Mathias Lechner

Graduate School
Henzinger_Thomas Group

21 Publications

Mark all

[21]
2022 | Thesis | IST-REx-ID: 11362 | OA
Learning verifiable representations
M. Lechner, Learning Verifiable Representations, ISTA, 2022.
View | Files available | DOI
 
[20]
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
 
[19]
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
 
[18]
2022 | Journal Article | IST-REx-ID: 12147 | OA
Closed-form continuous-time neural networks
Hasani, Ramin, Closed-form continuous-time neural networks. Nature Machine Intelligence 4 (11). 2022
View | Files available | DOI | arXiv
 
[17]
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.)
 
[16]
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
 
[15]
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
 
[14]
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
 
[13]
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
 
[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: 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
 
[10]
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
 
[9]
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.)
 
[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: 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.)
 
[6]
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
 
[5]
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
 
[4]
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
 
[3]
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
 
[2]
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
 
[1]
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
 
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21 Publications

Mark all

[21]
2022 | Thesis | IST-REx-ID: 11362 | OA
Learning verifiable representations
M. Lechner, Learning Verifiable Representations, ISTA, 2022.
View | Files available | DOI
 
[20]
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
 
[19]
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
 
[18]
2022 | Journal Article | IST-REx-ID: 12147 | OA
Closed-form continuous-time neural networks
Hasani, Ramin, Closed-form continuous-time neural networks. Nature Machine Intelligence 4 (11). 2022
View | Files available | DOI | arXiv
 
[17]
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.)
 
[16]
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
 
[15]
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
 
[14]
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
 
[13]
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
 
[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: 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
 
[10]
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
 
[9]
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.)
 
[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: 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.)
 
[6]
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
 
[5]
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
 
[4]
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
 
[3]
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
 
[2]
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
 
[1]
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
 
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