Mathias Lechner

1.3k total citations
22 papers, 458 citations indexed

About

Mathias Lechner is a scholar working on Artificial Intelligence, Statistical and Nonlinear Physics and Control and Systems Engineering. According to data from OpenAlex, Mathias Lechner has authored 22 papers receiving a total of 458 indexed citations (citations by other indexed papers that have themselves been cited), including 19 papers in Artificial Intelligence, 8 papers in Statistical and Nonlinear Physics and 7 papers in Control and Systems Engineering. Recurrent topics in Mathias Lechner's work include Adversarial Robustness in Machine Learning (8 papers), Model Reduction and Neural Networks (8 papers) and Neural Networks and Applications (7 papers). Mathias Lechner is often cited by papers focused on Adversarial Robustness in Machine Learning (8 papers), Model Reduction and Neural Networks (8 papers) and Neural Networks and Applications (7 papers). Mathias Lechner collaborates with scholars based in Austria, United States and Denmark. Mathias Lechner's co-authors include Ramin Hasani, Daniela Rus, Alexander Amini, Radu Grosu, Thomas A. Henzinger, Aaron Ray, Max Tschaikowski, Gerald Teschl, Krishnendu Chatterjee and Manuel Zimmer and has published in prestigious journals such as Science Robotics, IEEE Robotics and Automation Letters and Nature Machine Intelligence.

In The Last Decade

Mathias Lechner

22 papers receiving 446 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Mathias Lechner Austria 9 202 97 87 70 56 22 458
Gangquan Si China 15 140 0.7× 280 2.9× 122 1.4× 51 0.7× 72 1.3× 45 555
K. Ramkumar India 13 101 0.5× 106 1.1× 274 3.1× 94 1.3× 54 1.0× 73 711
HE Guo-guang China 11 195 1.0× 53 0.5× 108 1.2× 122 1.7× 21 0.4× 78 428
Boudour Ammar Tunisia 10 287 1.4× 172 1.8× 72 0.8× 48 0.7× 51 0.9× 33 479
Mustafa Poyraz Türkiye 14 159 0.8× 180 1.9× 208 2.4× 76 1.1× 92 1.6× 33 649
Timothée Lesort France 5 313 1.5× 36 0.4× 76 0.9× 41 0.6× 136 2.4× 7 459
Vincent Gripon France 13 327 1.6× 197 2.0× 28 0.3× 71 1.0× 149 2.7× 54 599
Giovanni De Magistris Japan 12 181 0.9× 27 0.3× 221 2.5× 33 0.5× 71 1.3× 29 575
Michiel Hermans Belgium 12 499 2.5× 306 3.2× 72 0.8× 96 1.4× 82 1.5× 21 758
Jo-Anne Ting United States 12 227 1.1× 36 0.4× 153 1.8× 66 0.9× 149 2.7× 17 504

Countries citing papers authored by Mathias Lechner

Since Specialization
Citations

This map shows the geographic impact of Mathias Lechner's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Mathias Lechner with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mathias Lechner more than expected).

Fields of papers citing papers by Mathias Lechner

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Mathias Lechner. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Mathias Lechner. The network helps show where Mathias Lechner may publish in the future.

Co-authorship network of co-authors of Mathias Lechner

This figure shows the co-authorship network connecting the top 25 collaborators of Mathias Lechner. A scholar is included among the top collaborators of Mathias Lechner based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Mathias Lechner. Mathias Lechner is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Hasani, Ramin, et al.. (2023). Robust flight navigation out of distribution with liquid neural networks. Science Robotics. 8(77). eadc8892–eadc8892. 33 indexed citations
2.
Lechner, Mathias, et al.. (2023). Quantization-Aware Interval Bound Propagation for Training Certifiably Robust Quantized Neural Networks. Proceedings of the AAAI Conference on Artificial Intelligence. 37(12). 14964–14973. 1 indexed citations
3.
Lechner, Mathias, et al.. (2023). Learning Control Policies for Stochastic Systems with Reach-Avoid Guarantees. Proceedings of the AAAI Conference on Artificial Intelligence. 37(10). 11926–11935. 8 indexed citations
4.
Lechner, Mathias, Alexander Amini, Daniela Rus, & Thomas A. Henzinger. (2023). Revisiting the Adversarial Robustness-Accuracy Tradeoff in Robot Learning. IEEE Robotics and Automation Letters. 8(3). 1595–1602. 4 indexed citations
5.
Lechner, Mathias, et al.. (2023). Infrastructure-based End-to-End Learning and Prevention of Driver Failure. 3576–3583. 1 indexed citations
6.
Liu, Chao, et al.. (2023). Towards Cooperative Flight Control Using Visual-Attention. 6334–6341. 2 indexed citations
7.
Hasani, Ramin, Mathias Lechner, Alexander Amini, et al.. (2022). Closed-form continuous-time neural networks. Nature Machine Intelligence. 4(11). 992–1003. 69 indexed citations
8.
Lechner, Mathias, et al.. (2022). Latent Imagination Facilitates Zero-Shot Transfer in Autonomous Racing. 2022 International Conference on Robotics and Automation (ICRA). 7513–7520. 21 indexed citations
9.
Lechner, Mathias, et al.. (2022). Stability Verification in Stochastic Control Systems via Neural Network Supermartingales. Proceedings of the AAAI Conference on Artificial Intelligence. 36(7). 7326–7336. 10 indexed citations
10.
Lechner, Mathias, Ramin Hasani, Daniela Rus, et al.. (2022). GoTube: Scalable Statistical Verification of Continuous-Depth Models. Proceedings of the AAAI Conference on Artificial Intelligence. 36(6). 6755–6764. 1 indexed citations
11.
Lechner, Mathias, et al.. (2021). Infinite Time Horizon Safety of Bayesian Neural Networks. arXiv (Cornell University). 34. 1 indexed citations
12.
Hasani, Ramin, Mathias Lechner, Alexander Amini, Daniela Rus, & Radu Grosu. (2021). Liquid Time-constant Networks. Proceedings of the AAAI Conference on Artificial Intelligence. 35(9). 7657–7666. 119 indexed citations
13.
Henzinger, Thomas A., et al.. (2021). Scalable Verification of Quantized Neural Networks. Proceedings of the AAAI Conference on Artificial Intelligence. 35(5). 3787–3795. 12 indexed citations
14.
Hasani, Ramin, et al.. (2021). On the Verification of Neural ODEs with Stochastic Guarantees. Proceedings of the AAAI Conference on Artificial Intelligence. 35(13). 11525–11535. 5 indexed citations
15.
Lechner, Mathias & Ramin Hasani. (2020). Learning Long-Term Dependencies in Irregularly-Sampled Time Series. Neural Information Processing Systems. 33. 4 indexed citations
16.
Lechner, Mathias, Ramin Hasani, Daniela Rus, & Radu Grosu. (2020). Gershgorin Loss Stabilizes the Recurrent Neural Network Compartment of an End-to-end Robot Learning Scheme. reposiTUm (TU Wien). 8 indexed citations
17.
Hasani, Ramin, et al.. (2020). On The Verification of Neural ODEs with Stochastic Guarantees. arXiv (Cornell University). 35(13). 11525–11535. 1 indexed citations
18.
Hasani, Ramin, Mathias Lechner, Alexander Amini, Daniela Rus, & Radu Grosu. (2020). The Natural Lottery Ticket Winner: Reinforcement Learning with Ordinary Neural Circuits. reposiTUm (TU Wien). 1. 4082–4093. 6 indexed citations
19.
Lechner, Mathias, Ramin Hasani, Alexander Amini, et al.. (2020). Neural circuit policies enabling auditable autonomy. Nature Machine Intelligence. 2(10). 642–652. 130 indexed citations
20.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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