---
_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:
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checksum: 2bc8155b2526a70fba5b7301bc89dbd1
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issue: 5A
language:
- iso: eng
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- 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'
...