[{"oa":1,"publisher":"Institute of Science and Technology Austria","date_created":"2022-05-12T07:14:01Z","doi":"10.15479/at:ista:11362","date_published":"2022-05-12T00:00:00Z","page":"124","day":"12","year":"2022","has_accepted_license":"1","project":[{"_id":"25F42A32-B435-11E9-9278-68D0E5697425","call_identifier":"FWF","name":"The Wittgenstein Prize","grant_number":"Z211"},{"name":"Vigilant Algorithmic Monitoring of Software","grant_number":"101020093","_id":"62781420-2b32-11ec-9570-8d9b63373d4d","call_identifier":"H2020"}],"title":"Learning verifiable representations","article_processing_charge":"No","author":[{"full_name":"Lechner, Mathias","last_name":"Lechner","first_name":"Mathias","id":"3DC22916-F248-11E8-B48F-1D18A9856A87"}],"user_id":"8b945eb4-e2f2-11eb-945a-df72226e66a9","citation":{"mla":"Lechner, Mathias. Learning Verifiable Representations. Institute of Science and Technology Austria, 2022, doi:10.15479/at:ista:11362.","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","short":"M. Lechner, Learning Verifiable Representations, Institute of Science and Technology Austria, 2022.","ieee":"M. Lechner, “Learning verifiable representations,” Institute of Science and Technology Austria, 2022.","chicago":"Lechner, Mathias. “Learning Verifiable Representations.” Institute of Science and Technology Austria, 2022. https://doi.org/10.15479/at:ista:11362.","ista":"Lechner M. 2022. Learning verifiable representations. Institute of Science and Technology Austria."},"month":"05","alternative_title":["ISTA Thesis"],"oa_version":"Published Version","abstract":[{"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.","lang":"eng"}],"license":"https://creativecommons.org/licenses/by-nd/4.0/","ec_funded":1,"related_material":{"record":[{"relation":"part_of_dissertation","id":"10665","status":"public"},{"relation":"part_of_dissertation","status":"public","id":"10667"},{"id":"11366","status":"public","relation":"part_of_dissertation"},{"id":"7808","status":"public","relation":"part_of_dissertation"},{"status":"public","id":"10666","relation":"part_of_dissertation"}]},"language":[{"iso":"eng"}],"file":[{"relation":"source_file","access_level":"closed","content_type":"application/zip","file_id":"11378","checksum":"8eefa9c7c10ca7e1a2ccdd731962a645","creator":"mlechner","file_size":13210143,"date_updated":"2022-05-13T12:49:00Z","file_name":"src.zip","date_created":"2022-05-13T12:33:26Z"},{"checksum":"1b9e1e5a9a83ed9d89dad2f5133dc026","file_id":"11382","content_type":"application/pdf","relation":"main_file","access_level":"open_access","file_name":"thesis_main-a2.pdf","date_created":"2022-05-16T08:02:28Z","file_size":2732536,"date_updated":"2022-05-17T15:19:39Z","creator":"mlechner"}],"publication_status":"published","degree_awarded":"PhD","publication_identifier":{"isbn":["978-3-99078-017-6"]},"keyword":["neural networks","verification","machine learning"],"status":"public","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by-nd/4.0/legalcode","image":"/image/cc_by_nd.png","name":"Creative Commons Attribution-NoDerivatives 4.0 International (CC BY-ND 4.0)","short":"CC BY-ND (4.0)"},"type":"dissertation","_id":"11362","file_date_updated":"2022-05-17T15:19:39Z","department":[{"_id":"GradSch"},{"_id":"ToHe"}],"ddc":["004"],"date_updated":"2023-08-17T06:58:38Z","supervisor":[{"last_name":"Henzinger","full_name":"Henzinger, Thomas A","orcid":"0000-0002-2985-7724","first_name":"Thomas A","id":"40876CD8-F248-11E8-B48F-1D18A9856A87"}]},{"project":[{"call_identifier":"H2020","_id":"62781420-2b32-11ec-9570-8d9b63373d4d","name":"Vigilant Algorithmic Monitoring of Software","grant_number":"101020093"}],"title":"FORQ-based language inclusion formal testing","article_processing_charge":"No","external_id":{"arxiv":["2207.13549"],"isi":["000870310500006"]},"author":[{"full_name":"Doveri, Kyveli","last_name":"Doveri","first_name":"Kyveli"},{"full_name":"Ganty, Pierre","last_name":"Ganty","first_name":"Pierre"},{"id":"b26baa86-3308-11ec-87b0-8990f34baa85","first_name":"Nicolas Adrien","last_name":"Mazzocchi","full_name":"Mazzocchi, Nicolas Adrien"}],"user_id":"c635000d-4b10-11ee-a964-aac5a93f6ac1","citation":{"short":"K. Doveri, P. Ganty, N.A. Mazzocchi, in:, Computer Aided Verification, Springer Nature, 2022, pp. 109–129.","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.","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","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","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.","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.","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."},"oa":1,"publisher":"Springer Nature","quality_controlled":"1","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.","date_created":"2023-01-16T10:06:31Z","date_published":"2022-08-06T00:00:00Z","doi":"10.1007/978-3-031-13188-2_6","page":"109-129","publication":"Computer Aided Verification","day":"06","year":"2022","isi":1,"has_accepted_license":"1","status":"public","conference":{"start_date":"2022-08-07","location":"Haifa, Israel","end_date":"2022-08-10","name":"CAV: Computer Aided Verification"},"tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)"},"type":"conference","_id":"12302","file_date_updated":"2023-01-30T12:51:02Z","department":[{"_id":"ToHe"}],"ddc":["000"],"date_updated":"2023-09-05T15:13:36Z","intvolume":" 13372","month":"08","alternative_title":["LNCS"],"scopus_import":"1","oa_version":"Published Version","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"}],"ec_funded":1,"license":"https://creativecommons.org/licenses/by/4.0/","volume":13372,"language":[{"iso":"eng"}],"file":[{"file_id":"12465","checksum":"edc363b1be5447a09063e115c247918a","success":1,"content_type":"application/pdf","access_level":"open_access","relation":"main_file","date_created":"2023-01-30T12:51:02Z","file_name":"2022_LNCS_Doveri.pdf","date_updated":"2023-01-30T12:51:02Z","file_size":497682,"creator":"dernst"}],"publication_status":"published","publication_identifier":{"eissn":["1611-3349"],"isbn":["9783031131875"],"issn":["0302-9743"],"eisbn":["9783031131882"]}},{"project":[{"grant_number":"101020093","name":"Vigilant Algorithmic Monitoring of Software","call_identifier":"H2020","_id":"62781420-2b32-11ec-9570-8d9b63373d4d"}],"user_id":"c635000d-4b10-11ee-a964-aac5a93f6ac1","citation":{"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.","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.","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.","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","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","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.","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."},"title":"History-deterministic timed automata are not determinizable","article_processing_charge":"No","author":[{"last_name":"Bose","full_name":"Bose, Sougata","first_name":"Sougata"},{"first_name":"Thomas A","id":"40876CD8-F248-11E8-B48F-1D18A9856A87","last_name":"Henzinger","orcid":"0000-0002-2985-7724","full_name":"Henzinger, Thomas A"},{"first_name":"Karoliina","last_name":"Lehtinen","full_name":"Lehtinen, Karoliina"},{"first_name":"Sven","full_name":"Schewe, Sven","last_name":"Schewe"},{"last_name":"Totzke","full_name":"Totzke, Patrick","first_name":"Patrick"}],"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.","oa":1,"publisher":"Springer Nature","quality_controlled":"1","publication":"16th International Conference on Reachability Problems","day":"12","year":"2022","date_created":"2023-01-12T12:11:57Z","doi":"10.1007/978-3-031-19135-0_5","date_published":"2022-10-12T00:00:00Z","page":"67-76","_id":"12175","status":"public","conference":{"start_date":"2022-10-17","location":"Kaiserslautern, Germany","end_date":"2022-10-21","name":"RC: Reachability Problems"},"type":"conference","date_updated":"2023-09-05T15:12:08Z","department":[{"_id":"ToHe"}],"oa_version":"Preprint","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."}],"intvolume":" 13608","month":"10","main_file_link":[{"open_access":"1","url":"https://hal.science/hal-03849398/"}],"scopus_import":"1","alternative_title":["LNCS"],"language":[{"iso":"eng"}],"publication_status":"published","publication_identifier":{"eisbn":["9783031191350"],"issn":["0302-9743"],"eissn":["1611-3349"],"isbn":["9783031191343"]},"ec_funded":1,"volume":13608},{"language":[{"iso":"eng"}],"publication_identifier":{"eissn":["2374-3468"],"isbn":["978577358350"],"issn":["2159-5399"]},"publication_status":"published","issue":"6","volume":36,"ec_funded":1,"oa_version":"Preprint","abstract":[{"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.","lang":"eng"}],"month":"06","intvolume":" 36","scopus_import":"1","main_file_link":[{"url":"https://arxiv.org/abs/2107.08467","open_access":"1"}],"date_updated":"2023-09-26T10:46:59Z","department":[{"_id":"ToHe"}],"_id":"12510","status":"public","keyword":["General Medicine"],"type":"journal_article","article_type":"original","day":"28","publication":"Proceedings of the AAAI Conference on Artificial Intelligence","year":"2022","doi":"10.1609/aaai.v36i6.20631","date_published":"2022-06-28T00:00:00Z","date_created":"2023-02-05T17:27:42Z","page":"6755-6764","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).","quality_controlled":"1","publisher":"Association for the Advancement of Artificial Intelligence","oa":1,"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","citation":{"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.","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.","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","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.","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."},"title":"GoTube: Scalable statistical verification of continuous-depth models","author":[{"first_name":"Sophie A.","last_name":"Gruenbacher","full_name":"Gruenbacher, Sophie A."},{"last_name":"Lechner","full_name":"Lechner, Mathias","first_name":"Mathias","id":"3DC22916-F248-11E8-B48F-1D18A9856A87"},{"first_name":"Ramin","last_name":"Hasani","full_name":"Hasani, Ramin"},{"full_name":"Rus, Daniela","last_name":"Rus","first_name":"Daniela"},{"id":"40876CD8-F248-11E8-B48F-1D18A9856A87","first_name":"Thomas A","full_name":"Henzinger, Thomas A","orcid":"0000-0002-2985-7724","last_name":"Henzinger"},{"last_name":"Smolka","full_name":"Smolka, Scott A.","first_name":"Scott A."},{"first_name":"Radu","full_name":"Grosu, Radu","last_name":"Grosu"}],"external_id":{"arxiv":["2107.08467"]},"article_processing_charge":"No","project":[{"name":"The Wittgenstein Prize","grant_number":"Z211","_id":"25F42A32-B435-11E9-9278-68D0E5697425","call_identifier":"FWF"},{"name":"Vigilant Algorithmic Monitoring of Software","grant_number":"101020093","call_identifier":"H2020","_id":"62781420-2b32-11ec-9570-8d9b63373d4d"}]},{"article_type":"original","type":"journal_article","keyword":["General Medicine"],"status":"public","_id":"12511","department":[{"_id":"ToHe"},{"_id":"KrCh"}],"date_updated":"2023-11-30T10:55:37Z","main_file_link":[{"url":"https://arxiv.org/abs/2112.09495","open_access":"1"}],"scopus_import":"1","intvolume":" 36","month":"06","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."}],"oa_version":"Preprint","ec_funded":1,"related_material":{"record":[{"id":"14539","status":"public","relation":"dissertation_contains"}]},"issue":"7","volume":36,"publication_status":"published","publication_identifier":{"issn":["2159-5399"],"isbn":["9781577358350"],"eissn":["2374-3468"]},"language":[{"iso":"eng"}],"project":[{"name":"Vigilant Algorithmic Monitoring of Software","grant_number":"101020093","call_identifier":"H2020","_id":"62781420-2b32-11ec-9570-8d9b63373d4d"},{"call_identifier":"H2020","_id":"0599E47C-7A3F-11EA-A408-12923DDC885E","name":"Formal Methods for Stochastic Models: Algorithms and Applications","grant_number":"863818"},{"grant_number":"665385","name":"International IST Doctoral Program","_id":"2564DBCA-B435-11E9-9278-68D0E5697425","call_identifier":"H2020"}],"external_id":{"arxiv":["2112.09495"]},"article_processing_charge":"No","author":[{"last_name":"Lechner","full_name":"Lechner, Mathias","id":"3DC22916-F248-11E8-B48F-1D18A9856A87","first_name":"Mathias"},{"first_name":"Dorde","id":"294AA7A6-F248-11E8-B48F-1D18A9856A87","last_name":"Zikelic","full_name":"Zikelic, Dorde","orcid":"0000-0002-4681-1699"},{"full_name":"Chatterjee, Krishnendu","orcid":"0000-0002-4561-241X","last_name":"Chatterjee","id":"2E5DCA20-F248-11E8-B48F-1D18A9856A87","first_name":"Krishnendu"},{"full_name":"Henzinger, Thomas A","orcid":"0000-0002-2985-7724","last_name":"Henzinger","id":"40876CD8-F248-11E8-B48F-1D18A9856A87","first_name":"Thomas A"}],"title":"Stability verification in stochastic control systems via neural network supermartingales","citation":{"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.","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.","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.","short":"M. Lechner, D. Zikelic, K. Chatterjee, T.A. Henzinger, Proceedings of the AAAI Conference on Artificial Intelligence 36 (2022) 7326–7336.","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","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"},"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","oa":1,"quality_controlled":"1","publisher":"Association for the Advancement of Artificial Intelligence","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.","page":"7326-7336","date_created":"2023-02-05T17:29:50Z","date_published":"2022-06-28T00:00:00Z","doi":"10.1609/aaai.v36i7.20695","year":"2022","publication":"Proceedings of the AAAI Conference on Artificial Intelligence","day":"28"},{"language":[{"iso":"eng"}],"publication":"arXiv","day":"24","year":"2022","publication_status":"submitted","date_created":"2023-11-24T13:22:30Z","ec_funded":1,"date_published":"2022-05-24T00:00:00Z","related_material":{"record":[{"relation":"dissertation_contains","id":"14539","status":"public"}]},"doi":"10.48550/arXiv.2205.11991","oa_version":"Preprint","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."}],"month":"05","oa":1,"main_file_link":[{"open_access":"1","url":"https://arxiv.org/abs/2205.11991"}],"user_id":"8b945eb4-e2f2-11eb-945a-df72226e66a9","citation":{"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.).","ieee":"D. Zikelic, M. Lechner, K. Chatterjee, and T. A. Henzinger, “Learning stabilizing policies in stochastic control systems,” arXiv. .","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.","ista":"Zikelic D, Lechner M, Chatterjee K, Henzinger TA. Learning stabilizing policies in stochastic control systems. arXiv, 10.48550/arXiv.2205.11991."},"date_updated":"2023-11-30T10:55:37Z","department":[{"_id":"KrCh"},{"_id":"ToHe"}],"title":"Learning stabilizing policies in stochastic control systems","external_id":{"arxiv":["2205.11991"]},"article_processing_charge":"No","author":[{"id":"294AA7A6-F248-11E8-B48F-1D18A9856A87","first_name":"Dorde","last_name":"Zikelic","orcid":"0000-0002-4681-1699","full_name":"Zikelic, Dorde"},{"id":"3DC22916-F248-11E8-B48F-1D18A9856A87","first_name":"Mathias","last_name":"Lechner","full_name":"Lechner, Mathias"},{"last_name":"Chatterjee","full_name":"Chatterjee, Krishnendu","orcid":"0000-0002-4561-241X","id":"2E5DCA20-F248-11E8-B48F-1D18A9856A87","first_name":"Krishnendu"},{"last_name":"Henzinger","orcid":"0000-0002-2985-7724","full_name":"Henzinger, Thomas A","first_name":"Thomas A","id":"40876CD8-F248-11E8-B48F-1D18A9856A87"}],"_id":"14601","project":[{"_id":"62781420-2b32-11ec-9570-8d9b63373d4d","call_identifier":"H2020","grant_number":"101020093","name":"Vigilant Algorithmic Monitoring of Software"},{"grant_number":"863818","name":"Formal Methods for Stochastic Models: Algorithms and Applications","call_identifier":"H2020","_id":"0599E47C-7A3F-11EA-A408-12923DDC885E"},{"_id":"2564DBCA-B435-11E9-9278-68D0E5697425","call_identifier":"H2020","grant_number":"665385","name":"International IST Doctoral Program"}],"status":"public","type":"preprint"},{"title":"Learning control policies for stochastic systems with reach-avoid guarantees","department":[{"_id":"KrCh"},{"_id":"ToHe"}],"external_id":{"arxiv":["2210.05308"]},"article_processing_charge":"No","author":[{"last_name":"Zikelic","full_name":"Zikelic, Dorde","orcid":"0000-0002-4681-1699","first_name":"Dorde","id":"294AA7A6-F248-11E8-B48F-1D18A9856A87"},{"first_name":"Mathias","id":"3DC22916-F248-11E8-B48F-1D18A9856A87","last_name":"Lechner","full_name":"Lechner, Mathias"},{"orcid":"0000-0002-2985-7724","full_name":"Henzinger, Thomas A","last_name":"Henzinger","first_name":"Thomas A","id":"40876CD8-F248-11E8-B48F-1D18A9856A87"},{"last_name":"Chatterjee","full_name":"Chatterjee, Krishnendu","orcid":"0000-0002-4561-241X","id":"2E5DCA20-F248-11E8-B48F-1D18A9856A87","first_name":"Krishnendu"}],"user_id":"8b945eb4-e2f2-11eb-945a-df72226e66a9","date_updated":"2024-01-22T14:08:29Z","citation":{"mla":"Zikelic, Dorde, et al. “Learning Control Policies for Stochastic Systems with Reach-Avoid Guarantees.” ArXiv, doi:10.48550/ARXIV.2210.05308.","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","short":"D. Zikelic, M. Lechner, T.A. Henzinger, K. Chatterjee, ArXiv (n.d.).","ieee":"D. Zikelic, M. Lechner, T. A. Henzinger, and K. Chatterjee, “Learning control policies for stochastic systems with reach-avoid guarantees,” arXiv. .","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.","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."},"project":[{"call_identifier":"H2020","_id":"0599E47C-7A3F-11EA-A408-12923DDC885E","name":"Formal Methods for Stochastic Models: Algorithms and Applications","grant_number":"863818"},{"call_identifier":"H2020","_id":"62781420-2b32-11ec-9570-8d9b63373d4d","grant_number":"101020093","name":"Vigilant Algorithmic Monitoring of Software"},{"_id":"2564DBCA-B435-11E9-9278-68D0E5697425","call_identifier":"H2020","name":"International IST Doctoral Program","grant_number":"665385"}],"status":"public","tmp":{"short":"CC BY-SA (4.0)","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)"},"type":"preprint","_id":"14600","license":"https://creativecommons.org/licenses/by-sa/4.0/","ec_funded":1,"date_created":"2023-11-24T13:10:09Z","related_material":{"record":[{"relation":"dissertation_contains","id":"14539","status":"public"},{"relation":"later_version","status":"public","id":"14830"}]},"date_published":"2022-11-29T00:00:00Z","doi":"10.48550/ARXIV.2210.05308","language":[{"iso":"eng"}],"publication":"arXiv","day":"29","publication_status":"submitted","year":"2022","month":"11","oa":1,"main_file_link":[{"url":"https://arxiv.org/abs/2210.05308","open_access":"1"}],"oa_version":"Preprint","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."}]},{"doi":"10.1145/3485504","date_published":"2021-10-15T00:00:00Z","date_created":"2021-10-19T12:48:44Z","day":"15","publication":"Proceedings of the ACM on Programming Languages","has_accepted_license":"1","year":"2021","quality_controlled":"1","publisher":"Association for Computing Machinery","oa":1,"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.","title":"Transitioning from structural to nominal code with efficient gradual typing","author":[{"last_name":"Mühlböck","full_name":"Mühlböck, Fabian","orcid":"0000-0003-1548-0177","id":"6395C5F6-89DF-11E9-9C97-6BDFE5697425","first_name":"Fabian"},{"full_name":"Tate, Ross","last_name":"Tate","first_name":"Ross"}],"article_processing_charge":"No","user_id":"8b945eb4-e2f2-11eb-945a-df72226e66a9","citation":{"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.","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.","short":"F. Mühlböck, R. Tate, Proceedings of the ACM on Programming Languages 5 (2021).","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.","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","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."},"project":[{"call_identifier":"FWF","_id":"25F42A32-B435-11E9-9278-68D0E5697425","grant_number":"Z211","name":"The Wittgenstein Prize"}],"article_number":"127","volume":5,"file":[{"content_type":"application/pdf","relation":"main_file","access_level":"open_access","success":1,"checksum":"71011efd2da771cafdec7f0d9693f8c1","file_id":"10154","file_size":770269,"date_updated":"2021-10-19T12:52:23Z","creator":"fmuehlbo","file_name":"monnom-oopsla21.pdf","date_created":"2021-10-19T12:52:23Z"}],"language":[{"iso":"eng"}],"publication_identifier":{"eissn":["2475-1421"]},"publication_status":"published","month":"10","intvolume":" 5","oa_version":"Published Version","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."}],"file_date_updated":"2021-10-19T12:52:23Z","department":[{"_id":"ToHe"}],"ddc":["005"],"date_updated":"2021-11-12T11:30:07Z","status":"public","keyword":["gradual typing","gradual guarantee","nominal","structural","call tags"],"article_type":"original","type":"journal_article","conference":{"name":"OOPSLA: Object-Oriented Programming, Systems, Languages, and Applications","location":"Chicago, IL, United States","end_date":"2021-10-23","start_date":"2021-10-17"},"tmp":{"legal_code_url":"https://creativecommons.org/licenses/by-nd/4.0/legalcode","image":"/image/cc_by_nd.png","name":"Creative Commons Attribution-NoDerivatives 4.0 International (CC BY-ND 4.0)","short":"CC BY-ND (4.0)"},"_id":"10153"},{"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","publisher":"AAAI Press","quality_controlled":"1","oa":1,"day":"28","publication":"Proceedings of the AAAI Conference on Artificial Intelligence","has_accepted_license":"1","year":"2021","date_published":"2021-05-28T00:00:00Z","date_created":"2022-01-25T15:47:20Z","page":"11525-11535","project":[{"name":"The Wittgenstein Prize","grant_number":"Z211","call_identifier":"FWF","_id":"25F42A32-B435-11E9-9278-68D0E5697425"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","citation":{"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.","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.","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.","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.","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.","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."},"title":"On the verification of neural ODEs with stochastic guarantees","author":[{"full_name":"Grunbacher, Sophie","last_name":"Grunbacher","first_name":"Sophie"},{"full_name":"Hasani, Ramin","last_name":"Hasani","first_name":"Ramin"},{"id":"3DC22916-F248-11E8-B48F-1D18A9856A87","first_name":"Mathias","last_name":"Lechner","full_name":"Lechner, Mathias"},{"last_name":"Cyranka","full_name":"Cyranka, Jacek","first_name":"Jacek"},{"first_name":"Scott A","last_name":"Smolka","full_name":"Smolka, Scott A"},{"full_name":"Grosu, Radu","last_name":"Grosu","first_name":"Radu"}],"external_id":{"arxiv":["2012.08863"]},"article_processing_charge":"No","oa_version":"Published Version","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."}],"month":"05","intvolume":" 35","alternative_title":["Technical Tracks"],"main_file_link":[{"url":"https://ojs.aaai.org/index.php/AAAI/article/view/17372","open_access":"1"}],"file":[{"success":1,"file_id":"10680","checksum":"468d07041e282a1d46ffdae92f709630","content_type":"application/pdf","relation":"main_file","access_level":"open_access","file_name":"17372-Article Text-20866-1-2-20210518.pdf","date_created":"2022-01-26T07:38:08Z","file_size":286906,"date_updated":"2022-01-26T07:38:08Z","creator":"mlechner"}],"language":[{"iso":"eng"}],"publication_identifier":{"issn":["2159-5399"],"isbn":["978-1-57735-866-4"],"eissn":["2374-3468"]},"publication_status":"published","issue":"13","volume":35,"_id":"10669","status":"public","type":"conference","conference":{"name":"AAAI: Association for the Advancement of Artificial Intelligence","location":"Virtual","end_date":"2021-02-09","start_date":"2021-02-02"},"ddc":["000"],"date_updated":"2022-05-24T06:33:14Z","department":[{"_id":"GradSch"},{"_id":"ToHe"}],"file_date_updated":"2022-01-26T07:38:08Z"},{"external_id":{"arxiv":["2006.04439"]},"article_processing_charge":"No","author":[{"first_name":"Ramin","last_name":"Hasani","full_name":"Hasani, Ramin"},{"last_name":"Lechner","full_name":"Lechner, Mathias","first_name":"Mathias","id":"3DC22916-F248-11E8-B48F-1D18A9856A87"},{"first_name":"Alexander","last_name":"Amini","full_name":"Amini, Alexander"},{"full_name":"Rus, Daniela","last_name":"Rus","first_name":"Daniela"},{"first_name":"Radu","last_name":"Grosu","full_name":"Grosu, Radu"}],"title":"Liquid time-constant networks","citation":{"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.","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.","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.","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.","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."},"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","project":[{"name":"The Wittgenstein Prize","grant_number":"Z211","call_identifier":"FWF","_id":"25F42A32-B435-11E9-9278-68D0E5697425"}],"page":"7657-7666","date_created":"2022-01-25T15:48:36Z","date_published":"2021-05-28T00:00:00Z","year":"2021","has_accepted_license":"1","publication":"Proceedings of the AAAI Conference on Artificial Intelligence","day":"28","oa":1,"quality_controlled":"1","publisher":"AAAI Press","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.","file_date_updated":"2022-01-26T07:36:03Z","department":[{"_id":"GradSch"},{"_id":"ToHe"}],"date_updated":"2022-05-24T06:36:54Z","ddc":["000"],"conference":{"end_date":"2021-02-09","location":"Virtual","start_date":"2021-02-02","name":"AAAI: Association for the Advancement of Artificial Intelligence"},"type":"conference","status":"public","_id":"10671","issue":"9","volume":35,"publication_status":"published","publication_identifier":{"issn":["2159-5399"],"isbn":["978-1-57735-866-4"],"eissn":["2374-3468"]},"language":[{"iso":"eng"}],"file":[{"checksum":"0f06995fba06dbcfa7ed965fc66027ff","file_id":"10678","success":1,"access_level":"open_access","relation":"main_file","content_type":"application/pdf","date_created":"2022-01-26T07:36:03Z","file_name":"16936-Article Text-20430-1-2-20210518 (1).pdf","creator":"mlechner","date_updated":"2022-01-26T07:36:03Z","file_size":4302669}],"main_file_link":[{"open_access":"1","url":"https://ojs.aaai.org/index.php/AAAI/article/view/16936"}],"alternative_title":["Technical Tracks"],"intvolume":" 35","month":"05","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."}],"oa_version":"Published Version"}]