020 | Conference Paper | IST-REx-ID: 8704 | OA
Gershgorin loss stabilizes the recurrent neural network compartment of an end-to-end robot learning scheme
M. Lechner, R. Hasani, D. Rus, R. Grosu, in:, Proceedings - IEEE International Conference on Robotics and Automation, IEEE, 2020, pp. 5446–5452.
View | Files available | DOI
 
[3]
2020 | Conference Paper | IST-REx-ID: 9103 | OA
Lagrangian reachtubes: The next generation
S. Gruenbacher, J. Cyranka, M. Lechner, M.A. Islam, S.A. Smolka, R. Grosu, in:, Proceedings of the 59th IEEE Conference on Decision and Control, IEEE, 2020, pp. 1556–1563.
View | DOI | Download Preprint (ext.) | arXiv
 
[2]
2019 | Conference Paper | IST-REx-ID: 6888 | OA
Designing worm-inspired neural networks for interpretable robotic control
M. Lechner, R. Hasani, M. Zimmer, T.A. Henzinger, R. Grosu, in:, Proceedings - IEEE International Conference on Robotics and Automation, IEEE, 2019.
View | Files available | DOI
 
[1]
2019 | Conference Paper | IST-REx-ID: 6985 | OA
Response characterization for auditing cell dynamics in long short-term memory networks
R. Hasani, A. Amini, M. Lechner, F. Naser, R. Grosu, D. Rus, in:, Proceedings of the International Joint Conference on Neural Networks, IEEE, 2019.
View | DOI | Download Preprint (ext.) | arXiv
 
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24 Publications

Mark all

[24]
2023 | Journal Article | IST-REx-ID: 12704 | OA
Revisiting the adversarial robustness-accuracy tradeoff in robot learning
M. Lechner, A. Amini, D. Rus, T.A. Henzinger, IEEE Robotics and Automation Letters 8 (2023) 1595–1602.
View | Files available | DOI | arXiv
 
[23]
2022 | Conference Paper | IST-REx-ID: 12010 | OA
Latent imagination facilitates zero-shot transfer in autonomous racing
A. Brunnbauer, L. Berducci, A. Brandstatter, M. Lechner, R. Hasani, D. Rus, R. Grosu, in:, 2022 International Conference on Robotics and Automation, IEEE, 2022, pp. 7513–7520.
View | DOI | Download Preprint (ext.) | arXiv
 
[22]
2022 | Journal Article | IST-REx-ID: 12147 | OA
Closed-form continuous-time neural networks
R. Hasani, M. Lechner, A. Amini, L. Liebenwein, A. Ray, M. Tschaikowski, G. Teschl, D. Rus, Nature Machine Intelligence 4 (2022) 992–1003.
View | Files available | DOI | arXiv
 
[21]
2022 | Journal Article | IST-REx-ID: 12510
GoTube: Scalable statistical verification of continuous-depth models
S.A. Gruenbacher, M. Lechner, R. Hasani, D. Rus, T.A. Henzinger, S.A. Smolka, R. Grosu, Proceedings of the AAAI Conference on Artificial Intelligence 36 (2022) 6755–6764.
View | DOI | arXiv
 
[20]
2022 | Journal Article | IST-REx-ID: 12511
Stability verification in stochastic control systems via neural network supermartingales
M. Lechner, D. Zikelic, K. Chatterjee, T.A. Henzinger, Proceedings of the AAAI Conference on Artificial Intelligence 36 (2022) 7326–7336.
View | DOI | arXiv
 
[19]
2022 | Thesis | IST-REx-ID: 11362 | OA
Learning verifiable representations
M. Lechner, Learning Verifiable Representations, ISTA, 2022.
View | Files available | DOI
 
[18]
2022 | Preprint | IST-REx-ID: 11366 | OA
Revisiting the adversarial robustness-accuracy tradeoff in robot learning
M. Lechner, A. Amini, D. Rus, T.A. Henzinger, ArXiv (n.d.).
View | Files available | DOI | Download Preprint (ext.) | arXiv
 
[17]
2021 | Journal Article | IST-REx-ID: 10404 | OA
Interactive analysis of CNN robustness
S. Sietzen, M. Lechner, J. Borowski, R. Hasani, M. Waldner, Computer Graphics Forum 40 (2021) 253–264.
View | DOI | Download Preprint (ext.) | arXiv
 
[16]
2021 | Conference Paper | IST-REx-ID: 10669 | OA
On the verification of neural ODEs with stochastic guarantees
S. Grunbacher, R. Hasani, M. Lechner, J. Cyranka, S.A. Smolka, R. Grosu, in:, Proceedings of the AAAI Conference on Artificial Intelligence, AAAI Press, 2021, pp. 11525–11535.
View | Files available | Download Published Version (ext.) | arXiv
 
[15]
2021 | Conference Paper | IST-REx-ID: 10671 | OA
Liquid time-constant networks
R. Hasani, M. Lechner, A. Amini, D. Rus, R. Grosu, in:, Proceedings of the AAAI Conference on Artificial Intelligence, AAAI Press, 2021, pp. 7657–7666.
View | Files available | Download Published Version (ext.) | arXiv
 
[14]
2021 | Conference Paper | IST-REx-ID: 10668 | OA
On-off center-surround receptive fields for accurate and robust image classification
Z. Babaiee, R. Hasani, M. Lechner, D. Rus, R. Grosu, in:, Proceedings of the 38th International Conference on Machine Learning, ML Research Press, 2021, pp. 478–489.
View | Files available | Download Published Version (ext.)
 
[13]
2021 | Conference Paper | IST-REx-ID: 10670 | OA
Causal navigation by continuous-time neural networks
C.J. Vorbach, R. Hasani, A. Amini, M. Lechner, D. Rus, in:, 35th Conference on Neural Information Processing Systems, 2021.
View | Files available | Download Published Version (ext.) | arXiv
 
[12]
2021 | Conference Paper | IST-REx-ID: 10665 | OA
Scalable verification of quantized neural networks
T.A. Henzinger, M. Lechner, D. Zikelic, in:, Proceedings of the AAAI Conference on Artificial Intelligence, AAAI Press, 2021, pp. 3787–3795.
View | Files available | Download Published Version (ext.) | arXiv
 
[11]
2021 | Conference Paper | IST-REx-ID: 10667 | OA
Infinite time horizon safety of Bayesian neural networks
M. Lechner, Ð. Žikelić, K. Chatterjee, T.A. Henzinger, in:, 35th Conference on Neural Information Processing Systems, 2021.
View | Files available | DOI | Download Published Version (ext.) | arXiv
 
[10]
2021 | Conference Paper | IST-REx-ID: 10666 | OA
Adversarial training is not ready for robot learning
M. Lechner, R. Hasani, R. Grosu, D. Rus, T.A. Henzinger, in:, 2021 IEEE International Conference on Robotics and Automation, 2021, pp. 4140–4147.
View | Files available | DOI | Download None (ext.) | arXiv
 
[9]
2020 | Conference Paper | IST-REx-ID: 10673 | OA
A natural lottery ticket winner: Reinforcement learning with ordinary neural circuits
R. Hasani, M. Lechner, A. Amini, D. Rus, R. Grosu, in:, Proceedings of the 37th International Conference on Machine Learning, 2020, pp. 4082–4093.
View | Files available | Download Published Version (ext.)
 
[8]
2020 | Conference Paper | IST-REx-ID: 10672 | OA
Learning representations for binary-classification without backpropagation
M. Lechner, in:, 8th International Conference on Learning Representations, ICLR, 2020.
View | Files available | Download Published Version (ext.)
 
[7]
2020 | Conference Paper | IST-REx-ID: 7808 | OA
How many bits does it take to quantize your neural network?
M. Giacobbe, T.A. Henzinger, M. Lechner, in:, International Conference on Tools and Algorithms for the Construction and Analysis of Systems, Springer Nature, 2020, pp. 79–97.
View | Files available | DOI
 
[6]
2020 | Conference Paper | IST-REx-ID: 8194 | OA
An SMT theory of fixed-point arithmetic
M. Baranowski, S. He, M. Lechner, T.S. Nguyen, Z. Rakamarić, in:, Automated Reasoning, Springer Nature, 2020, pp. 13–31.
View | DOI | Download Published Version (ext.)
 
[5]
2020 | Journal Article | IST-REx-ID: 8679
Neural circuit policies enabling auditable autonomy
M. Lechner, R. Hasani, A. Amini, T.A. Henzinger, D. Rus, R. Grosu, Nature Machine Intelligence 2 (2020) 642–652.
View | Files available | DOI
 
[4]
2020 | Conference Paper | IST-REx-ID: 8704 | OA
Gershgorin loss stabilizes the recurrent neural network compartment of an end-to-end robot learning scheme
M. Lechner, R. Hasani, D. Rus, R. Grosu, in:, Proceedings - IEEE International Conference on Robotics and Automation, IEEE, 2020, pp. 5446–5452.
View | Files available | DOI
 
[3]
2020 | Conference Paper | IST-REx-ID: 9103 | OA
Lagrangian reachtubes: The next generation
S. Gruenbacher, J. Cyranka, M. Lechner, M.A. Islam, S.A. Smolka, R. Grosu, in:, Proceedings of the 59th IEEE Conference on Decision and Control, IEEE, 2020, pp. 1556–1563.
View | DOI | Download Preprint (ext.) | arXiv
 
[2]
2019 | Conference Paper | IST-REx-ID: 6888 | OA
Designing worm-inspired neural networks for interpretable robotic control
M. Lechner, R. Hasani, M. Zimmer, T.A. Henzinger, R. Grosu, in:, Proceedings - IEEE International Conference on Robotics and Automation, IEEE, 2019.
View | Files available | DOI
 
[1]
2019 | Conference Paper | IST-REx-ID: 6985 | OA
Response characterization for auditing cell dynamics in long short-term memory networks
R. Hasani, A. Amini, M. Lechner, F. Naser, R. Grosu, D. Rus, in:, Proceedings of the International Joint Conference on Neural Networks, IEEE, 2019.
View | DOI | Download Preprint (ext.) | arXiv
 
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