--- _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' ...