--- _id: '11366' abstract: - lang: eng text: "Adversarial training (i.e., training on adversarially perturbed input data) is a well-studied method for making neural networks robust to potential adversarial attacks during inference. However, the improved robustness does not\r\ncome for free but rather is accompanied by a decrease in overall model accuracy and performance. Recent work has shown that, in practical robot learning applications, the effects of adversarial training do not pose a fair trade-off\r\nbut inflict a net loss when measured in holistic robot performance. This work revisits the robustness-accuracy trade-off in robot learning by systematically analyzing if recent advances in robust training methods and theory in\r\nconjunction with adversarial robot learning can make adversarial training suitable for real-world robot applications. We evaluate a wide variety of robot learning tasks ranging from autonomous driving in a high-fidelity environment\r\namenable to sim-to-real deployment, to mobile robot gesture recognition. Our results demonstrate that, while these techniques make incremental improvements on the trade-off on a relative scale, the negative side-effects caused by\r\nadversarial training still outweigh the improvements by an order of magnitude. We conclude that more substantial advances in robust learning methods are necessary before they can benefit robot learning tasks in practice." acknowledgement: "This work was supported in parts by the ERC-2020-AdG 101020093, National Science Foundation (NSF), and JP\r\nMorgan Graduate Fellowships. We thank Christoph Lampert for inspiring this work.\r\n" article_number: '2204.07373' article_processing_charge: No author: - first_name: Mathias full_name: Lechner, Mathias id: 3DC22916-F248-11E8-B48F-1D18A9856A87 last_name: Lechner - first_name: Alexander full_name: Amini, Alexander last_name: Amini - first_name: Daniela full_name: Rus, Daniela last_name: Rus - first_name: Thomas A full_name: Henzinger, Thomas A id: 40876CD8-F248-11E8-B48F-1D18A9856A87 last_name: Henzinger orcid: 0000-0002-2985-7724 citation: ama: Lechner M, Amini A, Rus D, Henzinger TA. Revisiting the adversarial robustness-accuracy tradeoff in robot learning. arXiv. doi:10.48550/arXiv.2204.07373 apa: 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 chicago: Lechner, Mathias, Alexander Amini, Daniela Rus, and Thomas A Henzinger. “Revisiting the Adversarial Robustness-Accuracy Tradeoff in Robot Learning.” ArXiv, n.d. https://doi.org/10.48550/arXiv.2204.07373. ieee: M. Lechner, A. Amini, D. Rus, and T. A. Henzinger, “Revisiting the adversarial robustness-accuracy tradeoff in robot learning,” arXiv. . ista: Lechner M, Amini A, Rus D, Henzinger TA. Revisiting the adversarial robustness-accuracy tradeoff in robot learning. arXiv, 2204.07373. mla: Lechner, Mathias, et al. “Revisiting the Adversarial Robustness-Accuracy Tradeoff in Robot Learning.” ArXiv, 2204.07373, doi:10.48550/arXiv.2204.07373. short: M. Lechner, A. Amini, D. Rus, T.A. Henzinger, ArXiv (n.d.). date_created: 2022-05-12T13:20:17Z date_published: 2022-04-15T00:00:00Z date_updated: 2023-08-01T13:36:50Z day: '15' department: - _id: ToHe doi: 10.48550/arXiv.2204.07373 ec_funded: 1 external_id: arxiv: - '2204.07373' language: - iso: eng main_file_link: - open_access: '1' url: https://doi.org/10.48550/arXiv.2204.07373 month: '04' oa: 1 oa_version: Preprint project: - _id: 62781420-2b32-11ec-9570-8d9b63373d4d call_identifier: H2020 grant_number: '101020093' name: Vigilant Algorithmic Monitoring of Software publication: arXiv publication_status: submitted related_material: record: - id: '11362' relation: dissertation_contains status: public - id: '12704' relation: later_version status: public status: public title: Revisiting the adversarial robustness-accuracy tradeoff in robot learning type: preprint user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2022' ... --- _id: '10891' abstract: - lang: eng text: We present a formal framework for the online black-box monitoring of software using monitors with quantitative verdict functions. Quantitative verdict functions have several advantages. First, quantitative monitors can be approximate, i.e., the value of the verdict function does not need to correspond exactly to the value of the property under observation. Second, quantitative monitors can be quantified universally, i.e., for every possible observed behavior, the monitor tries to make the best effort to estimate the value of the property under observation. Third, quantitative monitors can watch boolean as well as quantitative properties, such as average response time. Fourth, quantitative monitors can use non-finite-state resources, such as counters. As a consequence, quantitative monitors can be compared according to how many resources they use (e.g., the number of counters) and how precisely they approximate the property under observation. This allows for a rich spectrum of cost-precision trade-offs in monitoring software. acknowledgement: The formal framework for quantitative monitoring which is presented in this invited talk was defined jointly with N. Ege Saraç at LICS 2021. This work was supported in part by the Wittgenstein Award Z211-N23 of the Austrian Science Fund. article_processing_charge: No author: - first_name: Thomas A full_name: Henzinger, Thomas A id: 40876CD8-F248-11E8-B48F-1D18A9856A87 last_name: Henzinger orcid: 0000-0002-2985-7724 citation: ama: 'Henzinger TA. Quantitative monitoring of software. In: Software Verification. Vol 13124. LNCS. Springer Nature; 2022:3-6. doi:10.1007/978-3-030-95561-8_1' apa: 'Henzinger, T. A. (2022). Quantitative monitoring of software. In Software Verification (Vol. 13124, pp. 3–6). New Haven, CT, United States: Springer Nature. https://doi.org/10.1007/978-3-030-95561-8_1' chicago: Henzinger, Thomas A. “Quantitative Monitoring of Software.” In Software Verification, 13124:3–6. LNCS. Springer Nature, 2022. https://doi.org/10.1007/978-3-030-95561-8_1. ieee: T. A. Henzinger, “Quantitative monitoring of software,” in Software Verification, New Haven, CT, United States, 2022, vol. 13124, pp. 3–6. ista: 'Henzinger TA. 2022. Quantitative monitoring of software. Software Verification. NSV: Numerical Software VerificationLNCS vol. 13124, 3–6.' mla: Henzinger, Thomas A. “Quantitative Monitoring of Software.” Software Verification, vol. 13124, Springer Nature, 2022, pp. 3–6, doi:10.1007/978-3-030-95561-8_1. short: T.A. Henzinger, in:, Software Verification, Springer Nature, 2022, pp. 3–6. conference: end_date: 2021-10-19 location: New Haven, CT, United States name: 'NSV: Numerical Software Verification' start_date: 2021-10-18 date_created: 2022-03-20T23:01:40Z date_published: 2022-02-22T00:00:00Z date_updated: 2023-08-03T06:11:55Z day: '22' department: - _id: ToHe doi: 10.1007/978-3-030-95561-8_1 external_id: isi: - '000771713200001' intvolume: ' 13124' isi: 1 language: - iso: eng month: '02' oa_version: None page: 3-6 project: - _id: 25F42A32-B435-11E9-9278-68D0E5697425 call_identifier: FWF grant_number: Z211 name: The Wittgenstein Prize publication: Software Verification publication_identifier: eissn: - 1611-3349 isbn: - '9783030955601' issn: - 0302-9743 publication_status: published publisher: Springer Nature quality_controlled: '1' scopus_import: '1' series_title: LNCS status: public title: Quantitative monitoring of software type: conference user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8 volume: 13124 year: '2022' ... --- _id: '11355' abstract: - lang: eng text: "Contract-based design is a promising methodology for taming the complexity of developing sophisticated systems. A formal contract distinguishes between assumptions, which are constraints that the designer of a component puts on the environments in which the component can be used safely, and guarantees, which are promises that the designer asks from the team that implements the component. A theory of formal contracts can be formalized as an interface theory, which supports the composition and refinement of both assumptions and guarantees.\r\nAlthough there is a rich landscape of contract-based design methods that address functional and extra-functional properties, we present the first interface theory that is designed for ensuring system-wide security properties. Our framework provides a refinement relation and a composition operation that support both incremental design and independent implementability. We develop our theory for both stateless and stateful interfaces. We illustrate the applicability of our framework with an example inspired from the automotive domain." acknowledgement: This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 956123 and was funded in part by the FWF project W1255-N23 and by the ERC-2020-AdG 101020093. alternative_title: - LNCS article_processing_charge: No author: - first_name: Ezio full_name: Bartocci, Ezio last_name: Bartocci - first_name: Thomas full_name: Ferrere, Thomas id: 40960E6E-F248-11E8-B48F-1D18A9856A87 last_name: Ferrere orcid: 0000-0001-5199-3143 - first_name: Thomas A full_name: Henzinger, Thomas A id: 40876CD8-F248-11E8-B48F-1D18A9856A87 last_name: Henzinger orcid: 0000-0002-2985-7724 - first_name: Dejan full_name: Nickovic, Dejan id: 41BCEE5C-F248-11E8-B48F-1D18A9856A87 last_name: Nickovic - first_name: Ana Oliveira full_name: Da Costa, Ana Oliveira last_name: Da Costa citation: ama: 'Bartocci E, Ferrere T, Henzinger TA, Nickovic D, Da Costa AO. Information-flow interfaces. In: Fundamental Approaches to Software Engineering. Vol 13241. Springer Nature; 2022:3-22. doi:10.1007/978-3-030-99429-7_1' apa: 'Bartocci, E., Ferrere, T., Henzinger, T. A., Nickovic, D., & Da Costa, A. O. (2022). Information-flow interfaces. In Fundamental Approaches to Software Engineering (Vol. 13241, pp. 3–22). Munich, Germany: Springer Nature. https://doi.org/10.1007/978-3-030-99429-7_1' chicago: Bartocci, Ezio, Thomas Ferrere, Thomas A Henzinger, Dejan Nickovic, and Ana Oliveira Da Costa. “Information-Flow Interfaces.” In Fundamental Approaches to Software Engineering, 13241:3–22. Springer Nature, 2022. https://doi.org/10.1007/978-3-030-99429-7_1. ieee: E. Bartocci, T. Ferrere, T. A. Henzinger, D. Nickovic, and A. O. Da Costa, “Information-flow interfaces,” in Fundamental Approaches to Software Engineering, Munich, Germany, 2022, vol. 13241, pp. 3–22. ista: 'Bartocci E, Ferrere T, Henzinger TA, Nickovic D, Da Costa AO. 2022. Information-flow interfaces. Fundamental Approaches to Software Engineering. FASE: Fundamental Approaches to Software Engineering, LNCS, vol. 13241, 3–22.' mla: Bartocci, Ezio, et al. “Information-Flow Interfaces.” Fundamental Approaches to Software Engineering, vol. 13241, Springer Nature, 2022, pp. 3–22, doi:10.1007/978-3-030-99429-7_1. short: E. Bartocci, T. Ferrere, T.A. Henzinger, D. Nickovic, A.O. Da Costa, in:, Fundamental Approaches to Software Engineering, Springer Nature, 2022, pp. 3–22. conference: end_date: 2022-04-07 location: Munich, Germany name: 'FASE: Fundamental Approaches to Software Engineering' start_date: 2022-04-02 date_created: 2022-05-08T22:01:44Z date_published: 2022-03-29T00:00:00Z date_updated: 2023-08-03T07:03:40Z day: '29' ddc: - '000' department: - _id: ToHe doi: 10.1007/978-3-030-99429-7_1 ec_funded: 1 external_id: isi: - '000782393600001' file: - access_level: open_access checksum: 7f6f860b20b8de2a249e9c1b4eee15cf content_type: application/pdf creator: dernst date_created: 2022-05-09T06:52:44Z date_updated: 2022-05-09T06:52:44Z file_id: '11357' file_name: 2022_LNCS_Bartocci.pdf file_size: 479146 relation: main_file success: 1 file_date_updated: 2022-05-09T06:52:44Z has_accepted_license: '1' intvolume: ' 13241' isi: 1 language: - iso: eng license: https://creativecommons.org/licenses/by/4.0/ month: '03' oa: 1 oa_version: Published Version page: 3-22 project: - _id: 62781420-2b32-11ec-9570-8d9b63373d4d call_identifier: H2020 grant_number: '101020093' name: Vigilant Algorithmic Monitoring of Software publication: Fundamental Approaches to Software Engineering publication_identifier: eissn: - 1611-3349 isbn: - '9783030994280' issn: - 0302-9743 publication_status: published publisher: Springer Nature quality_controlled: '1' scopus_import: '1' status: public title: Information-flow interfaces tmp: image: /images/cc_by.png legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) short: CC BY (4.0) type: conference user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8 volume: 13241 year: '2022' ... --- _id: '11775' abstract: - lang: eng text: 'Quantitative monitoring can be universal and approximate: For every finite sequence of observations, the specification provides a value and the monitor outputs a best-effort approximation of it. The quality of the approximation may depend on the resources that are available to the monitor. By taking to the limit the sequences of specification values and monitor outputs, we obtain precision-resource trade-offs also for limit monitoring. This paper provides a formal framework for studying such trade-offs using an abstract interpretation for monitors: For each natural number n, the aggregate semantics of a monitor at time n is an equivalence relation over all sequences of at most n observations so that two equivalent sequences are indistinguishable to the monitor and thus mapped to the same output. This abstract interpretation of quantitative monitors allows us to measure the number of equivalence classes (or “resource use”) that is necessary for a certain precision up to a certain time, or at any time. Our framework offers several insights. For example, we identify a family of specifications for which any resource-optimal exact limit monitor is independent of any error permitted over finite traces. Moreover, we present a specification for which any resource-optimal approximate limit monitor does not minimize its resource use at any time. ' acknowledgement: We thank the anonymous reviewers for their helpful comments. This work was supported in part by the ERC-2020-AdG 101020093. alternative_title: - LNCS article_processing_charge: Yes author: - first_name: Thomas A full_name: Henzinger, Thomas A id: 40876CD8-F248-11E8-B48F-1D18A9856A87 last_name: Henzinger orcid: 0000-0002-2985-7724 - first_name: Nicolas Adrien full_name: Mazzocchi, Nicolas Adrien id: b26baa86-3308-11ec-87b0-8990f34baa85 last_name: Mazzocchi - first_name: Naci E full_name: Sarac, Naci E id: 8C6B42F8-C8E6-11E9-A03A-F2DCE5697425 last_name: Sarac citation: ama: 'Henzinger TA, Mazzocchi NA, Sarac NE. Abstract monitors for quantitative specifications. In: 22nd International Conference on Runtime Verification. Vol 13498. Springer Nature; 2022:200-220. doi:10.1007/978-3-031-17196-3_11' apa: 'Henzinger, T. A., Mazzocchi, N. A., & Sarac, N. E. (2022). Abstract monitors for quantitative specifications. In 22nd International Conference on Runtime Verification (Vol. 13498, pp. 200–220). Tbilisi, Georgia: Springer Nature. https://doi.org/10.1007/978-3-031-17196-3_11' chicago: Henzinger, Thomas A, Nicolas Adrien Mazzocchi, and Naci E Sarac. “Abstract Monitors for Quantitative Specifications.” In 22nd International Conference on Runtime Verification, 13498:200–220. Springer Nature, 2022. https://doi.org/10.1007/978-3-031-17196-3_11. ieee: T. A. Henzinger, N. A. Mazzocchi, and N. E. Sarac, “Abstract monitors for quantitative specifications,” in 22nd International Conference on Runtime Verification, Tbilisi, Georgia, 2022, vol. 13498, pp. 200–220. ista: 'Henzinger TA, Mazzocchi NA, Sarac NE. 2022. Abstract monitors for quantitative specifications. 22nd International Conference on Runtime Verification. RV: Runtime Verification, LNCS, vol. 13498, 200–220.' mla: Henzinger, Thomas A., et al. “Abstract Monitors for Quantitative Specifications.” 22nd International Conference on Runtime Verification, vol. 13498, Springer Nature, 2022, pp. 200–20, doi:10.1007/978-3-031-17196-3_11. short: T.A. Henzinger, N.A. Mazzocchi, N.E. Sarac, in:, 22nd International Conference on Runtime Verification, Springer Nature, 2022, pp. 200–220. conference: end_date: 2022-09-30 location: Tbilisi, Georgia name: 'RV: Runtime Verification' start_date: 2022-09-28 date_created: 2022-08-08T17:09:09Z date_published: 2022-09-23T00:00:00Z date_updated: 2023-08-03T13:38:46Z day: '23' ddc: - '000' department: - _id: GradSch - _id: ToHe doi: 10.1007/978-3-031-17196-3_11 ec_funded: 1 external_id: isi: - '000866539700011' file: - access_level: open_access checksum: 05c7dcfbb9053a98f46441fb2eccb213 content_type: application/pdf creator: dernst date_created: 2023-01-20T07:34:50Z date_updated: 2023-01-20T07:34:50Z file_id: '12317' file_name: 2022_LNCS_RV_Henzinger.pdf file_size: 477110 relation: main_file success: 1 file_date_updated: 2023-01-20T07:34:50Z has_accepted_license: '1' intvolume: ' 13498' isi: 1 language: - iso: eng month: '09' oa: 1 oa_version: Published Version page: 200-220 project: - _id: 62781420-2b32-11ec-9570-8d9b63373d4d call_identifier: H2020 grant_number: '101020093' name: Vigilant Algorithmic Monitoring of Software publication: 22nd International Conference on Runtime Verification publication_identifier: issn: - 0302-9743 publication_status: published publisher: Springer Nature quality_controlled: '1' scopus_import: '1' status: public title: Abstract monitors for quantitative specifications tmp: image: /images/cc_by.png legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) short: CC BY (4.0) type: conference user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8 volume: 13498 year: '2022' ... --- _id: '12147' abstract: - lang: eng text: Continuous-time neural networks are a class of machine learning systems that can tackle representation learning on spatiotemporal decision-making tasks. These models are typically represented by continuous differential equations. However, their expressive power when they are deployed on computers is bottlenecked by numerical differential equation solvers. This limitation has notably slowed down the scaling and understanding of numerous natural physical phenomena such as the dynamics of nervous systems. Ideally, we would circumvent this bottleneck by solving the given dynamical system in closed form. This is known to be intractable in general. Here, we show that it is possible to closely approximate the interaction between neurons and synapses—the building blocks of natural and artificial neural networks—constructed by liquid time-constant networks efficiently in closed form. To this end, we compute a tightly bounded approximation of the solution of an integral appearing in liquid time-constant dynamics that has had no known closed-form solution so far. This closed-form solution impacts the design of continuous-time and continuous-depth neural models. For instance, since time appears explicitly in closed form, the formulation relaxes the need for complex numerical solvers. Consequently, we obtain models that are between one and five orders of magnitude faster in training and inference compared with differential equation-based counterparts. More importantly, in contrast to ordinary differential equation-based continuous networks, closed-form networks can scale remarkably well compared with other deep learning instances. Lastly, as these models are derived from liquid networks, they show good performance in time-series modelling compared with advanced recurrent neural network models. acknowledgement: This research was supported in part by the AI2050 program at Schmidt Futures (grant G-22-63172), the Boeing Company, and the United States Air Force Research Laboratory and the United States Air Force Artificial Intelligence Accelerator and was accomplished under cooperative agreement number FA8750-19-2-1000. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the United States Air Force or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes, notwithstanding any copyright notation herein. This work was further supported by The Boeing Company and Office of Naval Research grant N00014-18-1-2830. M.T. is supported by the Poul Due Jensen Foundation, grant 883901. M.L. was supported in part by the Austrian Science Fund under grant Z211-N23 (Wittgenstein Award). A.A. was supported by the National Science Foundation Graduate Research Fellowship Program. We thank T.-H. Wang, P. Kao, M. Chahine, W. Xiao, X. Li, L. Yin and Y. Ben for useful suggestions and for testing of CfC models to confirm the results across other domains. article_processing_charge: No article_type: original author: - first_name: Ramin full_name: Hasani, Ramin last_name: Hasani - first_name: Mathias full_name: Lechner, Mathias id: 3DC22916-F248-11E8-B48F-1D18A9856A87 last_name: Lechner - first_name: Alexander full_name: Amini, Alexander last_name: Amini - first_name: Lucas full_name: Liebenwein, Lucas last_name: Liebenwein - first_name: Aaron full_name: Ray, Aaron last_name: Ray - first_name: Max full_name: Tschaikowski, Max last_name: Tschaikowski - first_name: Gerald full_name: Teschl, Gerald last_name: Teschl - first_name: Daniela full_name: Rus, Daniela last_name: Rus citation: ama: Hasani R, Lechner M, Amini A, et al. Closed-form continuous-time neural networks. Nature Machine Intelligence. 2022;4(11):992-1003. doi:10.1038/s42256-022-00556-7 apa: 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 chicago: Hasani, Ramin, Mathias Lechner, Alexander Amini, Lucas Liebenwein, Aaron Ray, Max Tschaikowski, Gerald Teschl, and Daniela Rus. “Closed-Form Continuous-Time Neural Networks.” Nature Machine Intelligence. Springer Nature, 2022. https://doi.org/10.1038/s42256-022-00556-7. ieee: R. Hasani et al., “Closed-form continuous-time neural networks,” Nature Machine Intelligence, vol. 4, no. 11. Springer Nature, pp. 992–1003, 2022. ista: Hasani R, Lechner M, Amini A, Liebenwein L, Ray A, Tschaikowski M, Teschl G, Rus D. 2022. Closed-form continuous-time neural networks. Nature Machine Intelligence. 4(11), 992–1003. mla: Hasani, Ramin, et al. “Closed-Form Continuous-Time Neural Networks.” Nature Machine Intelligence, vol. 4, no. 11, Springer Nature, 2022, pp. 992–1003, doi:10.1038/s42256-022-00556-7. short: R. Hasani, M. Lechner, A. Amini, L. Liebenwein, A. Ray, M. Tschaikowski, G. Teschl, D. Rus, Nature Machine Intelligence 4 (2022) 992–1003. date_created: 2023-01-12T12:07:21Z date_published: 2022-11-15T00:00:00Z date_updated: 2023-08-04T09:00:10Z day: '15' ddc: - '000' department: - _id: ToHe doi: 10.1038/s42256-022-00556-7 external_id: arxiv: - '2106.13898' isi: - '000884215600003' file: - access_level: open_access checksum: b4789122ce04bfb4ac042390f59aaa8b content_type: application/pdf creator: dernst date_created: 2023-01-24T09:49:44Z date_updated: 2023-01-24T09:49:44Z file_id: '12355' file_name: 2022_NatureMachineIntelligence_Hasani.pdf file_size: 3259553 relation: main_file success: 1 file_date_updated: 2023-01-24T09:49:44Z has_accepted_license: '1' intvolume: ' 4' isi: 1 issue: '11' keyword: - Artificial Intelligence - Computer Networks and Communications - Computer Vision and Pattern Recognition - Human-Computer Interaction - Software language: - iso: eng month: '11' oa: 1 oa_version: Published Version page: 992-1003 project: - _id: 25F42A32-B435-11E9-9278-68D0E5697425 call_identifier: FWF grant_number: Z211 name: The Wittgenstein Prize publication: Nature Machine Intelligence publication_identifier: issn: - 2522-5839 publication_status: published publisher: Springer Nature quality_controlled: '1' related_material: link: - relation: erratum url: https://doi.org/10.1038/s42256-022-00597-y scopus_import: '1' status: public title: Closed-form continuous-time neural networks tmp: image: /images/cc_by.png legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) short: CC BY (4.0) type: journal_article user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8 volume: 4 year: '2022' ... --- _id: '11362' abstract: - lang: eng text: "Deep learning has enabled breakthroughs in challenging computing problems and has emerged as the standard problem-solving tool for computer vision and natural language processing tasks.\r\nOne exception to this trend is safety-critical tasks where robustness and resilience requirements contradict the black-box nature of neural networks. \r\nTo deploy deep learning methods for these tasks, it is vital to provide guarantees on neural network agents' safety and robustness criteria. \r\nThis can be achieved by developing formal verification methods to verify the safety and robustness properties of neural networks.\r\n\r\nOur goal is to design, develop and assess safety verification methods for neural networks to improve their reliability and trustworthiness in real-world applications.\r\nThis thesis establishes techniques for the verification of compressed and adversarially trained models as well as the design of novel neural networks for verifiably safe decision-making.\r\n\r\nFirst, we establish the problem of verifying quantized neural networks. Quantization is a technique that trades numerical precision for the computational efficiency of running a neural network and is widely adopted in industry.\r\nWe show that neglecting the reduced precision when verifying a neural network can lead to wrong conclusions about the robustness and safety of the network, highlighting that novel techniques for quantized network verification are necessary. We introduce several bit-exact verification methods explicitly designed for quantized neural networks and experimentally confirm on realistic networks that the network's robustness and other formal properties are affected by the quantization.\r\n\r\nFurthermore, we perform a case study providing evidence that adversarial training, a standard technique for making neural networks more robust, has detrimental effects on the network's performance. This robustness-accuracy tradeoff has been studied before regarding the accuracy obtained on classification datasets where each data point is independent of all other data points. On the other hand, we investigate the tradeoff empirically in robot learning settings where a both, a high accuracy and a high robustness, are desirable.\r\nOur results suggest that the negative side-effects of adversarial training outweigh its robustness benefits in practice.\r\n\r\nFinally, we consider the problem of verifying safety when running a Bayesian neural network policy in a feedback loop with systems over the infinite time horizon. Bayesian neural networks are probabilistic models for learning uncertainties in the data and are therefore often used on robotic and healthcare applications where data is inherently stochastic.\r\nWe introduce a method for recalibrating Bayesian neural networks so that they yield probability distributions over safe decisions only.\r\nOur method learns a safety certificate that guarantees safety over the infinite time horizon to determine which decisions are safe in every possible state of the system.\r\nWe demonstrate the effectiveness of our approach on a series of reinforcement learning benchmarks." alternative_title: - ISTA Thesis article_processing_charge: No author: - first_name: Mathias full_name: Lechner, Mathias id: 3DC22916-F248-11E8-B48F-1D18A9856A87 last_name: Lechner citation: ama: Lechner M. Learning verifiable representations. 2022. doi:10.15479/at:ista:11362 apa: Lechner, M. (2022). Learning verifiable representations. Institute of Science and Technology Austria. https://doi.org/10.15479/at:ista:11362 chicago: Lechner, Mathias. “Learning Verifiable Representations.” Institute of Science and Technology Austria, 2022. https://doi.org/10.15479/at:ista:11362. ieee: M. Lechner, “Learning verifiable representations,” Institute of Science and Technology Austria, 2022. ista: Lechner M. 2022. Learning verifiable representations. Institute of Science and Technology Austria. mla: Lechner, Mathias. Learning Verifiable Representations. Institute of Science and Technology Austria, 2022, doi:10.15479/at:ista:11362. short: M. Lechner, Learning Verifiable Representations, Institute of Science and Technology Austria, 2022. date_created: 2022-05-12T07:14:01Z date_published: 2022-05-12T00:00:00Z date_updated: 2023-08-17T06:58:38Z day: '12' ddc: - '004' degree_awarded: PhD department: - _id: GradSch - _id: ToHe doi: 10.15479/at:ista:11362 ec_funded: 1 file: - access_level: closed checksum: 8eefa9c7c10ca7e1a2ccdd731962a645 content_type: application/zip creator: mlechner date_created: 2022-05-13T12:33:26Z date_updated: 2022-05-13T12:49:00Z file_id: '11378' file_name: src.zip file_size: 13210143 relation: source_file - access_level: open_access checksum: 1b9e1e5a9a83ed9d89dad2f5133dc026 content_type: application/pdf creator: mlechner date_created: 2022-05-16T08:02:28Z date_updated: 2022-05-17T15:19:39Z file_id: '11382' file_name: thesis_main-a2.pdf file_size: 2732536 relation: main_file file_date_updated: 2022-05-17T15:19:39Z has_accepted_license: '1' keyword: - neural networks - verification - machine learning language: - iso: eng license: https://creativecommons.org/licenses/by-nd/4.0/ month: '05' oa: 1 oa_version: Published Version page: '124' project: - _id: 25F42A32-B435-11E9-9278-68D0E5697425 call_identifier: FWF grant_number: Z211 name: The Wittgenstein Prize - _id: 62781420-2b32-11ec-9570-8d9b63373d4d call_identifier: H2020 grant_number: '101020093' name: Vigilant Algorithmic Monitoring of Software publication_identifier: isbn: - 978-3-99078-017-6 publication_status: published publisher: Institute of Science and Technology Austria related_material: record: - id: '10665' relation: part_of_dissertation status: public - id: '10667' relation: part_of_dissertation status: public - id: '11366' relation: part_of_dissertation status: public - id: '7808' relation: part_of_dissertation status: public - id: '10666' relation: part_of_dissertation status: public status: public supervisor: - first_name: Thomas A full_name: Henzinger, Thomas A id: 40876CD8-F248-11E8-B48F-1D18A9856A87 last_name: Henzinger orcid: 0000-0002-2985-7724 title: Learning verifiable representations tmp: image: /image/cc_by_nd.png legal_code_url: https://creativecommons.org/licenses/by-nd/4.0/legalcode name: Creative Commons Attribution-NoDerivatives 4.0 International (CC BY-ND 4.0) short: CC BY-ND (4.0) type: dissertation user_id: 8b945eb4-e2f2-11eb-945a-df72226e66a9 year: '2022' ... --- _id: '12302' abstract: - lang: eng text: 'We propose a novel algorithm to decide the language inclusion between (nondeterministic) Büchi automata, a PSPACE-complete problem. Our approach, like others before, leverage a notion of quasiorder to prune the search for a counterexample by discarding candidates which are subsumed by others for the quasiorder. Discarded candidates are guaranteed to not compromise the completeness of the algorithm. The novelty of our work lies in the quasiorder used to discard candidates. We introduce FORQs (family of right quasiorders) that we obtain by adapting the notion of family of right congruences put forward by Maler and Staiger in 1993. We define a FORQ-based inclusion algorithm which we prove correct and instantiate it for a specific FORQ, called the structural FORQ, induced by the Büchi automaton to the right of the inclusion sign. The resulting implementation, called FORKLIFT, scales up better than the state-of-the-art on a variety of benchmarks including benchmarks from program verification and theorem proving for word combinatorics. Artifact: https://doi.org/10.5281/zenodo.6552870' acknowledgement: This work was partially funded by the ESF Investing in your future, the Madrid regional project S2018/TCS-4339 BLOQUES, the Spanish project PGC2018-102210-B-I00 BOSCO, the Ramón y Cajal fellowship RYC-2016-20281, and the ERC grant PR1001ERC02. alternative_title: - LNCS article_processing_charge: No author: - first_name: Kyveli full_name: Doveri, Kyveli last_name: Doveri - first_name: Pierre full_name: Ganty, Pierre last_name: Ganty - first_name: Nicolas Adrien full_name: Mazzocchi, Nicolas Adrien id: b26baa86-3308-11ec-87b0-8990f34baa85 last_name: Mazzocchi citation: ama: 'Doveri K, Ganty P, Mazzocchi NA. FORQ-based language inclusion formal testing. In: Computer Aided Verification. Vol 13372. Springer Nature; 2022:109-129. doi:10.1007/978-3-031-13188-2_6' apa: 'Doveri, K., Ganty, P., & Mazzocchi, N. A. (2022). FORQ-based language inclusion formal testing. In Computer Aided Verification (Vol. 13372, pp. 109–129). Haifa, Israel: Springer Nature. https://doi.org/10.1007/978-3-031-13188-2_6' chicago: Doveri, Kyveli, Pierre Ganty, and Nicolas Adrien Mazzocchi. “FORQ-Based Language Inclusion Formal Testing.” In Computer Aided Verification, 13372:109–29. Springer Nature, 2022. https://doi.org/10.1007/978-3-031-13188-2_6. ieee: K. Doveri, P. Ganty, and N. A. Mazzocchi, “FORQ-based language inclusion formal testing,” in Computer Aided Verification, Haifa, Israel, 2022, vol. 13372, pp. 109–129. ista: 'Doveri K, Ganty P, Mazzocchi NA. 2022. FORQ-based language inclusion formal testing. Computer Aided Verification. CAV: Computer Aided Verification, LNCS, vol. 13372, 109–129.' mla: Doveri, Kyveli, et al. “FORQ-Based Language Inclusion Formal Testing.” Computer Aided Verification, vol. 13372, Springer Nature, 2022, pp. 109–29, doi:10.1007/978-3-031-13188-2_6. short: K. Doveri, P. Ganty, N.A. Mazzocchi, in:, Computer Aided Verification, Springer Nature, 2022, pp. 109–129. conference: end_date: 2022-08-10 location: Haifa, Israel name: 'CAV: Computer Aided Verification' start_date: 2022-08-07 date_created: 2023-01-16T10:06:31Z date_published: 2022-08-06T00:00:00Z date_updated: 2023-09-05T15:13:36Z day: '06' ddc: - '000' department: - _id: ToHe doi: 10.1007/978-3-031-13188-2_6 ec_funded: 1 external_id: arxiv: - '2207.13549' isi: - '000870310500006' file: - access_level: open_access checksum: edc363b1be5447a09063e115c247918a content_type: application/pdf creator: dernst date_created: 2023-01-30T12:51:02Z date_updated: 2023-01-30T12:51:02Z file_id: '12465' file_name: 2022_LNCS_Doveri.pdf file_size: 497682 relation: main_file success: 1 file_date_updated: 2023-01-30T12:51:02Z has_accepted_license: '1' intvolume: ' 13372' isi: 1 language: - iso: eng month: '08' oa: 1 oa_version: Published Version page: 109-129 project: - _id: 62781420-2b32-11ec-9570-8d9b63373d4d call_identifier: H2020 grant_number: '101020093' name: Vigilant Algorithmic Monitoring of Software publication: Computer Aided Verification publication_identifier: eisbn: - '9783031131882' eissn: - 1611-3349 isbn: - '9783031131875' issn: - 0302-9743 publication_status: published publisher: Springer Nature quality_controlled: '1' scopus_import: '1' status: public title: FORQ-based language inclusion formal testing tmp: image: /images/cc_by.png legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) short: CC BY (4.0) type: conference user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1 volume: 13372 year: '2022' ... --- _id: '12175' abstract: - lang: eng text: An automaton is history-deterministic (HD) if one can safely resolve its non-deterministic choices on the fly. In a recent paper, Henzinger, Lehtinen and Totzke studied this in the context of Timed Automata [9], where it was conjectured that the class of timed ω-languages recognised by HD-timed automata strictly extends that of deterministic ones. We provide a proof for this fact. acknowledgement: This work was supported in part by the ERC-2020-AdG 101020093, the EPSRC project EP/V025848/1, and the EPSRC project EP/X017796/1. alternative_title: - LNCS article_processing_charge: No author: - first_name: Sougata full_name: Bose, Sougata last_name: Bose - first_name: Thomas A full_name: Henzinger, Thomas A id: 40876CD8-F248-11E8-B48F-1D18A9856A87 last_name: Henzinger orcid: 0000-0002-2985-7724 - first_name: Karoliina full_name: Lehtinen, Karoliina last_name: Lehtinen - first_name: Sven full_name: Schewe, Sven last_name: Schewe - first_name: Patrick full_name: Totzke, Patrick last_name: Totzke citation: ama: 'Bose S, Henzinger TA, Lehtinen K, Schewe S, Totzke P. History-deterministic timed automata are not determinizable. In: 16th International Conference on Reachability Problems. Vol 13608. Springer Nature; 2022:67-76. doi:10.1007/978-3-031-19135-0_5' apa: 'Bose, S., Henzinger, T. A., Lehtinen, K., Schewe, S., & Totzke, P. (2022). History-deterministic timed automata are not determinizable. In 16th International Conference on Reachability Problems (Vol. 13608, pp. 67–76). Kaiserslautern, Germany: Springer Nature. https://doi.org/10.1007/978-3-031-19135-0_5' chicago: Bose, Sougata, Thomas A Henzinger, Karoliina Lehtinen, Sven Schewe, and Patrick Totzke. “History-Deterministic Timed Automata Are Not Determinizable.” In 16th International Conference on Reachability Problems, 13608:67–76. Springer Nature, 2022. https://doi.org/10.1007/978-3-031-19135-0_5. ieee: S. Bose, T. A. Henzinger, K. Lehtinen, S. Schewe, and P. Totzke, “History-deterministic timed automata are not determinizable,” in 16th International Conference on Reachability Problems, Kaiserslautern, Germany, 2022, vol. 13608, pp. 67–76. ista: 'Bose S, Henzinger TA, Lehtinen K, Schewe S, Totzke P. 2022. History-deterministic timed automata are not determinizable. 16th International Conference on Reachability Problems. RC: Reachability Problems, LNCS, vol. 13608, 67–76.' mla: Bose, Sougata, et al. “History-Deterministic Timed Automata Are Not Determinizable.” 16th International Conference on Reachability Problems, vol. 13608, Springer Nature, 2022, pp. 67–76, doi:10.1007/978-3-031-19135-0_5. short: S. Bose, T.A. Henzinger, K. Lehtinen, S. Schewe, P. Totzke, in:, 16th International Conference on Reachability Problems, Springer Nature, 2022, pp. 67–76. conference: end_date: 2022-10-21 location: Kaiserslautern, Germany name: 'RC: Reachability Problems' start_date: 2022-10-17 date_created: 2023-01-12T12:11:57Z date_published: 2022-10-12T00:00:00Z date_updated: 2023-09-05T15:12:08Z day: '12' department: - _id: ToHe doi: 10.1007/978-3-031-19135-0_5 ec_funded: 1 intvolume: ' 13608' language: - iso: eng main_file_link: - open_access: '1' url: https://hal.science/hal-03849398/ month: '10' oa: 1 oa_version: Preprint page: 67-76 project: - _id: 62781420-2b32-11ec-9570-8d9b63373d4d call_identifier: H2020 grant_number: '101020093' name: Vigilant Algorithmic Monitoring of Software publication: 16th International Conference on Reachability Problems publication_identifier: eisbn: - '9783031191350' eissn: - 1611-3349 isbn: - '9783031191343' issn: - 0302-9743 publication_status: published publisher: Springer Nature quality_controlled: '1' scopus_import: '1' status: public title: History-deterministic timed automata are not determinizable type: conference user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1 volume: 13608 year: '2022' ... --- _id: '12510' abstract: - lang: eng text: "We introduce a new statistical verification algorithm that formally quantifies the behavioral robustness of any time-continuous process formulated as a continuous-depth model. Our algorithm solves a set of global optimization (Go) problems over a given time horizon to construct a tight enclosure (Tube) of the set of all process executions starting from a ball of initial states. We call our algorithm GoTube. Through its construction, GoTube ensures that the bounding tube is conservative up to a desired probability and up to a desired tightness.\r\n GoTube is implemented in JAX and optimized to scale to complex continuous-depth neural network models. Compared to advanced reachability analysis tools for time-continuous neural networks, GoTube does not accumulate overapproximation errors between time steps and avoids the infamous wrapping effect inherent in symbolic techniques. We show that GoTube substantially outperforms state-of-the-art verification tools in terms of the size of the initial ball, speed, time-horizon, task completion, and scalability on a large set of experiments.\r\n GoTube is stable and sets the state-of-the-art in terms of its ability to scale to time horizons well beyond what has been previously possible." acknowledgement: SG is funded by the Austrian Science Fund (FWF) project number W1255-N23. ML and TH are supported in part by FWF under grant Z211-N23 (Wittgenstein Award) and the ERC-2020-AdG 101020093. SS is supported by NSF awards DCL-2040599, CCF-1918225, and CPS-1446832. RH and DR are partially supported by Boeing. RG is partially supported by Horizon-2020 ECSEL Project grant No. 783163 (iDev40). article_processing_charge: No article_type: original author: - first_name: Sophie A. full_name: Gruenbacher, Sophie A. last_name: Gruenbacher - first_name: Mathias full_name: Lechner, Mathias id: 3DC22916-F248-11E8-B48F-1D18A9856A87 last_name: Lechner - first_name: Ramin full_name: Hasani, Ramin last_name: Hasani - first_name: Daniela full_name: Rus, Daniela last_name: Rus - first_name: Thomas A full_name: Henzinger, Thomas A id: 40876CD8-F248-11E8-B48F-1D18A9856A87 last_name: Henzinger orcid: 0000-0002-2985-7724 - first_name: Scott A. full_name: Smolka, Scott A. last_name: Smolka - first_name: Radu full_name: Grosu, Radu last_name: Grosu citation: ama: 'Gruenbacher SA, Lechner M, Hasani R, et al. GoTube: Scalable statistical verification of continuous-depth models. Proceedings of the AAAI Conference on Artificial Intelligence. 2022;36(6):6755-6764. doi:10.1609/aaai.v36i6.20631' apa: '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' chicago: 'Gruenbacher, Sophie A., Mathias Lechner, Ramin Hasani, Daniela Rus, Thomas A Henzinger, Scott A. Smolka, and Radu Grosu. “GoTube: Scalable Statistical Verification of Continuous-Depth Models.” Proceedings of the AAAI Conference on Artificial Intelligence. Association for the Advancement of Artificial Intelligence, 2022. https://doi.org/10.1609/aaai.v36i6.20631.' ieee: 'S. A. Gruenbacher et al., “GoTube: Scalable statistical verification of continuous-depth models,” Proceedings of the AAAI Conference on Artificial Intelligence, vol. 36, no. 6. Association for the Advancement of Artificial Intelligence, pp. 6755–6764, 2022.' ista: 'Gruenbacher SA, Lechner M, Hasani R, Rus D, Henzinger TA, Smolka SA, Grosu R. 2022. GoTube: Scalable statistical verification of continuous-depth models. Proceedings of the AAAI Conference on Artificial Intelligence. 36(6), 6755–6764.' mla: 'Gruenbacher, Sophie A., et al. “GoTube: Scalable Statistical Verification of Continuous-Depth Models.” Proceedings of the AAAI Conference on Artificial Intelligence, vol. 36, no. 6, Association for the Advancement of Artificial Intelligence, 2022, pp. 6755–64, doi:10.1609/aaai.v36i6.20631.' short: 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. date_created: 2023-02-05T17:27:42Z date_published: 2022-06-28T00:00:00Z date_updated: 2023-09-26T10:46:59Z day: '28' department: - _id: ToHe doi: 10.1609/aaai.v36i6.20631 ec_funded: 1 external_id: arxiv: - '2107.08467' intvolume: ' 36' issue: '6' keyword: - General Medicine language: - iso: eng main_file_link: - open_access: '1' url: https://arxiv.org/abs/2107.08467 month: '06' oa: 1 oa_version: Preprint page: 6755-6764 project: - _id: 25F42A32-B435-11E9-9278-68D0E5697425 call_identifier: FWF grant_number: Z211 name: The Wittgenstein Prize - _id: 62781420-2b32-11ec-9570-8d9b63373d4d call_identifier: H2020 grant_number: '101020093' name: Vigilant Algorithmic Monitoring of Software publication: Proceedings of the AAAI Conference on Artificial Intelligence publication_identifier: eissn: - 2374-3468 isbn: - '978577358350' issn: - 2159-5399 publication_status: published publisher: Association for the Advancement of Artificial Intelligence quality_controlled: '1' scopus_import: '1' status: public title: 'GoTube: Scalable statistical verification of continuous-depth models' type: journal_article user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 36 year: '2022' ... --- _id: '12511' abstract: - lang: eng text: "We consider the problem of formally verifying almost-sure (a.s.) asymptotic stability in discrete-time nonlinear stochastic control systems. While verifying stability in deterministic control systems is extensively studied in the literature, verifying stability in stochastic control systems is an open problem. The few existing works on this topic either consider only specialized forms of stochasticity or make restrictive assumptions on the system, rendering them inapplicable to learning algorithms with neural network policies. \r\n In this work, we present an approach for general nonlinear stochastic control problems with two novel aspects: (a) instead of classical stochastic extensions of Lyapunov functions, we use ranking supermartingales (RSMs) to certify a.s. asymptotic stability, and (b) we present a method for learning neural network RSMs. \r\n We prove that our approach guarantees a.s. asymptotic stability of the system and\r\n provides the first method to obtain bounds on the stabilization time, which stochastic Lyapunov functions do not.\r\n Finally, we validate our approach experimentally on a set of nonlinear stochastic reinforcement learning environments with neural network policies." acknowledgement: "This work was supported in part by the ERC-2020-AdG 101020093, ERC CoG 863818 (FoRM-SMArt) and the European Union’s Horizon 2020 research and innovation programme\r\nunder the Marie Skłodowska-Curie Grant Agreement No. 665385." article_processing_charge: No article_type: original author: - first_name: Mathias full_name: Lechner, Mathias id: 3DC22916-F248-11E8-B48F-1D18A9856A87 last_name: Lechner - first_name: Dorde full_name: Zikelic, Dorde id: 294AA7A6-F248-11E8-B48F-1D18A9856A87 last_name: Zikelic orcid: 0000-0002-4681-1699 - first_name: Krishnendu full_name: Chatterjee, Krishnendu id: 2E5DCA20-F248-11E8-B48F-1D18A9856A87 last_name: Chatterjee orcid: 0000-0002-4561-241X - first_name: Thomas A full_name: Henzinger, Thomas A id: 40876CD8-F248-11E8-B48F-1D18A9856A87 last_name: Henzinger orcid: 0000-0002-2985-7724 citation: ama: Lechner M, Zikelic D, Chatterjee K, Henzinger TA. Stability verification in stochastic control systems via neural network supermartingales. Proceedings of the AAAI Conference on Artificial Intelligence. 2022;36(7):7326-7336. doi:10.1609/aaai.v36i7.20695 apa: 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 chicago: Lechner, Mathias, Dorde Zikelic, Krishnendu Chatterjee, and Thomas A Henzinger. “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, 2022. https://doi.org/10.1609/aaai.v36i7.20695. ieee: M. Lechner, D. Zikelic, K. Chatterjee, and T. A. Henzinger, “Stability verification in stochastic control systems via neural network supermartingales,” Proceedings of the AAAI Conference on Artificial Intelligence, vol. 36, no. 7. Association for the Advancement of Artificial Intelligence, pp. 7326–7336, 2022. ista: Lechner M, Zikelic D, Chatterjee K, Henzinger TA. 2022. Stability verification in stochastic control systems via neural network supermartingales. Proceedings of the AAAI Conference on Artificial Intelligence. 36(7), 7326–7336. mla: Lechner, Mathias, et al. “Stability Verification in Stochastic Control Systems via Neural Network Supermartingales.” Proceedings of the AAAI Conference on Artificial Intelligence, vol. 36, no. 7, Association for the Advancement of Artificial Intelligence, 2022, pp. 7326–36, doi:10.1609/aaai.v36i7.20695. short: M. Lechner, D. Zikelic, K. Chatterjee, T.A. Henzinger, Proceedings of the AAAI Conference on Artificial Intelligence 36 (2022) 7326–7336. date_created: 2023-02-05T17:29:50Z date_published: 2022-06-28T00:00:00Z date_updated: 2023-11-30T10:55:37Z day: '28' department: - _id: ToHe - _id: KrCh doi: 10.1609/aaai.v36i7.20695 ec_funded: 1 external_id: arxiv: - '2112.09495' intvolume: ' 36' issue: '7' keyword: - General Medicine language: - iso: eng main_file_link: - open_access: '1' url: https://arxiv.org/abs/2112.09495 month: '06' oa: 1 oa_version: Preprint page: 7326-7336 project: - _id: 62781420-2b32-11ec-9570-8d9b63373d4d call_identifier: H2020 grant_number: '101020093' name: Vigilant Algorithmic Monitoring of Software - _id: 0599E47C-7A3F-11EA-A408-12923DDC885E call_identifier: H2020 grant_number: '863818' name: 'Formal Methods for Stochastic Models: Algorithms and Applications' - _id: 2564DBCA-B435-11E9-9278-68D0E5697425 call_identifier: H2020 grant_number: '665385' name: International IST Doctoral Program publication: Proceedings of the AAAI Conference on Artificial Intelligence publication_identifier: eissn: - 2374-3468 isbn: - '9781577358350' issn: - 2159-5399 publication_status: published publisher: Association for the Advancement of Artificial Intelligence quality_controlled: '1' related_material: record: - id: '14539' relation: dissertation_contains status: public scopus_import: '1' status: public title: Stability verification in stochastic control systems via neural network supermartingales type: journal_article user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 36 year: '2022' ... --- _id: '14601' abstract: - lang: eng text: "In this work, we address the problem of learning provably stable neural\r\nnetwork policies for stochastic control systems. While recent work has\r\ndemonstrated the feasibility of certifying given policies using martingale\r\ntheory, the problem of how to learn such policies is little explored. Here, we\r\nstudy the effectiveness of jointly learning a policy together with a martingale\r\ncertificate that proves its stability using a single learning algorithm. We\r\nobserve that the joint optimization problem becomes easily stuck in local\r\nminima when starting from a randomly initialized policy. Our results suggest\r\nthat some form of pre-training of the policy is required for the joint\r\noptimization to repair and verify the policy successfully." article_processing_charge: No author: - first_name: Dorde full_name: Zikelic, Dorde id: 294AA7A6-F248-11E8-B48F-1D18A9856A87 last_name: Zikelic orcid: 0000-0002-4681-1699 - first_name: Mathias full_name: Lechner, Mathias id: 3DC22916-F248-11E8-B48F-1D18A9856A87 last_name: Lechner - first_name: Krishnendu full_name: Chatterjee, Krishnendu id: 2E5DCA20-F248-11E8-B48F-1D18A9856A87 last_name: Chatterjee orcid: 0000-0002-4561-241X - first_name: Thomas A full_name: Henzinger, Thomas A id: 40876CD8-F248-11E8-B48F-1D18A9856A87 last_name: Henzinger orcid: 0000-0002-2985-7724 citation: ama: Zikelic D, Lechner M, Chatterjee K, Henzinger TA. Learning stabilizing policies in stochastic control systems. arXiv. doi:10.48550/arXiv.2205.11991 apa: 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 chicago: Zikelic, Dorde, Mathias Lechner, Krishnendu Chatterjee, and Thomas A Henzinger. “Learning Stabilizing Policies in Stochastic Control Systems.” ArXiv, n.d. https://doi.org/10.48550/arXiv.2205.11991. ieee: D. Zikelic, M. Lechner, K. Chatterjee, and T. A. Henzinger, “Learning stabilizing policies in stochastic control systems,” arXiv. . ista: Zikelic D, Lechner M, Chatterjee K, Henzinger TA. Learning stabilizing policies in stochastic control systems. arXiv, 10.48550/arXiv.2205.11991. mla: Zikelic, Dorde, et al. “Learning Stabilizing Policies in Stochastic Control Systems.” ArXiv, doi:10.48550/arXiv.2205.11991. short: D. Zikelic, M. Lechner, K. Chatterjee, T.A. Henzinger, ArXiv (n.d.). date_created: 2023-11-24T13:22:30Z date_published: 2022-05-24T00:00:00Z date_updated: 2023-11-30T10:55:37Z day: '24' department: - _id: KrCh - _id: ToHe doi: 10.48550/arXiv.2205.11991 ec_funded: 1 external_id: arxiv: - '2205.11991' language: - iso: eng main_file_link: - open_access: '1' url: https://arxiv.org/abs/2205.11991 month: '05' oa: 1 oa_version: Preprint project: - _id: 62781420-2b32-11ec-9570-8d9b63373d4d call_identifier: H2020 grant_number: '101020093' name: Vigilant Algorithmic Monitoring of Software - _id: 0599E47C-7A3F-11EA-A408-12923DDC885E call_identifier: H2020 grant_number: '863818' name: 'Formal Methods for Stochastic Models: Algorithms and Applications' - _id: 2564DBCA-B435-11E9-9278-68D0E5697425 call_identifier: H2020 grant_number: '665385' name: International IST Doctoral Program publication: arXiv publication_status: submitted related_material: record: - id: '14539' relation: dissertation_contains status: public status: public title: Learning stabilizing policies in stochastic control systems type: preprint user_id: 8b945eb4-e2f2-11eb-945a-df72226e66a9 year: '2022' ... --- _id: '14600' abstract: - lang: eng text: We study the problem of learning controllers for discrete-time non-linear stochastic dynamical systems with formal reach-avoid guarantees. This work presents the first method for providing formal reach-avoid guarantees, which combine and generalize stability and safety guarantees, with a tolerable probability threshold $p\in[0,1]$ over the infinite time horizon. Our method leverages advances in machine learning literature and it represents formal certificates as neural networks. In particular, we learn a certificate in the form of a reach-avoid supermartingale (RASM), a novel notion that we introduce in this work. Our RASMs provide reachability and avoidance guarantees by imposing constraints on what can be viewed as a stochastic extension of level sets of Lyapunov functions for deterministic systems. Our approach solves several important problems -- it can be used to learn a control policy from scratch, to verify a reach-avoid specification for a fixed control policy, or to fine-tune a pre-trained policy if it does not satisfy the reach-avoid specification. We validate our approach on $3$ stochastic non-linear reinforcement learning tasks. article_processing_charge: No author: - first_name: Dorde full_name: Zikelic, Dorde id: 294AA7A6-F248-11E8-B48F-1D18A9856A87 last_name: Zikelic orcid: 0000-0002-4681-1699 - first_name: Mathias full_name: Lechner, Mathias id: 3DC22916-F248-11E8-B48F-1D18A9856A87 last_name: Lechner - first_name: Thomas A full_name: Henzinger, Thomas A id: 40876CD8-F248-11E8-B48F-1D18A9856A87 last_name: Henzinger orcid: 0000-0002-2985-7724 - first_name: Krishnendu full_name: Chatterjee, Krishnendu id: 2E5DCA20-F248-11E8-B48F-1D18A9856A87 last_name: Chatterjee orcid: 0000-0002-4561-241X citation: ama: Zikelic D, Lechner M, Henzinger TA, Chatterjee K. Learning control policies for stochastic systems with reach-avoid guarantees. arXiv. doi:10.48550/ARXIV.2210.05308 apa: 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 chicago: Zikelic, Dorde, Mathias Lechner, Thomas A Henzinger, and Krishnendu Chatterjee. “Learning Control Policies for Stochastic Systems with Reach-Avoid Guarantees.” ArXiv, n.d. https://doi.org/10.48550/ARXIV.2210.05308. ieee: D. Zikelic, M. Lechner, T. A. Henzinger, and K. Chatterjee, “Learning control policies for stochastic systems with reach-avoid guarantees,” arXiv. . ista: Zikelic D, Lechner M, Henzinger TA, Chatterjee K. Learning control policies for stochastic systems with reach-avoid guarantees. arXiv, 10.48550/ARXIV.2210.05308. mla: Zikelic, Dorde, et al. “Learning Control Policies for Stochastic Systems with Reach-Avoid Guarantees.” ArXiv, doi:10.48550/ARXIV.2210.05308. short: D. Zikelic, M. Lechner, T.A. Henzinger, K. Chatterjee, ArXiv (n.d.). date_created: 2023-11-24T13:10:09Z date_published: 2022-11-29T00:00:00Z date_updated: 2024-01-22T14:08:29Z day: '29' department: - _id: KrCh - _id: ToHe doi: 10.48550/ARXIV.2210.05308 ec_funded: 1 external_id: arxiv: - '2210.05308' language: - iso: eng license: https://creativecommons.org/licenses/by-sa/4.0/ main_file_link: - open_access: '1' url: https://arxiv.org/abs/2210.05308 month: '11' oa: 1 oa_version: Preprint project: - _id: 0599E47C-7A3F-11EA-A408-12923DDC885E call_identifier: H2020 grant_number: '863818' name: 'Formal Methods for Stochastic Models: Algorithms and Applications' - _id: 62781420-2b32-11ec-9570-8d9b63373d4d call_identifier: H2020 grant_number: '101020093' name: Vigilant Algorithmic Monitoring of Software - _id: 2564DBCA-B435-11E9-9278-68D0E5697425 call_identifier: H2020 grant_number: '665385' name: International IST Doctoral Program publication: arXiv publication_status: submitted related_material: record: - id: '14539' relation: dissertation_contains status: public - id: '14830' relation: later_version status: public status: public title: Learning control policies for stochastic systems with reach-avoid guarantees tmp: image: /images/cc_by_sa.png legal_code_url: https://creativecommons.org/licenses/by-sa/4.0/legalcode name: Creative Commons Attribution-ShareAlike 4.0 International Public License (CC BY-SA 4.0) short: CC BY-SA (4.0) type: preprint user_id: 8b945eb4-e2f2-11eb-945a-df72226e66a9 year: '2022' ... --- _id: '10153' abstract: - lang: eng text: "Gradual typing is a principled means for mixing typed and untyped code. But typed and untyped code often exhibit different programming patterns. There is already substantial research investigating gradually giving types to code exhibiting typical untyped patterns, and some research investigating gradually removing types from code exhibiting typical typed patterns. This paper investigates how to extend these established gradual-typing concepts to give formal guarantees not only about how to change types as code evolves but also about how to change such programming patterns as well.\r\n\r\nIn particular, we explore mixing untyped \"structural\" code with typed \"nominal\" code in an object-oriented language. But whereas previous work only allowed \"nominal\" objects to be treated as \"structural\" objects, we also allow \"structural\" objects to dynamically acquire certain nominal types, namely interfaces. We present a calculus that supports such \"cross-paradigm\" code migration and interoperation in a manner satisfying both the static and dynamic gradual guarantees, and demonstrate that the calculus can be implemented efficiently." acknowledgement: "We thank the reviewers for their valuable suggestions towards improving the paper. We also \r\nthank Mae Milano and Adrian Sampson, as well as the members of the Programming Languages Discussion Group at Cornell University and of the Programming Research Laboratory at Northeastern University, for their helpful feedback on preliminary findings of this work.\r\n\r\nThis material is based upon work supported in part by the National Science Foundation (NSF) through grant CCF-1350182 and the Austrian Science Fund (FWF) through grant Z211-N23 (Wittgenstein~Award).\r\nAny opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the NSF or the FWF." article_number: '127' article_processing_charge: No article_type: original author: - first_name: Fabian full_name: Mühlböck, Fabian id: 6395C5F6-89DF-11E9-9C97-6BDFE5697425 last_name: Mühlböck orcid: 0000-0003-1548-0177 - first_name: Ross full_name: Tate, Ross last_name: Tate citation: ama: Mühlböck F, Tate R. Transitioning from structural to nominal code with efficient gradual typing. Proceedings of the ACM on Programming Languages. 2021;5. doi:10.1145/3485504 apa: 'Mühlböck, F., & Tate, R. (2021). Transitioning from structural to nominal code with efficient gradual typing. Proceedings of the ACM on Programming Languages. Chicago, IL, United States: Association for Computing Machinery. https://doi.org/10.1145/3485504' chicago: Mühlböck, Fabian, and Ross Tate. “Transitioning from Structural to Nominal Code with Efficient Gradual Typing.” Proceedings of the ACM on Programming Languages. Association for Computing Machinery, 2021. https://doi.org/10.1145/3485504. ieee: F. Mühlböck and R. Tate, “Transitioning from structural to nominal code with efficient gradual typing,” Proceedings of the ACM on Programming Languages, vol. 5. Association for Computing Machinery, 2021. ista: Mühlböck F, Tate R. 2021. Transitioning from structural to nominal code with efficient gradual typing. Proceedings of the ACM on Programming Languages. 5, 127. mla: Mühlböck, Fabian, and Ross Tate. “Transitioning from Structural to Nominal Code with Efficient Gradual Typing.” Proceedings of the ACM on Programming Languages, vol. 5, 127, Association for Computing Machinery, 2021, doi:10.1145/3485504. short: F. Mühlböck, R. Tate, Proceedings of the ACM on Programming Languages 5 (2021). conference: end_date: 2021-10-23 location: Chicago, IL, United States name: 'OOPSLA: Object-Oriented Programming, Systems, Languages, and Applications' start_date: 2021-10-17 date_created: 2021-10-19T12:48:44Z date_published: 2021-10-15T00:00:00Z date_updated: 2021-11-12T11:30:07Z day: '15' ddc: - '005' department: - _id: ToHe doi: 10.1145/3485504 file: - access_level: open_access checksum: 71011efd2da771cafdec7f0d9693f8c1 content_type: application/pdf creator: fmuehlbo date_created: 2021-10-19T12:52:23Z date_updated: 2021-10-19T12:52:23Z file_id: '10154' file_name: monnom-oopsla21.pdf file_size: 770269 relation: main_file success: 1 file_date_updated: 2021-10-19T12:52:23Z has_accepted_license: '1' intvolume: ' 5' keyword: - gradual typing - gradual guarantee - nominal - structural - call tags language: - iso: eng month: '10' oa: 1 oa_version: Published Version project: - _id: 25F42A32-B435-11E9-9278-68D0E5697425 call_identifier: FWF grant_number: Z211 name: The Wittgenstein Prize publication: Proceedings of the ACM on Programming Languages publication_identifier: eissn: - 2475-1421 publication_status: published publisher: Association for Computing Machinery quality_controlled: '1' status: public title: Transitioning from structural to nominal code with efficient gradual typing tmp: image: /image/cc_by_nd.png legal_code_url: https://creativecommons.org/licenses/by-nd/4.0/legalcode name: Creative Commons Attribution-NoDerivatives 4.0 International (CC BY-ND 4.0) short: CC BY-ND (4.0) type: journal_article user_id: 8b945eb4-e2f2-11eb-945a-df72226e66a9 volume: 5 year: '2021' ... --- _id: '10669' abstract: - lang: eng text: "We show that Neural ODEs, an emerging class of timecontinuous neural networks, can be verified by solving a set of global-optimization problems. For this purpose, we introduce Stochastic Lagrangian Reachability (SLR), an\r\nabstraction-based technique for constructing a tight Reachtube (an over-approximation of the set of reachable states\r\nover a given time-horizon), and provide stochastic guarantees in the form of confidence intervals for the Reachtube bounds. SLR inherently avoids the infamous wrapping effect (accumulation of over-approximation errors) by performing local optimization steps to expand safe regions instead of repeatedly forward-propagating them as is done by deterministic reachability methods. To enable fast local optimizations, we introduce a novel forward-mode adjoint sensitivity method to compute gradients without the need for backpropagation. Finally, we establish asymptotic and non-asymptotic convergence rates for SLR." acknowledgement: "The authors would like to thank the reviewers for their insightful comments. RH and RG were partially supported by\r\nHorizon-2020 ECSEL Project grant No. 783163 (iDev40). RH was partially supported by Boeing. ML was supported\r\nin part by the Austrian Science Fund (FWF) under grant Z211-N23 (Wittgenstein Award). SG was funded by FWF\r\nproject W1255-N23. JC was partially supported by NAWA Polish Returns grant PPN/PPO/2018/1/00029. SS was supported by NSF awards DCL-2040599, CCF-1918225, and CPS-1446832.\r\n" alternative_title: - Technical Tracks article_processing_charge: No author: - first_name: Sophie full_name: Grunbacher, Sophie last_name: Grunbacher - first_name: Ramin full_name: Hasani, Ramin last_name: Hasani - first_name: Mathias full_name: Lechner, Mathias id: 3DC22916-F248-11E8-B48F-1D18A9856A87 last_name: Lechner - first_name: Jacek full_name: Cyranka, Jacek last_name: Cyranka - first_name: Scott A full_name: Smolka, Scott A last_name: Smolka - first_name: Radu full_name: Grosu, Radu last_name: Grosu citation: ama: 'Grunbacher S, Hasani R, Lechner M, Cyranka J, Smolka SA, Grosu R. On the verification of neural ODEs with stochastic guarantees. In: Proceedings of the AAAI Conference on Artificial Intelligence. Vol 35. AAAI Press; 2021:11525-11535.' apa: '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.' chicago: Grunbacher, Sophie, Ramin Hasani, Mathias Lechner, Jacek Cyranka, Scott A Smolka, and Radu Grosu. “On the Verification of Neural ODEs with Stochastic Guarantees.” In Proceedings of the AAAI Conference on Artificial Intelligence, 35:11525–35. AAAI Press, 2021. ieee: S. Grunbacher, R. Hasani, M. Lechner, J. Cyranka, S. A. Smolka, and R. Grosu, “On the verification of neural ODEs with stochastic guarantees,” in Proceedings of the AAAI Conference on Artificial Intelligence, Virtual, 2021, vol. 35, no. 13, pp. 11525–11535. ista: 'Grunbacher S, Hasani R, Lechner M, Cyranka J, Smolka SA, Grosu R. 2021. On the verification of neural ODEs with stochastic guarantees. Proceedings of the AAAI Conference on Artificial Intelligence. AAAI: Association for the Advancement of Artificial Intelligence, Technical Tracks, vol. 35, 11525–11535.' mla: Grunbacher, Sophie, et al. “On the Verification of Neural ODEs with Stochastic Guarantees.” Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, no. 13, AAAI Press, 2021, pp. 11525–35. short: 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. conference: end_date: 2021-02-09 location: Virtual name: 'AAAI: Association for the Advancement of Artificial Intelligence' start_date: 2021-02-02 date_created: 2022-01-25T15:47:20Z date_published: 2021-05-28T00:00:00Z date_updated: 2022-05-24T06:33:14Z day: '28' ddc: - '000' department: - _id: GradSch - _id: ToHe external_id: arxiv: - '2012.08863' file: - access_level: open_access checksum: 468d07041e282a1d46ffdae92f709630 content_type: application/pdf creator: mlechner date_created: 2022-01-26T07:38:08Z date_updated: 2022-01-26T07:38:08Z file_id: '10680' file_name: 17372-Article Text-20866-1-2-20210518.pdf file_size: 286906 relation: main_file success: 1 file_date_updated: 2022-01-26T07:38:08Z has_accepted_license: '1' intvolume: ' 35' issue: '13' language: - iso: eng main_file_link: - open_access: '1' url: https://ojs.aaai.org/index.php/AAAI/article/view/17372 month: '05' oa: 1 oa_version: Published Version page: 11525-11535 project: - _id: 25F42A32-B435-11E9-9278-68D0E5697425 call_identifier: FWF grant_number: Z211 name: The Wittgenstein Prize publication: Proceedings of the AAAI Conference on Artificial Intelligence publication_identifier: eissn: - 2374-3468 isbn: - 978-1-57735-866-4 issn: - 2159-5399 publication_status: published publisher: AAAI Press quality_controlled: '1' status: public title: On the verification of neural ODEs with stochastic guarantees type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 35 year: '2021' ... --- _id: '10671' abstract: - lang: eng text: We introduce a new class of time-continuous recurrent neural network models. Instead of declaring a learning system’s dynamics by implicit nonlinearities, we construct networks of linear first-order dynamical systems modulated via nonlinear interlinked gates. The resulting models represent dynamical systems with varying (i.e., liquid) time-constants coupled to their hidden state, with outputs being computed by numerical differential equation solvers. These neural networks exhibit stable and bounded behavior, yield superior expressivity within the family of neural ordinary differential equations, and give rise to improved performance on time-series prediction tasks. To demonstrate these properties, we first take a theoretical approach to find bounds over their dynamics, and compute their expressive power by the trajectory length measure in a latent trajectory space. We then conduct a series of time-series prediction experiments to manifest the approximation capability of Liquid Time-Constant Networks (LTCs) compared to classical and modern RNNs. acknowledgement: "R.H. and D.R. are partially supported by Boeing. R.H. and R.G. were partially supported by the Horizon-2020 ECSEL\r\nProject grant No. 783163 (iDev40). M.L. was supported in part by the Austrian Science Fund (FWF) under grant Z211-N23 (Wittgenstein Award). A.A. is supported by the National Science Foundation (NSF) Graduate Research Fellowship Program. This research work is partially drawn from the PhD dissertation of R.H." alternative_title: - Technical Tracks article_processing_charge: No author: - first_name: Ramin full_name: Hasani, Ramin last_name: Hasani - first_name: Mathias full_name: Lechner, Mathias id: 3DC22916-F248-11E8-B48F-1D18A9856A87 last_name: Lechner - first_name: Alexander full_name: Amini, Alexander last_name: Amini - first_name: Daniela full_name: Rus, Daniela last_name: Rus - first_name: Radu full_name: Grosu, Radu last_name: Grosu citation: ama: 'Hasani R, Lechner M, Amini A, Rus D, Grosu R. Liquid time-constant networks. In: Proceedings of the AAAI Conference on Artificial Intelligence. Vol 35. AAAI Press; 2021:7657-7666.' apa: '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.' chicago: Hasani, Ramin, Mathias Lechner, Alexander Amini, Daniela Rus, and Radu Grosu. “Liquid Time-Constant Networks.” In Proceedings of the AAAI Conference on Artificial Intelligence, 35:7657–66. AAAI Press, 2021. ieee: R. Hasani, M. Lechner, A. Amini, D. Rus, and R. Grosu, “Liquid time-constant networks,” in Proceedings of the AAAI Conference on Artificial Intelligence, Virtual, 2021, vol. 35, no. 9, pp. 7657–7666. ista: 'Hasani R, Lechner M, Amini A, Rus D, Grosu R. 2021. Liquid time-constant networks. Proceedings of the AAAI Conference on Artificial Intelligence. AAAI: Association for the Advancement of Artificial Intelligence, Technical Tracks, vol. 35, 7657–7666.' mla: Hasani, Ramin, et al. “Liquid Time-Constant Networks.” Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, no. 9, AAAI Press, 2021, pp. 7657–66. short: 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. conference: end_date: 2021-02-09 location: Virtual name: 'AAAI: Association for the Advancement of Artificial Intelligence' start_date: 2021-02-02 date_created: 2022-01-25T15:48:36Z date_published: 2021-05-28T00:00:00Z date_updated: 2022-05-24T06:36:54Z day: '28' ddc: - '000' department: - _id: GradSch - _id: ToHe external_id: arxiv: - '2006.04439' file: - access_level: open_access checksum: 0f06995fba06dbcfa7ed965fc66027ff content_type: application/pdf creator: mlechner date_created: 2022-01-26T07:36:03Z date_updated: 2022-01-26T07:36:03Z file_id: '10678' file_name: 16936-Article Text-20430-1-2-20210518 (1).pdf file_size: 4302669 relation: main_file success: 1 file_date_updated: 2022-01-26T07:36:03Z has_accepted_license: '1' intvolume: ' 35' issue: '9' language: - iso: eng main_file_link: - open_access: '1' url: https://ojs.aaai.org/index.php/AAAI/article/view/16936 month: '05' oa: 1 oa_version: Published Version page: 7657-7666 project: - _id: 25F42A32-B435-11E9-9278-68D0E5697425 call_identifier: FWF grant_number: Z211 name: The Wittgenstein Prize publication: Proceedings of the AAAI Conference on Artificial Intelligence publication_identifier: eissn: - 2374-3468 isbn: - 978-1-57735-866-4 issn: - 2159-5399 publication_status: published publisher: AAAI Press quality_controlled: '1' status: public title: Liquid time-constant networks type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 35 year: '2021' ... --- _id: '10668' abstract: - lang: eng text: 'Robustness to variations in lighting conditions is a key objective for any deep vision system. To this end, our paper extends the receptive field of convolutional neural networks with two residual components, ubiquitous in the visual processing system of vertebrates: On-center and off-center pathways, with an excitatory center and inhibitory surround; OOCS for short. The On-center pathway is excited by the presence of a light stimulus in its center, but not in its surround, whereas the Off-center pathway is excited by the absence of a light stimulus in its center, but not in its surround. We design OOCS pathways via a difference of Gaussians, with their variance computed analytically from the size of the receptive fields. OOCS pathways complement each other in their response to light stimuli, ensuring this way a strong edge-detection capability, and as a result an accurate and robust inference under challenging lighting conditions. We provide extensive empirical evidence showing that networks supplied with OOCS pathways gain accuracy and illumination-robustness from the novel edge representation, compared to other baselines.' acknowledgement: Z.B. is supported by the Doctoral College Resilient Embedded Systems, which is run jointly by the TU Wien’s Faculty of Informatics and the UAS Technikum Wien. R.G. is partially supported by the Horizon 2020 Era-Permed project Persorad, and ECSEL Project grant no. 783163 (iDev40). R.H and D.R were partially supported by Boeing and MIT. M.L. is supported in part by the Austrian Science Fund (FWF) under grant Z211-N23 (Wittgenstein Award). alternative_title: - PMLR article_processing_charge: No author: - first_name: Zahra full_name: Babaiee, Zahra last_name: Babaiee - first_name: Ramin full_name: Hasani, Ramin last_name: Hasani - first_name: Mathias full_name: Lechner, Mathias id: 3DC22916-F248-11E8-B48F-1D18A9856A87 last_name: Lechner - first_name: Daniela full_name: Rus, Daniela last_name: Rus - first_name: Radu full_name: Grosu, Radu last_name: Grosu citation: ama: 'Babaiee Z, Hasani R, Lechner M, Rus D, Grosu R. On-off center-surround receptive fields for accurate and robust image classification. In: Proceedings of the 38th International Conference on Machine Learning. Vol 139. ML Research Press; 2021:478-489.' apa: '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.' chicago: Babaiee, Zahra, Ramin Hasani, Mathias Lechner, Daniela Rus, and Radu Grosu. “On-off Center-Surround Receptive Fields for Accurate and Robust Image Classification.” In Proceedings of the 38th International Conference on Machine Learning, 139:478–89. ML Research Press, 2021. ieee: Z. Babaiee, R. Hasani, M. Lechner, D. Rus, and R. Grosu, “On-off center-surround receptive fields for accurate and robust image classification,” in Proceedings of the 38th International Conference on Machine Learning, Virtual, 2021, vol. 139, pp. 478–489. ista: 'Babaiee Z, Hasani R, Lechner M, Rus D, Grosu R. 2021. On-off center-surround receptive fields for accurate and robust image classification. Proceedings of the 38th International Conference on Machine Learning. ML: Machine Learning, PMLR, vol. 139, 478–489.' mla: Babaiee, Zahra, et al. “On-off Center-Surround Receptive Fields for Accurate and Robust Image Classification.” Proceedings of the 38th International Conference on Machine Learning, vol. 139, ML Research Press, 2021, pp. 478–89. short: 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. conference: end_date: 2021-07-24 location: Virtual name: 'ML: Machine Learning' start_date: 2021-07-18 date_created: 2022-01-25T15:46:33Z date_published: 2021-07-01T00:00:00Z date_updated: 2022-05-04T15:02:27Z day: '01' ddc: - '000' department: - _id: GradSch - _id: ToHe file: - access_level: open_access checksum: d30eae62561bb517d9f978437d7677db content_type: application/pdf creator: mlechner date_created: 2022-01-26T07:38:32Z date_updated: 2022-01-26T07:38:32Z file_id: '10681' file_name: babaiee21a.pdf file_size: 4246561 relation: main_file success: 1 file_date_updated: 2022-01-26T07:38:32Z has_accepted_license: '1' intvolume: ' 139' language: - iso: eng license: https://creativecommons.org/licenses/by-nc-nd/3.0/ main_file_link: - open_access: '1' url: https://proceedings.mlr.press/v139/babaiee21a month: '07' oa: 1 oa_version: Published Version page: 478-489 project: - _id: 25F42A32-B435-11E9-9278-68D0E5697425 call_identifier: FWF grant_number: Z211 name: The Wittgenstein Prize publication: Proceedings of the 38th International Conference on Machine Learning publication_identifier: issn: - 2640-3498 publication_status: published publisher: ML Research Press quality_controlled: '1' status: public title: On-off center-surround receptive fields for accurate and robust image classification tmp: image: /images/cc_by_nc_nd.png legal_code_url: https://creativecommons.org/licenses/by-nc-nd/3.0/legalcode name: Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported (CC BY-NC-ND 3.0) short: CC BY-NC-ND (3.0) type: conference user_id: 2EBD1598-F248-11E8-B48F-1D18A9856A87 volume: 139 year: '2021' ... --- _id: '10670' abstract: - lang: eng text: "Imitation learning enables high-fidelity, vision-based learning of policies within rich, photorealistic environments. However, such techniques often rely on traditional discrete-time neural models and face difficulties in generalizing to domain shifts by failing to account for the causal relationships between the agent and the environment. In this paper, we propose a theoretical and experimental framework for learning causal representations using continuous-time neural networks, specifically over their discrete-time counterparts. We evaluate our method in the context of visual-control learning of drones over a series of complex tasks, ranging from short- and long-term navigation, to chasing static and dynamic objects through photorealistic environments. Our results demonstrate that causal continuous-time\r\ndeep models can perform robust navigation tasks, where advanced recurrent models fail. These models learn complex causal control representations directly from raw visual inputs and scale to solve a variety of tasks using imitation learning." acknowledgement: "C.V., R.H. A.A. and D.R. are partially supported by Boeing and MIT. A.A. is supported by the National Science Foundation (NSF) Graduate Research Fellowship Program. M.L. is supported in part by the Austrian Science Fund (FWF) under grant Z211-N23 (Wittgenstein Award). Research was sponsored by the United States Air Force Research Laboratory and the United States Air Force Artificial Intelligence Accelerator and was accomplished under Cooperative Agreement Number FA8750-19-2-1000. The views and conclusions contained in this document are those of the authors\r\nand should not be interpreted as representing the official policies, either expressed or implied, of the United States Air Force or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation herein.\r\n" alternative_title: - ' Advances in Neural Information Processing Systems' article_processing_charge: No author: - first_name: Charles J full_name: Vorbach, Charles J last_name: Vorbach - first_name: Ramin full_name: Hasani, Ramin last_name: Hasani - first_name: Alexander full_name: Amini, Alexander last_name: Amini - first_name: Mathias full_name: Lechner, Mathias id: 3DC22916-F248-11E8-B48F-1D18A9856A87 last_name: Lechner - first_name: Daniela full_name: Rus, Daniela last_name: Rus citation: ama: 'Vorbach CJ, Hasani R, Amini A, Lechner M, Rus D. Causal navigation by continuous-time neural networks. In: 35th Conference on Neural Information Processing Systems. ; 2021.' apa: 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. chicago: Vorbach, Charles J, Ramin Hasani, Alexander Amini, Mathias Lechner, and Daniela Rus. “Causal Navigation by Continuous-Time Neural Networks.” In 35th Conference on Neural Information Processing Systems, 2021. ieee: C. J. Vorbach, R. Hasani, A. Amini, M. Lechner, and D. Rus, “Causal navigation by continuous-time neural networks,” in 35th Conference on Neural Information Processing Systems, Virtual, 2021. ista: 'Vorbach CJ, Hasani R, Amini A, Lechner M, Rus D. 2021. Causal navigation by continuous-time neural networks. 35th Conference on Neural Information Processing Systems. NeurIPS: Neural Information Processing Systems, Advances in Neural Information Processing Systems, .' mla: Vorbach, Charles J., et al. “Causal Navigation by Continuous-Time Neural Networks.” 35th Conference on Neural Information Processing Systems, 2021. short: C.J. Vorbach, R. Hasani, A. Amini, M. Lechner, D. Rus, in:, 35th Conference on Neural Information Processing Systems, 2021. conference: end_date: 2021-12-10 location: Virtual name: 'NeurIPS: Neural Information Processing Systems' start_date: 2021-12-06 date_created: 2022-01-25T15:47:50Z date_published: 2021-12-01T00:00:00Z date_updated: 2022-01-26T14:33:31Z day: '01' ddc: - '000' department: - _id: GradSch - _id: ToHe external_id: arxiv: - '2106.08314' file: - access_level: open_access checksum: be81f0ade174a8c9b2d4fe09590b2021 content_type: application/pdf creator: mlechner date_created: 2022-01-26T07:37:24Z date_updated: 2022-01-26T07:37:24Z file_id: '10679' file_name: NeurIPS-2021-causal-navigation-by-continuous-time-neural-networks-Paper.pdf file_size: 6841228 relation: main_file success: 1 file_date_updated: 2022-01-26T07:37:24Z has_accepted_license: '1' language: - iso: eng main_file_link: - open_access: '1' url: https://proceedings.neurips.cc/paper/2021/hash/67ba02d73c54f0b83c05507b7fb7267f-Abstract.html month: '12' oa: 1 oa_version: Published Version project: - _id: 25F42A32-B435-11E9-9278-68D0E5697425 call_identifier: FWF grant_number: Z211 name: The Wittgenstein Prize publication: 35th Conference on Neural Information Processing Systems publication_status: published quality_controlled: '1' status: public title: Causal navigation by continuous-time neural networks tmp: image: /images/cc_by_nc_nd.png legal_code_url: https://creativecommons.org/licenses/by-nc-nd/3.0/legalcode name: Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported (CC BY-NC-ND 3.0) short: CC BY-NC-ND (3.0) type: conference user_id: 8b945eb4-e2f2-11eb-945a-df72226e66a9 year: '2021' ... --- _id: '10688' abstract: - lang: eng text: "Civl is a static verifier for concurrent programs designed around the conceptual framework of layered refinement,\r\nwhich views the task of verifying a program as a sequence of program simplification steps each justified by its own invariant. Civl verifies a layered concurrent program that compactly expresses all the programs in this sequence and the supporting invariants. This paper presents the design and implementation of the Civl verifier." acknowledgement: This research was performed while Bernhard Kragl was at IST Austria, supported in part by the Austrian Science Fund (FWF) under grant Z211-N23 (Wittgenstein Award). alternative_title: - Conference Series article_processing_charge: No author: - first_name: Bernhard full_name: Kragl, Bernhard id: 320FC952-F248-11E8-B48F-1D18A9856A87 last_name: Kragl orcid: 0000-0001-7745-9117 - first_name: Shaz full_name: Qadeer, Shaz last_name: Qadeer citation: ama: 'Kragl B, Qadeer S. The Civl verifier. In: Ruzica P, Whalen MW, eds. Proceedings of the 21st Conference on Formal Methods in Computer-Aided Design. Vol 2. TU Wien Academic Press; 2021:143–152. doi:10.34727/2021/isbn.978-3-85448-046-4_23' apa: 'Kragl, B., & Qadeer, S. (2021). The Civl verifier. In P. Ruzica & M. W. Whalen (Eds.), Proceedings of the 21st Conference on Formal Methods in Computer-Aided Design (Vol. 2, pp. 143–152). Virtual: TU Wien Academic Press. https://doi.org/10.34727/2021/isbn.978-3-85448-046-4_23' chicago: Kragl, Bernhard, and Shaz Qadeer. “The Civl Verifier.” In Proceedings of the 21st Conference on Formal Methods in Computer-Aided Design, edited by Piskac Ruzica and Michael W. Whalen, 2:143–152. TU Wien Academic Press, 2021. https://doi.org/10.34727/2021/isbn.978-3-85448-046-4_23. ieee: B. Kragl and S. Qadeer, “The Civl verifier,” in Proceedings of the 21st Conference on Formal Methods in Computer-Aided Design, Virtual, 2021, vol. 2, pp. 143–152. ista: 'Kragl B, Qadeer S. 2021. The Civl verifier. Proceedings of the 21st Conference on Formal Methods in Computer-Aided Design. FMCAD: Formal Methods in Computer-Aided Design, Conference Series, vol. 2, 143–152.' mla: Kragl, Bernhard, and Shaz Qadeer. “The Civl Verifier.” Proceedings of the 21st Conference on Formal Methods in Computer-Aided Design, edited by Piskac Ruzica and Michael W. Whalen, vol. 2, TU Wien Academic Press, 2021, pp. 143–152, doi:10.34727/2021/isbn.978-3-85448-046-4_23. short: B. Kragl, S. Qadeer, in:, P. Ruzica, M.W. Whalen (Eds.), Proceedings of the 21st Conference on Formal Methods in Computer-Aided Design, TU Wien Academic Press, 2021, pp. 143–152. conference: end_date: 2021-10-22 location: Virtual name: 'FMCAD: Formal Methods in Computer-Aided Design' start_date: 2021-10-20 date_created: 2022-01-26T08:01:30Z date_published: 2021-10-01T00:00:00Z date_updated: 2022-01-26T08:20:41Z day: '01' ddc: - '000' department: - _id: ToHe doi: 10.34727/2021/isbn.978-3-85448-046-4_23 editor: - first_name: Piskac full_name: Ruzica, Piskac last_name: Ruzica - first_name: Michael W. full_name: Whalen, Michael W. last_name: Whalen file: - access_level: open_access checksum: 35438ac9f9750340b7f8ae4ae3220d9f content_type: application/pdf creator: cchlebak date_created: 2022-01-26T08:04:29Z date_updated: 2022-01-26T08:04:29Z file_id: '10689' file_name: 2021_FCAD2021_Kragl.pdf file_size: 390555 relation: main_file success: 1 file_date_updated: 2022-01-26T08:04:29Z has_accepted_license: '1' intvolume: ' 2' language: - iso: eng month: '10' oa: 1 oa_version: Published Version page: 143–152 project: - _id: 25F42A32-B435-11E9-9278-68D0E5697425 call_identifier: FWF grant_number: Z211 name: The Wittgenstein Prize publication: Proceedings of the 21st Conference on Formal Methods in Computer-Aided Design publication_identifier: isbn: - 978-3-85448-046-4 publication_status: published publisher: TU Wien Academic Press quality_controlled: '1' status: public title: The Civl verifier tmp: image: /images/cc_by.png legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) short: CC BY (4.0) type: conference user_id: 8b945eb4-e2f2-11eb-945a-df72226e66a9 volume: 2 year: '2021' ... --- _id: '9281' abstract: - lang: eng text: We comment on two formal proofs of Fermat's sum of two squares theorem, written using the Mathematical Components libraries of the Coq proof assistant. The first one follows Zagier's celebrated one-sentence proof; the second follows David Christopher's recent new proof relying on partition-theoretic arguments. Both formal proofs rely on a general property of involutions of finite sets, of independent interest. The proof technique consists for the most part of automating recurrent tasks (such as case distinctions and computations on natural numbers) via ad hoc tactics. article_number: '2103.11389' article_processing_charge: No author: - first_name: Guillaume full_name: Dubach, Guillaume id: D5C6A458-10C4-11EA-ABF4-A4B43DDC885E last_name: Dubach orcid: 0000-0001-6892-8137 - first_name: Fabian full_name: Mühlböck, Fabian id: 6395C5F6-89DF-11E9-9C97-6BDFE5697425 last_name: Mühlböck orcid: 0000-0003-1548-0177 citation: ama: Dubach G, Mühlböck F. Formal verification of Zagier’s one-sentence proof. arXiv. doi:10.48550/arXiv.2103.11389 apa: Dubach, G., & Mühlböck, F. (n.d.). Formal verification of Zagier’s one-sentence proof. arXiv. https://doi.org/10.48550/arXiv.2103.11389 chicago: Dubach, Guillaume, and Fabian Mühlböck. “Formal Verification of Zagier’s One-Sentence Proof.” ArXiv, n.d. https://doi.org/10.48550/arXiv.2103.11389. ieee: G. Dubach and F. Mühlböck, “Formal verification of Zagier’s one-sentence proof,” arXiv. . ista: Dubach G, Mühlböck F. Formal verification of Zagier’s one-sentence proof. arXiv, 2103.11389. mla: Dubach, Guillaume, and Fabian Mühlböck. “Formal Verification of Zagier’s One-Sentence Proof.” ArXiv, 2103.11389, doi:10.48550/arXiv.2103.11389. short: G. Dubach, F. Mühlböck, ArXiv (n.d.). date_created: 2021-03-23T05:38:48Z date_published: 2021-03-21T00:00:00Z date_updated: 2023-05-03T10:26:45Z day: '21' department: - _id: LaEr - _id: ToHe doi: 10.48550/arXiv.2103.11389 ec_funded: 1 external_id: arxiv: - '2103.11389' language: - iso: eng main_file_link: - open_access: '1' url: https://arxiv.org/abs/2103.11389 month: '03' oa: 1 oa_version: Preprint project: - _id: 260C2330-B435-11E9-9278-68D0E5697425 call_identifier: H2020 grant_number: '754411' name: ISTplus - Postdoctoral Fellowships publication: arXiv publication_status: submitted related_material: record: - id: '9946' relation: other status: public status: public title: Formal verification of Zagier's one-sentence proof type: preprint user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2021' ... --- _id: '10665' abstract: - lang: eng text: "Formal verification of neural networks is an active topic of research, and recent advances have significantly increased the size of the networks that verification tools can handle. However, most methods are designed for verification of an idealized model of the actual network which works over real arithmetic and ignores rounding imprecisions. This idealization is in stark contrast to network quantization, which is a technique that trades numerical precision for computational efficiency and is, therefore, often applied in practice. Neglecting rounding errors of such low-bit quantized neural networks has been shown to lead to wrong conclusions about the network’s correctness. Thus, the desired approach for verifying quantized neural networks would be one that takes these rounding errors\r\ninto account. In this paper, we show that verifying the bitexact implementation of quantized neural networks with bitvector specifications is PSPACE-hard, even though verifying idealized real-valued networks and satisfiability of bit-vector specifications alone are each in NP. Furthermore, we explore several practical heuristics toward closing the complexity gap between idealized and bit-exact verification. In particular, we propose three techniques for making SMT-based verification of quantized neural networks more scalable. Our experiments demonstrate that our proposed methods allow a speedup of up to three orders of magnitude over existing approaches." acknowledgement: "This research was supported in part by the Austrian Science Fund (FWF) under grant Z211-N23 (Wittgenstein\r\nAward), ERC CoG 863818 (FoRM-SMArt), and the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie Grant Agreement No. 665385.\r\n" alternative_title: - Technical Tracks article_processing_charge: No author: - first_name: Thomas A full_name: Henzinger, Thomas A id: 40876CD8-F248-11E8-B48F-1D18A9856A87 last_name: Henzinger orcid: 0000-0002-2985-7724 - first_name: Mathias full_name: Lechner, Mathias id: 3DC22916-F248-11E8-B48F-1D18A9856A87 last_name: Lechner - first_name: Dorde full_name: Zikelic, Dorde id: 294AA7A6-F248-11E8-B48F-1D18A9856A87 last_name: Zikelic citation: ama: 'Henzinger TA, Lechner M, Zikelic D. Scalable verification of quantized neural networks. In: Proceedings of the AAAI Conference on Artificial Intelligence. Vol 35. AAAI Press; 2021:3787-3795.' apa: '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.' chicago: Henzinger, Thomas A, Mathias Lechner, and Dorde Zikelic. “Scalable Verification of Quantized Neural Networks.” In Proceedings of the AAAI Conference on Artificial Intelligence, 35:3787–95. AAAI Press, 2021. ieee: T. A. Henzinger, M. Lechner, and D. Zikelic, “Scalable verification of quantized neural networks,” in Proceedings of the AAAI Conference on Artificial Intelligence, Virtual, 2021, vol. 35, no. 5A, pp. 3787–3795. ista: 'Henzinger TA, Lechner M, Zikelic D. 2021. Scalable verification of quantized neural networks. Proceedings of the AAAI Conference on Artificial Intelligence. AAAI: Association for the Advancement of Artificial Intelligence, Technical Tracks, vol. 35, 3787–3795.' mla: Henzinger, Thomas A., et al. “Scalable Verification of Quantized Neural Networks.” Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, no. 5A, AAAI Press, 2021, pp. 3787–95. short: T.A. Henzinger, M. Lechner, D. Zikelic, in:, Proceedings of the AAAI Conference on Artificial Intelligence, AAAI Press, 2021, pp. 3787–3795. conference: end_date: 2021-02-09 location: Virtual name: 'AAAI: Association for the Advancement of Artificial Intelligence' start_date: 2021-02-02 date_created: 2022-01-25T15:15:02Z date_published: 2021-05-28T00:00:00Z date_updated: 2023-06-23T07:01:11Z day: '28' ddc: - '000' department: - _id: GradSch - _id: ToHe ec_funded: 1 external_id: arxiv: - '2012.08185' file: - access_level: open_access checksum: 2bc8155b2526a70fba5b7301bc89dbd1 content_type: application/pdf creator: mlechner date_created: 2022-01-26T07:41:16Z date_updated: 2022-01-26T07:41:16Z file_id: '10684' file_name: 16496-Article Text-19990-1-2-20210518 (1).pdf file_size: 137235 relation: main_file success: 1 file_date_updated: 2022-01-26T07:41:16Z has_accepted_license: '1' intvolume: ' 35' issue: 5A language: - iso: eng main_file_link: - open_access: '1' url: https://ojs.aaai.org/index.php/AAAI/article/view/16496 month: '05' oa: 1 oa_version: Published Version page: 3787-3795 project: - _id: 2564DBCA-B435-11E9-9278-68D0E5697425 call_identifier: H2020 grant_number: '665385' name: International IST Doctoral Program - _id: 25F42A32-B435-11E9-9278-68D0E5697425 call_identifier: FWF grant_number: Z211 name: The Wittgenstein Prize - _id: 0599E47C-7A3F-11EA-A408-12923DDC885E call_identifier: H2020 grant_number: '863818' name: 'Formal Methods for Stochastic Models: Algorithms and Applications' publication: Proceedings of the AAAI Conference on Artificial Intelligence publication_identifier: eissn: - 2374-3468 isbn: - 978-1-57735-866-4 issn: - 2159-5399 publication_status: published publisher: AAAI Press quality_controlled: '1' related_material: record: - id: '11362' relation: dissertation_contains status: public scopus_import: '1' status: public title: Scalable verification of quantized neural networks type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 35 year: '2021' ...