6 Publications

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[6]
2020 | Conference Paper | IST-REx-ID: 7808 | OA
Giacobbe, Mirco, Thomas A Henzinger, and Mathias Lechner. “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, 12079:79–97. Springer Nature, 2020. https://doi.org/10.1007/978-3-030-45237-7_5.
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[5]
2020 | Conference Paper | IST-REx-ID: 8194 | OA
Baranowski, Marek, Shaobo He, Mathias Lechner, Thanh Son Nguyen, and Zvonimir Rakamarić. “An SMT Theory of Fixed-Point Arithmetic.” In Automated Reasoning, 12166:13–31. Springer Nature, 2020. https://doi.org/10.1007/978-3-030-51074-9_2.
View | DOI | Download Published Version (ext.)
 
[4]
2020 | Journal Article | IST-REx-ID: 8679
Lechner, Mathias, Ramin Hasani, Alexander Amini, Thomas A Henzinger, Daniela Rus, and Radu Grosu. “Neural Circuit Policies Enabling Auditable Autonomy.” Nature Machine Intelligence 2 (2020): 642–52. https://doi.org/10.1038/s42256-020-00237-3.
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[3]
2020 | Conference Paper | IST-REx-ID: 8704
Lechner, Mathias, Ramin Hasani, Daniela Rus, and Radu Grosu. “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. IEEE, 2020. https://doi.org/10.1109/ICRA40945.2020.9196608.
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[2]
2019 | Conference Paper | IST-REx-ID: 6985 | OA
Hasani, Ramin, Alexander Amini, Mathias Lechner, Felix Naser, Radu Grosu, and Daniela Rus. “Response Characterization for Auditing Cell Dynamics in Long Short-Term Memory Networks.” In Proceedings of the International Joint Conference on Neural Networks. IEEE, 2019. https://doi.org/10.1109/ijcnn.2019.8851954.
View | DOI | Download Preprint (ext.) | arXiv
 
[1]
2019 | Conference Paper | IST-REx-ID: 6888 | OA
Lechner, Mathias, Ramin Hasani, Manuel Zimmer, Thomas A Henzinger, and Radu Grosu. “Designing Worm-Inspired Neural Networks for Interpretable Robotic Control.” In Proceedings - IEEE International Conference on Robotics and Automation, Vol. 2019–May. IEEE, 2019. https://doi.org/10.1109/icra.2019.8793840.
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6 Publications

Mark all

[6]
2020 | Conference Paper | IST-REx-ID: 7808 | OA
Giacobbe, Mirco, Thomas A Henzinger, and Mathias Lechner. “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, 12079:79–97. Springer Nature, 2020. https://doi.org/10.1007/978-3-030-45237-7_5.
View | Files available | DOI
 
[5]
2020 | Conference Paper | IST-REx-ID: 8194 | OA
Baranowski, Marek, Shaobo He, Mathias Lechner, Thanh Son Nguyen, and Zvonimir Rakamarić. “An SMT Theory of Fixed-Point Arithmetic.” In Automated Reasoning, 12166:13–31. Springer Nature, 2020. https://doi.org/10.1007/978-3-030-51074-9_2.
View | DOI | Download Published Version (ext.)
 
[4]
2020 | Journal Article | IST-REx-ID: 8679
Lechner, Mathias, Ramin Hasani, Alexander Amini, Thomas A Henzinger, Daniela Rus, and Radu Grosu. “Neural Circuit Policies Enabling Auditable Autonomy.” Nature Machine Intelligence 2 (2020): 642–52. https://doi.org/10.1038/s42256-020-00237-3.
View | Files available | DOI
 
[3]
2020 | Conference Paper | IST-REx-ID: 8704
Lechner, Mathias, Ramin Hasani, Daniela Rus, and Radu Grosu. “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. IEEE, 2020. https://doi.org/10.1109/ICRA40945.2020.9196608.
View | DOI
 
[2]
2019 | Conference Paper | IST-REx-ID: 6985 | OA
Hasani, Ramin, Alexander Amini, Mathias Lechner, Felix Naser, Radu Grosu, and Daniela Rus. “Response Characterization for Auditing Cell Dynamics in Long Short-Term Memory Networks.” In Proceedings of the International Joint Conference on Neural Networks. IEEE, 2019. https://doi.org/10.1109/ijcnn.2019.8851954.
View | DOI | Download Preprint (ext.) | arXiv
 
[1]
2019 | Conference Paper | IST-REx-ID: 6888 | OA
Lechner, Mathias, Ramin Hasani, Manuel Zimmer, Thomas A Henzinger, and Radu Grosu. “Designing Worm-Inspired Neural Networks for Interpretable Robotic Control.” In Proceedings - IEEE International Conference on Robotics and Automation, Vol. 2019–May. IEEE, 2019. https://doi.org/10.1109/icra.2019.8793840.
View | Files available | DOI
 

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