---
_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
arxiv: 1
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
orcid: 0000-0002-4681-1699
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
corr_author: '1'
date_created: 2022-01-25T15:15:02Z
date_published: 2021-05-28T00:00:00Z
date_updated: 2026-04-07T14:21:58Z
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
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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: Formal methods for the design and analysis of complex systems
- _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'
...
---
_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
arxiv: 1
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: 2025-05-19T11:28:08Z
day: '01'
ddc:
- '000'
department:
- _id: GradSch
- _id: ToHe
external_id:
arxiv:
- '2106.07091'
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: Formal methods for the design and analysis of complex systems
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: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 139
year: '2021'
...
---
_id: '10669'
abstract:
- lang: eng
text: "We show that Neural ODEs, an emerging class of timecontinuous neural networks,
can be verified by solving a set of global-optimization problems. For this purpose,
we introduce Stochastic Lagrangian Reachability (SLR), an\r\nabstraction-based
technique for constructing a tight Reachtube (an over-approximation of the set
of reachable states\r\nover a given time-horizon), and provide stochastic guarantees
in the form of confidence intervals for the Reachtube bounds. SLR inherently avoids
the infamous wrapping effect (accumulation of over-approximation errors) by performing
local optimization steps to expand safe regions instead of repeatedly forward-propagating
them as is done by deterministic reachability methods. To enable fast local optimizations,
we introduce a novel forward-mode adjoint sensitivity method to compute gradients
without the need for backpropagation. Finally, we establish asymptotic and non-asymptotic
convergence rates for SLR."
acknowledgement: "The authors would like to thank the reviewers for their insightful
comments. RH and RG were partially supported by\r\nHorizon-2020 ECSEL Project grant
No. 783163 (iDev40). RH was partially supported by Boeing. ML was supported\r\nin
part by the Austrian Science Fund (FWF) under grant Z211-N23 (Wittgenstein Award).
SG was funded by FWF\r\nproject W1255-N23. JC was partially supported by NAWA Polish
Returns grant PPN/PPO/2018/1/00029. SS was supported by NSF awards DCL-2040599,
CCF-1918225, and CPS-1446832.\r\n"
alternative_title:
- Technical Tracks
article_processing_charge: No
arxiv: 1
author:
- first_name: Sophie
full_name: Grunbacher, Sophie
last_name: Grunbacher
- first_name: Ramin
full_name: Hasani, Ramin
last_name: Hasani
- first_name: Mathias
full_name: Lechner, Mathias
id: 3DC22916-F248-11E8-B48F-1D18A9856A87
last_name: Lechner
- first_name: Jacek
full_name: Cyranka, Jacek
last_name: Cyranka
- first_name: Scott A
full_name: Smolka, Scott A
last_name: Smolka
- first_name: Radu
full_name: Grosu, Radu
last_name: Grosu
citation:
ama: 'Grunbacher S, Hasani R, Lechner M, Cyranka J, Smolka SA, Grosu R. On the verification
of neural ODEs with stochastic guarantees. In: Proceedings of the AAAI Conference
on Artificial Intelligence. Vol 35. AAAI Press; 2021:11525-11535.'
apa: 'Grunbacher, S., Hasani, R., Lechner, M., Cyranka, J., Smolka, S. A., &
Grosu, R. (2021). On the verification of neural ODEs with stochastic guarantees.
In Proceedings of the AAAI Conference on Artificial Intelligence (Vol.
35, pp. 11525–11535). Virtual: AAAI Press.'
chicago: Grunbacher, Sophie, Ramin Hasani, Mathias Lechner, Jacek Cyranka, Scott
A Smolka, and Radu Grosu. “On the Verification of Neural ODEs with Stochastic
Guarantees.” In Proceedings of the AAAI Conference on Artificial Intelligence,
35:11525–35. AAAI Press, 2021.
ieee: S. Grunbacher, R. Hasani, M. Lechner, J. Cyranka, S. A. Smolka, and R. Grosu,
“On the verification of neural ODEs with stochastic guarantees,” in Proceedings
of the AAAI Conference on Artificial Intelligence, Virtual, 2021, vol. 35,
no. 13, pp. 11525–11535.
ista: 'Grunbacher S, Hasani R, Lechner M, Cyranka J, Smolka SA, Grosu R. 2021. On
the verification of neural ODEs with stochastic guarantees. Proceedings of the
AAAI Conference on Artificial Intelligence. AAAI: Association for the Advancement
of Artificial Intelligence, Technical Tracks, vol. 35, 11525–11535.'
mla: Grunbacher, Sophie, et al. “On the Verification of Neural ODEs with Stochastic
Guarantees.” Proceedings of the AAAI Conference on Artificial Intelligence,
vol. 35, no. 13, AAAI Press, 2021, pp. 11525–35.
short: S. Grunbacher, R. Hasani, M. Lechner, J. Cyranka, S.A. Smolka, R. Grosu,
in:, Proceedings of the AAAI Conference on Artificial Intelligence, AAAI Press,
2021, pp. 11525–11535.
conference:
end_date: 2021-02-09
location: Virtual
name: 'AAAI: Association for the Advancement of Artificial Intelligence'
start_date: 2021-02-02
corr_author: '1'
date_created: 2022-01-25T15:47:20Z
date_published: 2021-05-28T00:00:00Z
date_updated: 2025-04-15T06:25:56Z
day: '28'
ddc:
- '000'
department:
- _id: GradSch
- _id: ToHe
external_id:
arxiv:
- '2012.08863'
file:
- access_level: open_access
checksum: 468d07041e282a1d46ffdae92f709630
content_type: application/pdf
creator: mlechner
date_created: 2022-01-26T07:38:08Z
date_updated: 2022-01-26T07:38:08Z
file_id: '10680'
file_name: 17372-Article Text-20866-1-2-20210518.pdf
file_size: 286906
relation: main_file
success: 1
file_date_updated: 2022-01-26T07:38:08Z
has_accepted_license: '1'
intvolume: ' 35'
issue: '13'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://ojs.aaai.org/index.php/AAAI/article/view/17372
month: '05'
oa: 1
oa_version: Published Version
page: 11525-11535
project:
- _id: 25F42A32-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: Z211
name: Formal methods for the design and analysis of complex systems
publication: Proceedings of the AAAI Conference on Artificial Intelligence
publication_identifier:
eissn:
- 2374-3468
isbn:
- 978-1-57735-866-4
issn:
- 2159-5399
publication_status: published
publisher: AAAI Press
quality_controlled: '1'
status: public
title: On the verification of neural ODEs with stochastic guarantees
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 35
year: '2021'
...
---
_id: '10671'
abstract:
- lang: eng
text: We introduce a new class of time-continuous recurrent neural network models.
Instead of declaring a learning system’s dynamics by implicit nonlinearities,
we construct networks of linear first-order dynamical systems modulated via nonlinear
interlinked gates. The resulting models represent dynamical systems with varying
(i.e., liquid) time-constants coupled to their hidden state, with outputs being
computed by numerical differential equation solvers. These neural networks exhibit
stable and bounded behavior, yield superior expressivity within the family of
neural ordinary differential equations, and give rise to improved performance
on time-series prediction tasks. To demonstrate these properties, we first take
a theoretical approach to find bounds over their dynamics, and compute their expressive
power by the trajectory length measure in a latent trajectory space. We then conduct
a series of time-series prediction experiments to manifest the approximation capability
of Liquid Time-Constant Networks (LTCs) compared to classical and modern RNNs.
acknowledgement: "R.H. and D.R. are partially supported by Boeing. R.H. and R.G. were
partially supported by the Horizon-2020 ECSEL\r\nProject grant No. 783163 (iDev40).
M.L. was supported in part by the Austrian Science Fund (FWF) under grant Z211-N23
(Wittgenstein Award). A.A. is supported by the National Science Foundation (NSF)
Graduate Research Fellowship Program. This research work is partially drawn from
the PhD dissertation of R.H."
alternative_title:
- Technical Tracks
article_processing_charge: No
arxiv: 1
author:
- first_name: Ramin
full_name: Hasani, Ramin
last_name: Hasani
- first_name: Mathias
full_name: Lechner, Mathias
id: 3DC22916-F248-11E8-B48F-1D18A9856A87
last_name: Lechner
- first_name: Alexander
full_name: Amini, Alexander
last_name: Amini
- first_name: Daniela
full_name: Rus, Daniela
last_name: Rus
- first_name: Radu
full_name: Grosu, Radu
last_name: Grosu
citation:
ama: 'Hasani R, Lechner M, Amini A, Rus D, Grosu R. Liquid time-constant networks.
In: Proceedings of the AAAI Conference on Artificial Intelligence. Vol
35. AAAI Press; 2021:7657-7666.'
apa: 'Hasani, R., Lechner, M., Amini, A., Rus, D., & Grosu, R. (2021). Liquid
time-constant networks. In Proceedings of the AAAI Conference on Artificial
Intelligence (Vol. 35, pp. 7657–7666). Virtual: AAAI Press.'
chicago: Hasani, Ramin, Mathias Lechner, Alexander Amini, Daniela Rus, and Radu
Grosu. “Liquid Time-Constant Networks.” In Proceedings of the AAAI Conference
on Artificial Intelligence, 35:7657–66. AAAI Press, 2021.
ieee: R. Hasani, M. Lechner, A. Amini, D. Rus, and R. Grosu, “Liquid time-constant
networks,” in Proceedings of the AAAI Conference on Artificial Intelligence,
Virtual, 2021, vol. 35, no. 9, pp. 7657–7666.
ista: 'Hasani R, Lechner M, Amini A, Rus D, Grosu R. 2021. Liquid time-constant
networks. Proceedings of the AAAI Conference on Artificial Intelligence. AAAI:
Association for the Advancement of Artificial Intelligence, Technical Tracks,
vol. 35, 7657–7666.'
mla: Hasani, Ramin, et al. “Liquid Time-Constant Networks.” Proceedings of the
AAAI Conference on Artificial Intelligence, vol. 35, no. 9, AAAI Press, 2021,
pp. 7657–66.
short: R. Hasani, M. Lechner, A. Amini, D. Rus, R. Grosu, in:, Proceedings of the
AAAI Conference on Artificial Intelligence, AAAI Press, 2021, pp. 7657–7666.
conference:
end_date: 2021-02-09
location: Virtual
name: 'AAAI: Association for the Advancement of Artificial Intelligence'
start_date: 2021-02-02
corr_author: '1'
date_created: 2022-01-25T15:48:36Z
date_published: 2021-05-28T00:00:00Z
date_updated: 2025-04-15T06:25:56Z
day: '28'
ddc:
- '000'
department:
- _id: GradSch
- _id: ToHe
external_id:
arxiv:
- '2006.04439'
file:
- access_level: open_access
checksum: 0f06995fba06dbcfa7ed965fc66027ff
content_type: application/pdf
creator: mlechner
date_created: 2022-01-26T07:36:03Z
date_updated: 2022-01-26T07:36:03Z
file_id: '10678'
file_name: 16936-Article Text-20430-1-2-20210518 (1).pdf
file_size: 4302669
relation: main_file
success: 1
file_date_updated: 2022-01-26T07:36:03Z
has_accepted_license: '1'
intvolume: ' 35'
issue: '9'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://ojs.aaai.org/index.php/AAAI/article/view/16936
month: '05'
oa: 1
oa_version: Published Version
page: 7657-7666
project:
- _id: 25F42A32-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: Z211
name: Formal methods for the design and analysis of complex systems
publication: Proceedings of the AAAI Conference on Artificial Intelligence
publication_identifier:
eissn:
- 2374-3468
isbn:
- 978-1-57735-866-4
issn:
- 2159-5399
publication_status: published
publisher: AAAI Press
quality_controlled: '1'
status: public
title: Liquid time-constant networks
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 35
year: '2021'
...
---
_id: '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
corr_author: '1'
date_created: 2022-01-26T08:01:30Z
date_published: 2021-10-01T00:00:00Z
date_updated: 2025-04-15T06:25:56Z
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:
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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: Formal methods for the design and analysis of complex systems
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'
scopus_import: '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: '8912'
abstract:
- lang: eng
text: "For automata, synchronization, the problem of bringing an automaton to a
particular state regardless of its initial state, is important. It has several
applications in practice and is related to a fifty-year-old conjecture on the
length of the shortest synchronizing word. Although using shorter words increases
the effectiveness in practice, finding a shortest one (which is not necessarily
unique) is NP-hard. For this reason, there exist various heuristics in the literature.
However, high-quality heuristics such as SynchroP producing relatively shorter
sequences are very expensive and can take hours when the automaton has tens of
thousands of states. The SynchroP heuristic has been frequently used as a benchmark
to evaluate the performance of the new heuristics. In this work, we first improve
the runtime of SynchroP and its variants by using algorithmic techniques. We then
focus on adapting SynchroP for many-core architectures,\r\nand overall, we obtain
more than 1000× speedup on GPUs compared to naive sequential implementation that
has been frequently used as a benchmark to evaluate new heuristics in the literature.
We also propose two SynchroP variants and evaluate their performance."
acknowledgement: This work was supported by The Scientific and Technological Research
Council of Turkey (TUBITAK) [grant number 114E569]. This research was supported
in part by the Austrian Science Fund (FWF) under grant Z211-N23 (Wittgenstein Award).
We would like to thank the authors of (Roman & Szykula, 2015) for providing their
heuristics implementations, which we used to compare our SynchroP implementation
as given in Table 11.
article_number: '114203'
article_processing_charge: No
article_type: original
author:
- first_name: Naci E
full_name: Sarac, Naci E
id: 8C6B42F8-C8E6-11E9-A03A-F2DCE5697425
last_name: Sarac
- first_name: Ömer Faruk
full_name: Altun, Ömer Faruk
last_name: Altun
- first_name: Kamil Tolga
full_name: Atam, Kamil Tolga
last_name: Atam
- first_name: Sertac
full_name: Karahoda, Sertac
last_name: Karahoda
- first_name: Kamer
full_name: Kaya, Kamer
last_name: Kaya
- first_name: Hüsnü
full_name: Yenigün, Hüsnü
last_name: Yenigün
citation:
ama: Sarac NE, Altun ÖF, Atam KT, Karahoda S, Kaya K, Yenigün H. Boosting expensive
synchronizing heuristics. Expert Systems with Applications. 2021;167(4).
doi:10.1016/j.eswa.2020.114203
apa: Sarac, N. E., Altun, Ö. F., Atam, K. T., Karahoda, S., Kaya, K., & Yenigün,
H. (2021). Boosting expensive synchronizing heuristics. Expert Systems with
Applications. Elsevier. https://doi.org/10.1016/j.eswa.2020.114203
chicago: Sarac, Naci E, Ömer Faruk Altun, Kamil Tolga Atam, Sertac Karahoda, Kamer
Kaya, and Hüsnü Yenigün. “Boosting Expensive Synchronizing Heuristics.” Expert
Systems with Applications. Elsevier, 2021. https://doi.org/10.1016/j.eswa.2020.114203.
ieee: N. E. Sarac, Ö. F. Altun, K. T. Atam, S. Karahoda, K. Kaya, and H. Yenigün,
“Boosting expensive synchronizing heuristics,” Expert Systems with Applications,
vol. 167, no. 4. Elsevier, 2021.
ista: Sarac NE, Altun ÖF, Atam KT, Karahoda S, Kaya K, Yenigün H. 2021. Boosting
expensive synchronizing heuristics. Expert Systems with Applications. 167(4),
114203.
mla: Sarac, Naci E., et al. “Boosting Expensive Synchronizing Heuristics.” Expert
Systems with Applications, vol. 167, no. 4, 114203, Elsevier, 2021, doi:10.1016/j.eswa.2020.114203.
short: N.E. Sarac, Ö.F. Altun, K.T. Atam, S. Karahoda, K. Kaya, H. Yenigün, Expert
Systems with Applications 167 (2021).
corr_author: '1'
date_created: 2020-12-02T13:34:25Z
date_published: 2021-04-01T00:00:00Z
date_updated: 2026-04-16T09:15:47Z
day: '01'
ddc:
- '000'
department:
- _id: ToHe
doi: 10.1016/j.eswa.2020.114203
external_id:
isi:
- '000640531100038'
file:
- access_level: open_access
checksum: 600c2f81bc898a725bcfa7cf26ff4fed
content_type: application/pdf
creator: esarac
date_created: 2020-12-02T13:33:51Z
date_updated: 2020-12-02T13:33:51Z
file_id: '8913'
file_name: synchroPaperRevised.pdf
file_size: 634967
relation: main_file
file_date_updated: 2020-12-02T13:33:51Z
has_accepted_license: '1'
intvolume: ' 167'
isi: 1
issue: '4'
language:
- iso: eng
month: '04'
oa: 1
oa_version: Submitted Version
project:
- _id: 25F42A32-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: Z211
name: Formal methods for the design and analysis of complex systems
publication: Expert Systems with Applications
publication_identifier:
eissn:
- 1873-6793
issn:
- 0957-4174
publication_status: published
publisher: Elsevier
quality_controlled: '1'
scopus_import: '1'
status: public
title: Boosting expensive synchronizing heuristics
type: journal_article
user_id: ba8df636-2132-11f1-aed0-ed93e2281fdd
volume: 167
year: '2021'
...
---
_id: '9200'
abstract:
- lang: eng
text: Formal design of embedded and cyber-physical systems relies on mathematical
modeling. In this paper, we consider the model class of hybrid automata whose
dynamics are defined by affine differential equations. Given a set of time-series
data, we present an algorithmic approach to synthesize a hybrid automaton exhibiting
behavior that is close to the data, up to a specified precision, and changes in
synchrony with the data. A fundamental problem in our synthesis algorithm is to
check membership of a time series in a hybrid automaton. Our solution integrates
reachability and optimization techniques for affine dynamical systems to obtain
both a sufficient and a necessary condition for membership, combined in a refinement
framework. The algorithm processes one time series at a time and hence can be
interrupted, provide an intermediate result, and be resumed. We report experimental
results demonstrating the applicability of our synthesis approach.
acknowledgement: This research was supported in part by the Austrian Science Fund
(FWF) under grant Z211-N23 (Wittgenstein Award) and the European Union’s Horizon
2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement
No. 754411.
article_processing_charge: No
arxiv: 1
author:
- first_name: Miriam
full_name: Garcia Soto, Miriam
id: 4B3207F6-F248-11E8-B48F-1D18A9856A87
last_name: Garcia Soto
orcid: 0000-0003-2936-5719
- 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: Christian
full_name: Schilling, Christian
id: 3A2F4DCE-F248-11E8-B48F-1D18A9856A87
last_name: Schilling
orcid: 0000-0003-3658-1065
citation:
ama: 'Garcia Soto M, Henzinger TA, Schilling C. Synthesis of hybrid automata with
affine dynamics from time-series data. In: HSCC ’21: Proceedings of the 24th
International Conference on Hybrid Systems: Computation and Control. Association
for Computing Machinery; 2021:2102.12734. doi:10.1145/3447928.3456704'
apa: 'Garcia Soto, M., Henzinger, T. A., & Schilling, C. (2021). Synthesis of
hybrid automata with affine dynamics from time-series data. In HSCC ’21: Proceedings
of the 24th International Conference on Hybrid Systems: Computation and Control
(p. 2102.12734). Nashville, TN, United States: Association for Computing Machinery.
https://doi.org/10.1145/3447928.3456704'
chicago: 'Garcia Soto, Miriam, Thomas A Henzinger, and Christian Schilling. “Synthesis
of Hybrid Automata with Affine Dynamics from Time-Series Data.” In HSCC ’21:
Proceedings of the 24th International Conference on Hybrid Systems: Computation
and Control, 2102.12734. Association for Computing Machinery, 2021. https://doi.org/10.1145/3447928.3456704.'
ieee: 'M. Garcia Soto, T. A. Henzinger, and C. Schilling, “Synthesis of hybrid automata
with affine dynamics from time-series data,” in HSCC ’21: Proceedings of the
24th International Conference on Hybrid Systems: Computation and Control,
Nashville, TN, United States, 2021, p. 2102.12734.'
ista: 'Garcia Soto M, Henzinger TA, Schilling C. 2021. Synthesis of hybrid automata
with affine dynamics from time-series data. HSCC ’21: Proceedings of the 24th
International Conference on Hybrid Systems: Computation and Control. HSCC: Hybrid
Systems - Computation and Control, 2102.12734.'
mla: 'Garcia Soto, Miriam, et al. “Synthesis of Hybrid Automata with Affine Dynamics
from Time-Series Data.” HSCC ’21: Proceedings of the 24th International Conference
on Hybrid Systems: Computation and Control, Association for Computing Machinery,
2021, p. 2102.12734, doi:10.1145/3447928.3456704.'
short: 'M. Garcia Soto, T.A. Henzinger, C. Schilling, in:, HSCC ’21: Proceedings
of the 24th International Conference on Hybrid Systems: Computation and Control,
Association for Computing Machinery, 2021, p. 2102.12734.'
conference:
end_date: 2021-05-21
location: Nashville, TN, United States
name: 'HSCC: Hybrid Systems - Computation and Control'
start_date: 2021-05-19
corr_author: '1'
date_created: 2021-02-26T16:30:39Z
date_published: 2021-05-01T00:00:00Z
date_updated: 2025-07-10T12:01:40Z
day: '01'
ddc:
- '000'
department:
- _id: ToHe
doi: 10.1145/3447928.3456704
ec_funded: 1
external_id:
arxiv:
- '2102.12734'
isi:
- '000932821700028'
file:
- access_level: open_access
checksum: 4c1202c1abf71384c3ee6fea88c2f80e
content_type: application/pdf
creator: kschuh
date_created: 2021-05-25T13:53:22Z
date_updated: 2021-05-25T13:53:22Z
file_id: '9424'
file_name: 2021_HSCC_Soto.pdf
file_size: 1474786
relation: main_file
success: 1
file_date_updated: 2021-05-25T13:53:22Z
has_accepted_license: '1'
isi: 1
keyword:
- hybrid automaton
- membership
- system identification
language:
- iso: eng
month: '05'
oa: 1
oa_version: Published Version
page: '2102.12734'
project:
- _id: 25F42A32-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: Z211
name: Formal methods for the design and analysis of complex systems
- _id: 260C2330-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '754411'
name: ISTplus - Postdoctoral Fellowships
publication: 'HSCC ''21: Proceedings of the 24th International Conference on Hybrid
Systems: Computation and Control'
publication_identifier:
isbn:
- '9781450383394'
publication_status: published
publisher: Association for Computing Machinery
quality_controlled: '1'
scopus_import: '1'
status: public
title: Synthesis of hybrid automata with affine dynamics from time-series data
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: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2021'
...
---
_id: '9239'
abstract:
- lang: eng
text: 'A graph game proceeds as follows: two players move a token through a graph
to produce a finite or infinite path, which determines the payoff of the game.
We study bidding games in which in each turn, an auction determines which player
moves the token. Bidding games were largely studied in combination with two variants
of first-price auctions called “Richman” and “poorman” bidding. We study taxman
bidding, which span the spectrum between the two. The game is parameterized by
a constant : portion τ of the winning bid is paid to the other player, and portion to
the bank. While finite-duration (reachability) taxman games have been studied
before, we present, for the first time, results on infinite-duration taxman games:
we unify, generalize, and simplify previous equivalences between bidding games
and a class of stochastic games called random-turn games.'
article_processing_charge: No
article_type: original
arxiv: 1
author:
- first_name: Guy
full_name: Avni, Guy
id: 463C8BC2-F248-11E8-B48F-1D18A9856A87
last_name: Avni
orcid: 0000-0001-5588-8287
- 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: Đorđe
full_name: Žikelić, Đorđe
last_name: Žikelić
citation:
ama: Avni G, Henzinger TA, Žikelić Đ. Bidding mechanisms in graph games. Journal
of Computer and System Sciences. 2021;119(8):133-144. doi:10.1016/j.jcss.2021.02.008
apa: Avni, G., Henzinger, T. A., & Žikelić, Đ. (2021). Bidding mechanisms in
graph games. Journal of Computer and System Sciences. Elsevier. https://doi.org/10.1016/j.jcss.2021.02.008
chicago: Avni, Guy, Thomas A Henzinger, and Đorđe Žikelić. “Bidding Mechanisms in
Graph Games.” Journal of Computer and System Sciences. Elsevier, 2021.
https://doi.org/10.1016/j.jcss.2021.02.008.
ieee: G. Avni, T. A. Henzinger, and Đ. Žikelić, “Bidding mechanisms in graph games,”
Journal of Computer and System Sciences, vol. 119, no. 8. Elsevier, pp.
133–144, 2021.
ista: Avni G, Henzinger TA, Žikelić Đ. 2021. Bidding mechanisms in graph games.
Journal of Computer and System Sciences. 119(8), 133–144.
mla: Avni, Guy, et al. “Bidding Mechanisms in Graph Games.” Journal of Computer
and System Sciences, vol. 119, no. 8, Elsevier, 2021, pp. 133–44, doi:10.1016/j.jcss.2021.02.008.
short: G. Avni, T.A. Henzinger, Đ. Žikelić, Journal of Computer and System Sciences
119 (2021) 133–144.
date_created: 2021-03-14T23:01:32Z
date_published: 2021-03-03T00:00:00Z
date_updated: 2025-07-10T11:53:57Z
day: '03'
department:
- _id: ToHe
doi: 10.1016/j.jcss.2021.02.008
external_id:
arxiv:
- '1905.03835'
isi:
- '000634149800009'
intvolume: ' 119'
isi: 1
issue: '8'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.1905.03835
month: '03'
oa: 1
oa_version: Preprint
page: 133-144
publication: Journal of Computer and System Sciences
publication_identifier:
eissn:
- 1090-2724
issn:
- 0022-0000
publication_status: published
publisher: Elsevier
quality_controlled: '1'
related_material:
record:
- id: '6884'
relation: earlier_version
status: public
scopus_import: '1'
status: public
title: Bidding mechanisms in graph games
type: journal_article
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
volume: 119
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
arxiv: 1
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.).
corr_author: '1'
date_created: 2021-03-23T05:38:48Z
date_published: 2021-03-21T00:00:00Z
date_updated: 2025-04-15T06:26:12Z
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: '9647'
abstract:
- lang: eng
text: 'Gene expression is regulated by the set of transcription factors (TFs) that
bind to the promoter. The ensuing regulating function is often represented as
a combinational logic circuit, where output (gene expression) is determined by
current input values (promoter bound TFs) only. However, the simultaneous arrival
of TFs is a strong assumption, since transcription and translation of genes introduce
intrinsic time delays and there is no global synchronisation among the arrival
times of different molecular species at their targets. We present an experimentally
implementable genetic circuit with two inputs and one output, which in the presence
of small delays in input arrival, exhibits qualitatively distinct population-level
phenotypes, over timescales that are longer than typical cell doubling times.
From a dynamical systems point of view, these phenotypes represent long-lived
transients: although they converge to the same value eventually, they do so after
a very long time span. The key feature of this toy model genetic circuit is that,
despite having only two inputs and one output, it is regulated by twenty-three
distinct DNA-TF configurations, two of which are more stable than others (DNA
looped states), one promoting and another blocking the expression of the output
gene. Small delays in input arrival time result in a majority of cells in the
population quickly reaching the stable state associated with the first input,
while exiting of this stable state occurs at a slow timescale. In order to mechanistically
model the behaviour of this genetic circuit, we used a rule-based modelling language,
and implemented a grid-search to find parameter combinations giving rise to long-lived
transients. Our analysis shows that in the absence of feedback, there exist path-dependent
gene regulatory mechanisms based on the long timescale of transients. The behaviour
of this toy model circuit suggests that gene regulatory networks can exploit event
timing to create phenotypes, and it opens the possibility that they could use
event timing to memorise events, without regulatory feedback. The model reveals
the importance of (i) mechanistically modelling the transitions between the different
DNA-TF states, and (ii) employing transient analysis thereof.'
acknowledgement: 'Tatjana Petrov’s research was supported in part by SNSF Advanced
Postdoctoral Mobility Fellowship grant number P300P2 161067, the Ministry of Science,
Research and the Arts of the state of Baden-Wurttemberg, and the DFG Centre of Excellence
2117 ‘Centre for the Advanced Study of Collective Behaviour’ (ID: 422037984). Claudia
Igler is the recipient of a DOC Fellowship of the Austrian Academy of Sciences.
Thomas A. Henzinger’s research was supported in part by the Austrian Science Fund
(FWF) under grant Z211-N23 (Wittgenstein Award).'
article_processing_charge: No
article_type: original
author:
- first_name: Tatjana
full_name: Petrov, Tatjana
last_name: Petrov
- first_name: Claudia
full_name: Igler, Claudia
id: 46613666-F248-11E8-B48F-1D18A9856A87
last_name: Igler
- first_name: Ali
full_name: Sezgin, Ali
id: 4C7638DA-F248-11E8-B48F-1D18A9856A87
last_name: Sezgin
- 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: Calin C
full_name: Guet, Calin C
id: 47F8433E-F248-11E8-B48F-1D18A9856A87
last_name: Guet
orcid: 0000-0001-6220-2052
citation:
ama: Petrov T, Igler C, Sezgin A, Henzinger TA, Guet CC. Long lived transients in
gene regulation. Theoretical Computer Science. 2021;893:1-16. doi:10.1016/j.tcs.2021.05.023
apa: Petrov, T., Igler, C., Sezgin, A., Henzinger, T. A., & Guet, C. C. (2021).
Long lived transients in gene regulation. Theoretical Computer Science.
Elsevier. https://doi.org/10.1016/j.tcs.2021.05.023
chicago: Petrov, Tatjana, Claudia Igler, Ali Sezgin, Thomas A Henzinger, and Calin
C Guet. “Long Lived Transients in Gene Regulation.” Theoretical Computer Science.
Elsevier, 2021. https://doi.org/10.1016/j.tcs.2021.05.023.
ieee: T. Petrov, C. Igler, A. Sezgin, T. A. Henzinger, and C. C. Guet, “Long lived
transients in gene regulation,” Theoretical Computer Science, vol. 893.
Elsevier, pp. 1–16, 2021.
ista: Petrov T, Igler C, Sezgin A, Henzinger TA, Guet CC. 2021. Long lived transients
in gene regulation. Theoretical Computer Science. 893, 1–16.
mla: Petrov, Tatjana, et al. “Long Lived Transients in Gene Regulation.” Theoretical
Computer Science, vol. 893, Elsevier, 2021, pp. 1–16, doi:10.1016/j.tcs.2021.05.023.
short: T. Petrov, C. Igler, A. Sezgin, T.A. Henzinger, C.C. Guet, Theoretical Computer
Science 893 (2021) 1–16.
corr_author: '1'
date_created: 2021-07-11T22:01:18Z
date_published: 2021-06-04T00:00:00Z
date_updated: 2025-04-15T06:25:56Z
day: '04'
ddc:
- '004'
department:
- _id: ToHe
- _id: CaGu
doi: 10.1016/j.tcs.2021.05.023
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intvolume: ' 893'
isi: 1
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month: '06'
oa: 1
oa_version: Published Version
page: 1-16
project:
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call_identifier: FWF
grant_number: Z211
name: Formal methods for the design and analysis of complex systems
publication: Theoretical Computer Science
publication_identifier:
issn:
- 0304-3975
publication_status: published
publisher: Elsevier
quality_controlled: '1'
scopus_import: '1'
status: public
title: Long lived transients in gene regulation
tmp:
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name: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
(CC BY-NC-ND 4.0)
short: CC BY-NC-ND (4.0)
type: journal_article
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
volume: 893
year: '2021'
...
---
_id: '9946'
abstract:
- lang: eng
text: We argue that the time is ripe to investigate differential monitoring, in
which the specification of a program's behavior is implicitly given by a second
program implementing the same informal specification. Similar ideas have been
proposed before, and are currently implemented in restricted form for testing
and specialized run-time analyses, aspects of which we combine. We discuss the
challenges of implementing differential monitoring as a general-purpose, black-box
run-time monitoring framework, and present promising results of a preliminary
implementation, showing low monitoring overheads for diverse programs.
acknowledgement: The authors would like to thank Borzoo Bonakdarpour, Derek Dreyer,
Adrian Francalanza, Owolabi Legunsen, Matthew Milano, Manuel Rigger, Cesar Sanchez,
and the members of the IST Verification Seminar for their helpful comments and insights
on various stages of this work, as well as the reviewers of RV’21 for their helpful
suggestions on the actual paper.
alternative_title:
- IST Austria Technical Report
article_processing_charge: No
author:
- first_name: Fabian
full_name: Mühlböck, Fabian
id: 6395C5F6-89DF-11E9-9C97-6BDFE5697425
last_name: Mühlböck
orcid: 0000-0003-1548-0177
- first_name: Thomas A
full_name: Henzinger, Thomas A
id: 40876CD8-F248-11E8-B48F-1D18A9856A87
last_name: Henzinger
orcid: 0000-0002-2985-7724
citation:
ama: Mühlböck F, Henzinger TA. Differential Monitoring. IST Austria; 2021.
doi:10.15479/AT:ISTA:9946
apa: Mühlböck, F., & Henzinger, T. A. (2021). Differential monitoring.
IST Austria. https://doi.org/10.15479/AT:ISTA:9946
chicago: Mühlböck, Fabian, and Thomas A Henzinger. Differential Monitoring.
IST Austria, 2021. https://doi.org/10.15479/AT:ISTA:9946.
ieee: F. Mühlböck and T. A. Henzinger, Differential monitoring. IST Austria,
2021.
ista: Mühlböck F, Henzinger TA. 2021. Differential monitoring, IST Austria, 17p.
mla: Mühlböck, Fabian, and Thomas A. Henzinger. Differential Monitoring.
IST Austria, 2021, doi:10.15479/AT:ISTA:9946.
short: F. Mühlböck, T.A. Henzinger, Differential Monitoring, IST Austria, 2021.
date_created: 2021-08-20T20:00:37Z
date_published: 2021-09-01T00:00:00Z
date_updated: 2025-04-15T06:55:00Z
day: '01'
ddc:
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doi: 10.15479/AT:ISTA:9946
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creator: fmuehlbo
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file_id: '9948'
file_name: differentialmonitoring-techreport.pdf
file_size: '320453'
relation: main_file
file_date_updated: 2021-09-03T12:34:28Z
has_accepted_license: '1'
keyword:
- run-time verification
- software engineering
- implicit specification
language:
- iso: eng
month: '09'
oa: 1
oa_version: Published Version
page: '17'
project:
- _id: 25F42A32-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: Z211
name: Formal methods for the design and analysis of complex systems
publication_identifier:
issn:
- 2664-1690
publication_status: published
publisher: IST Austria
related_material:
record:
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status: public
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relation: other
status: public
status: public
title: Differential monitoring
type: technical_report
user_id: 8b945eb4-e2f2-11eb-945a-df72226e66a9
year: '2021'
...
---
_id: '10674'
abstract:
- lang: eng
text: 'In two-player games on graphs, the players move a token through a graph to
produce an infinite path, which determines the winner of the game. Such games
are central in formal methods since they model the interaction between a non-terminating
system and its environment. In bidding games the players bid for the right to
move the token: in each round, the players simultaneously submit bids, and the
higher bidder moves the token and pays the other player. Bidding games are known
to have a clean and elegant mathematical structure that relies on the ability
of the players to submit arbitrarily small bids. Many applications, however, require
a fixed granularity for the bids, which can represent, for example, the monetary
value expressed in cents. We study, for the first time, the combination of discrete-bidding
and infinite-duration games. Our most important result proves that these games
form a large determined subclass of concurrent games, where determinacy is the
strong property that there always exists exactly one player who can guarantee
winning the game. In particular, we show that, in contrast to non-discrete bidding
games, the mechanism with which tied bids are resolved plays an important role
in discrete-bidding games. We study several natural tie-breaking mechanisms and
show that, while some do not admit determinacy, most natural mechanisms imply
determinacy for every pair of initial budgets.'
acknowledgement: "This research was supported in part by the Austrian Science Fund
(FWF) under grants S11402-N23 (RiSE/SHiNE), Z211-N23 (Wittgenstein Award), and M
2369-N33 (Meitner fellowship).\r\n"
article_processing_charge: No
article_type: original
arxiv: 1
author:
- first_name: Milad
full_name: Aghajohari, Milad
last_name: Aghajohari
- first_name: Guy
full_name: Avni, Guy
id: 463C8BC2-F248-11E8-B48F-1D18A9856A87
last_name: Avni
orcid: 0000-0001-5588-8287
- first_name: Thomas A
full_name: Henzinger, Thomas A
id: 40876CD8-F248-11E8-B48F-1D18A9856A87
last_name: Henzinger
orcid: 0000-0002-2985-7724
citation:
ama: Aghajohari M, Avni G, Henzinger TA. Determinacy in discrete-bidding infinite-duration
games. Logical Methods in Computer Science. 2021;17(1):10:1-10:23. doi:10.23638/LMCS-17(1:10)2021
apa: Aghajohari, M., Avni, G., & Henzinger, T. A. (2021). Determinacy in discrete-bidding
infinite-duration games. Logical Methods in Computer Science. EPI Sciences.
https://doi.org/10.23638/LMCS-17(1:10)2021
chicago: Aghajohari, Milad, Guy Avni, and Thomas A Henzinger. “Determinacy in Discrete-Bidding
Infinite-Duration Games.” Logical Methods in Computer Science. EPI Sciences,
2021. https://doi.org/10.23638/LMCS-17(1:10)2021.
ieee: M. Aghajohari, G. Avni, and T. A. Henzinger, “Determinacy in discrete-bidding
infinite-duration games,” Logical Methods in Computer Science, vol. 17,
no. 1. EPI Sciences, p. 10:1-10:23, 2021.
ista: Aghajohari M, Avni G, Henzinger TA. 2021. Determinacy in discrete-bidding
infinite-duration games. Logical Methods in Computer Science. 17(1), 10:1-10:23.
mla: Aghajohari, Milad, et al. “Determinacy in Discrete-Bidding Infinite-Duration
Games.” Logical Methods in Computer Science, vol. 17, no. 1, EPI Sciences,
2021, p. 10:1-10:23, doi:10.23638/LMCS-17(1:10)2021.
short: M. Aghajohari, G. Avni, T.A. Henzinger, Logical Methods in Computer Science
17 (2021) 10:1-10:23.
corr_author: '1'
das_tickbox: '1'
date_created: 2022-01-25T16:32:13Z
date_published: 2021-02-03T00:00:00Z
date_updated: 2026-07-06T13:21:45Z
day: '03'
ddc:
- '510'
department:
- _id: ToHe
doi: 10.23638/LMCS-17(1:10)2021
external_id:
arxiv:
- '1905.03588'
isi:
- '000658724600010'
file:
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checksum: b35586a50ed1ca8f44767de116d18d81
content_type: application/pdf
creator: alisjak
date_created: 2022-01-26T08:04:50Z
date_updated: 2022-01-26T08:04:50Z
file_id: '10690'
file_name: 2021_LMCS_AGHAJOHAR.pdf
file_size: 819878
relation: main_file
success: 1
file_date_updated: 2022-01-26T08:04:50Z
has_accepted_license: '1'
intvolume: ' 17'
isi: 1
issue: '1'
keyword:
- computer science
- computer science and game theory
- logic in computer science
language:
- iso: eng
month: '02'
oa: 1
oa_version: Published Version
page: 10:1-10:23
project:
- _id: 264B3912-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: M02369
name: Formal Methods meets Algorithmic Game Theory
- _id: 25F2ACDE-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: S11402-N23
name: Rigorous Systems Engineering
- _id: 25F42A32-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: Z211
name: Formal methods for the design and analysis of complex systems
publication: Logical Methods in Computer Science
publication_identifier:
eissn:
- 1860-5974
publication_status: published
publisher: EPI Sciences
quality_controlled: '1'
scopus_import: '1'
status: public
title: Determinacy in discrete-bidding infinite-duration games
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 17
year: '2021'
...
---
OA_place: repository
OA_type: green
_id: '10666'
abstract:
- lang: eng
text: Adversarial training is an effective method to train deep learning models
that are resilient to norm-bounded perturbations, with the cost of nominal performance
drop. While adversarial training appears to enhance the robustness and safety
of a deep model deployed in open-world decision-critical applications, counterintuitively,
it induces undesired behaviors in robot learning settings. In this paper, we show
theoretically and experimentally that neural controllers obtained via adversarial
training are subjected to three types of defects, namely transient, systematic,
and conditional errors. We first generalize adversarial training to a safety-domain
optimization scheme allowing for more generic specifications. We then prove that
such a learning process tends to cause certain error profiles. We support our
theoretical results by a thorough experimental safety analysis in a robot-learning
task. Our results suggest that adversarial training is not yet ready for robot
learning.
acknowledgement: M.L. and T.A.H. are supported in part by the Austrian Science Fund
(FWF) under grant Z211-N23 (Wittgenstein Award). R.H. and D.R. are supported by
Boeing and R.G. by Horizon-2020 ECSEL Project grant no. 783163 (iDev40).
article_processing_charge: No
arxiv: 1
author:
- first_name: Mathias
full_name: Lechner, Mathias
id: 3DC22916-F248-11E8-B48F-1D18A9856A87
last_name: Lechner
- first_name: Ramin
full_name: Hasani, Ramin
last_name: Hasani
- first_name: Radu
full_name: Grosu, Radu
last_name: Grosu
- first_name: Daniela
full_name: Rus, Daniela
last_name: Rus
- first_name: Thomas A
full_name: Henzinger, Thomas A
id: 40876CD8-F248-11E8-B48F-1D18A9856A87
last_name: Henzinger
orcid: 0000-0002-2985-7724
citation:
ama: 'Lechner M, Hasani R, Grosu R, Rus D, Henzinger TA. Adversarial training is
not ready for robot learning. In: 2021 IEEE International Conference on Robotics
and Automation. IEEE; 2021:4140-4147. doi:10.1109/ICRA48506.2021.9561036'
apa: 'Lechner, M., Hasani, R., Grosu, R., Rus, D., & Henzinger, T. A. (2021).
Adversarial training is not ready for robot learning. In 2021 IEEE International
Conference on Robotics and Automation (pp. 4140–4147). Xi’an, China: IEEE.
https://doi.org/10.1109/ICRA48506.2021.9561036'
chicago: Lechner, Mathias, Ramin Hasani, Radu Grosu, Daniela Rus, and Thomas A Henzinger.
“Adversarial Training Is Not Ready for Robot Learning.” In 2021 IEEE International
Conference on Robotics and Automation, 4140–47. IEEE, 2021. https://doi.org/10.1109/ICRA48506.2021.9561036.
ieee: M. Lechner, R. Hasani, R. Grosu, D. Rus, and T. A. Henzinger, “Adversarial
training is not ready for robot learning,” in 2021 IEEE International Conference
on Robotics and Automation, Xi’an, China, 2021, pp. 4140–4147.
ista: 'Lechner M, Hasani R, Grosu R, Rus D, Henzinger TA. 2021. Adversarial training
is not ready for robot learning. 2021 IEEE International Conference on Robotics
and Automation. ICRA: International Conference on Robotics and Automation, 4140–4147.'
mla: Lechner, Mathias, et al. “Adversarial Training Is Not Ready for Robot Learning.”
2021 IEEE International Conference on Robotics and Automation, IEEE, 2021,
pp. 4140–47, doi:10.1109/ICRA48506.2021.9561036.
short: M. Lechner, R. Hasani, R. Grosu, D. Rus, T.A. Henzinger, in:, 2021 IEEE International
Conference on Robotics and Automation, IEEE, 2021, pp. 4140–4147.
conference:
end_date: 2021-06-05
location: Xi'an, China
name: 'ICRA: International Conference on Robotics and Automation'
start_date: 2021-05-30
das_tickbox: '1'
date_created: 2022-01-25T15:44:54Z
date_published: 2021-06-01T00:00:00Z
date_updated: 2026-07-07T06:20:35Z
day: '01'
ddc:
- '000'
department:
- _id: GradSch
- _id: ToHe
doi: 10.1109/ICRA48506.2021.9561036
external_id:
arxiv:
- '2103.08187'
isi:
- '000765738803040'
has_accepted_license: '1'
isi: 1
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/2103.08187
month: '06'
oa: 1
oa_version: Preprint
page: 4140-4147
project:
- _id: 25F42A32-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: Z211
name: Formal methods for the design and analysis of complex systems
publication: 2021 IEEE International Conference on Robotics and Automation
publication_identifier:
eisbn:
- 978-1-7281-9077-8
eissn:
- 2577-087X
isbn:
- 978-1-7281-9078-5
issn:
- 1050-4729
publication_status: published
publisher: IEEE
quality_controlled: '1'
related_material:
record:
- id: '11362'
relation: dissertation_contains
status: public
scopus_import: '1'
status: public
title: Adversarial training is not ready for robot learning
tmp:
image: /images/cc_by_nc_nd.png
legal_code_url: https://creativecommons.org/licenses/by-nc-nd/3.0/legalcode
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3.0)
short: CC BY-NC-ND (3.0)
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2021'
...
---
_id: '10667'
abstract:
- lang: eng
text: Bayesian neural networks (BNNs) place distributions over the weights of a
neural network to model uncertainty in the data and the network's prediction.
We consider the problem of verifying safety when running a Bayesian neural network
policy in a feedback loop with infinite time horizon systems. Compared to the
existing sampling-based approaches, which are inapplicable to the infinite time
horizon setting, we train a separate deterministic neural network that serves
as an infinite time horizon safety certificate. In particular, we show that the
certificate network guarantees the safety of the system over a subset of the BNN
weight posterior's support. Our method first computes a safe weight set and then
alters the BNN's weight posterior to reject samples outside this set. Moreover,
we show how to extend our approach to a safe-exploration reinforcement learning
setting, in order to avoid unsafe trajectories during the training of the policy.
We evaluate our approach on a series of reinforcement learning benchmarks, including
non-Lyapunovian safety specifications.
acknowledgement: This research was supported in part by the Austrian Science Fund
(FWF) under grant Z211-N23 (Wittgenstein Award), 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.
alternative_title:
- ' Advances in Neural Information Processing Systems'
article_processing_charge: No
arxiv: 1
author:
- first_name: Mathias
full_name: Lechner, Mathias
id: 3DC22916-F248-11E8-B48F-1D18A9856A87
last_name: Lechner
- first_name: Ðorđe
full_name: Žikelić, Ðorđe
last_name: Žikelić
- first_name: Krishnendu
full_name: Chatterjee, Krishnendu
id: 2E5DCA20-F248-11E8-B48F-1D18A9856A87
last_name: Chatterjee
orcid: 0000-0002-4561-241X
- first_name: Thomas A
full_name: Henzinger, Thomas A
id: 40876CD8-F248-11E8-B48F-1D18A9856A87
last_name: Henzinger
orcid: 0000-0002-2985-7724
citation:
ama: 'Lechner M, Žikelić Ð, Chatterjee K, Henzinger TA. Infinite time horizon safety
of Bayesian neural networks. In: 35th Conference on Neural Information Processing
Systems. Neural Information Processing Systems Foundation; 2021. doi:10.48550/arXiv.2111.03165'
apa: 'Lechner, M., Žikelić, Ð., Chatterjee, K., & Henzinger, T. A. (2021). Infinite
time horizon safety of Bayesian neural networks. In 35th Conference on Neural
Information Processing Systems. Virtual: Neural Information Processing Systems
Foundation. https://doi.org/10.48550/arXiv.2111.03165'
chicago: Lechner, Mathias, Ðorđe Žikelić, Krishnendu Chatterjee, and Thomas A Henzinger.
“Infinite Time Horizon Safety of Bayesian Neural Networks.” In 35th Conference
on Neural Information Processing Systems. Neural Information Processing Systems
Foundation, 2021. https://doi.org/10.48550/arXiv.2111.03165.
ieee: M. Lechner, Ð. Žikelić, K. Chatterjee, and T. A. Henzinger, “Infinite time
horizon safety of Bayesian neural networks,” in 35th Conference on Neural Information
Processing Systems, Virtual, 2021.
ista: 'Lechner M, Žikelić Ð, Chatterjee K, Henzinger TA. 2021. Infinite time horizon
safety of Bayesian neural networks. 35th Conference on Neural Information Processing
Systems. NeurIPS: Neural Information Processing Systems, Advances in Neural Information
Processing Systems, .'
mla: Lechner, Mathias, et al. “Infinite Time Horizon Safety of Bayesian Neural Networks.”
35th Conference on Neural Information Processing Systems, Neural Information
Processing Systems Foundation, 2021, doi:10.48550/arXiv.2111.03165.
short: M. Lechner, Ð. Žikelić, K. Chatterjee, T.A. Henzinger, in:, 35th Conference
on Neural Information Processing Systems, Neural Information Processing Systems
Foundation, 2021.
conference:
end_date: 2021-12-10
location: Virtual
name: 'NeurIPS: Neural Information Processing Systems'
start_date: 2021-12-06
corr_author: '1'
das_tickbox: '1'
date_created: 2022-01-25T15:45:58Z
date_published: 2021-12-01T00:00:00Z
date_updated: 2026-07-07T06:49:10Z
day: '01'
ddc:
- '000'
department:
- _id: GradSch
- _id: ToHe
- _id: KrCh
doi: 10.48550/arXiv.2111.03165
ec_funded: 1
external_id:
arxiv:
- '2111.03165'
file:
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checksum: 0fc0f852525c10dda9cc9ffea07fb4e4
content_type: application/pdf
creator: mlechner
date_created: 2022-01-26T07:39:59Z
date_updated: 2022-01-26T07:39:59Z
file_id: '10682'
file_name: infinite_time_horizon_safety_o.pdf
file_size: 452492
relation: main_file
success: 1
file_date_updated: 2022-01-26T07:39:59Z
has_accepted_license: '1'
language:
- iso: eng
main_file_link:
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url: https://proceedings.neurips.cc/paper/2021/hash/544defa9fddff50c53b71c43e0da72be-Abstract.html
month: '12'
oa: 1
oa_version: Published Version
project:
- _id: 2564DBCA-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '665385'
name: International IST Doctoral Program
- _id: 0599E47C-7A3F-11EA-A408-12923DDC885E
call_identifier: H2020
grant_number: '863818'
name: 'Formal Methods for Stochastic Models: Algorithms and Applications'
- _id: 25F42A32-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: Z211
name: Formal methods for the design and analysis of complex systems
publication: 35th Conference on Neural Information Processing Systems
publication_identifier:
issn:
- 1049-5258
publication_status: published
publisher: Neural Information Processing Systems Foundation
quality_controlled: '1'
related_material:
record:
- id: '11362'
relation: dissertation_contains
status: public
status: public
title: Infinite time horizon safety of Bayesian neural networks
tmp:
image: /images/cc_by_nc_nd.png
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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: 2DF688A6-F248-11E8-B48F-1D18A9856A87
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
arxiv: 1
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.
Neural Information Processing Systems Foundation; 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: Neural Information Processing
Systems Foundation.'
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. Neural Information Processing
Systems Foundation, 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, Neural Information
Processing Systems Foundation, 2021.
short: C.J. Vorbach, R. Hasani, A. Amini, M. Lechner, D. Rus, in:, 35th Conference
on Neural Information Processing Systems, Neural Information Processing Systems
Foundation, 2021.
conference:
end_date: 2021-12-10
location: Virtual
name: 'NeurIPS: Neural Information Processing Systems'
start_date: 2021-12-06
das_tickbox: '1'
date_created: 2022-01-25T15:47:50Z
date_published: 2021-12-01T00:00:00Z
date_updated: 2026-07-07T06:49:46Z
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: Formal methods for the design and analysis of complex systems
publication: 35th Conference on Neural Information Processing Systems
publication_identifier:
issn:
- 1049-5258
publication_status: published
publisher: Neural Information Processing Systems Foundation
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: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2021'
...
---
_id: '9356'
abstract:
- lang: eng
text: 'In runtime verification, a monitor watches a trace of a system and, if possible,
decides after observing each finite prefix whether or not the unknown infinite
trace satisfies a given specification. We generalize the theory of runtime verification
to monitors that attempt to estimate numerical values of quantitative trace properties
(instead of attempting to conclude boolean values of trace specifications), such
as maximal or average response time along a trace. Quantitative monitors are approximate:
with every finite prefix, they can improve their estimate of the infinite trace''s
unknown property value. Consequently, quantitative monitors can be compared with
regard to a precision-cost trade-off: better approximations of the property value
require more monitor resources, such as states (in the case of finite-state monitors)
or registers, and additional resources yield better approximations. We introduce
a formal framework for quantitative and approximate monitoring, show how it conservatively
generalizes the classical boolean setting for monitoring, and give several precision-cost
trade-offs for monitors. For example, we prove that there are quantitative properties
for which every additional register improves monitoring precision.'
acknowledgement: We thank the anonymous reviewers for their helpful comments. This
research was supported in part by the Austrian Science Fund (FWF) under grant Z211-N23
(Wittgenstein Award).
article_number: '9470547'
article_processing_charge: No
arxiv: 1
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: Naci E
full_name: Sarac, Naci E
id: 8C6B42F8-C8E6-11E9-A03A-F2DCE5697425
last_name: Sarac
citation:
ama: 'Henzinger TA, Sarac NE. Quantitative and approximate monitoring. In: Proceedings
of the 36th Annual ACM/IEEE Symposium on Logic in Computer Science. Institute
of Electrical and Electronics Engineers; 2021. doi:10.1109/LICS52264.2021.9470547'
apa: 'Henzinger, T. A., & Sarac, N. E. (2021). Quantitative and approximate
monitoring. In Proceedings of the 36th Annual ACM/IEEE Symposium on Logic in
Computer Science. Online: Institute of Electrical and Electronics Engineers.
https://doi.org/10.1109/LICS52264.2021.9470547'
chicago: Henzinger, Thomas A, and Naci E Sarac. “Quantitative and Approximate Monitoring.”
In Proceedings of the 36th Annual ACM/IEEE Symposium on Logic in Computer Science.
Institute of Electrical and Electronics Engineers, 2021. https://doi.org/10.1109/LICS52264.2021.9470547.
ieee: T. A. Henzinger and N. E. Sarac, “Quantitative and approximate monitoring,”
in Proceedings of the 36th Annual ACM/IEEE Symposium on Logic in Computer Science,
Online, 2021.
ista: 'Henzinger TA, Sarac NE. 2021. Quantitative and approximate monitoring. Proceedings
of the 36th Annual ACM/IEEE Symposium on Logic in Computer Science. LICS: Logic
in Computer Science, 9470547.'
mla: Henzinger, Thomas A., and Naci E. Sarac. “Quantitative and Approximate Monitoring.”
Proceedings of the 36th Annual ACM/IEEE Symposium on Logic in Computer Science,
9470547, Institute of Electrical and Electronics Engineers, 2021, doi:10.1109/LICS52264.2021.9470547.
short: T.A. Henzinger, N.E. Sarac, in:, Proceedings of the 36th Annual ACM/IEEE
Symposium on Logic in Computer Science, Institute of Electrical and Electronics
Engineers, 2021.
conference:
end_date: 2021-07-02
location: Online
name: 'LICS: Logic in Computer Science'
start_date: 2021-06-29
date_created: 2021-04-30T17:30:47Z
date_published: 2021-06-29T00:00:00Z
date_updated: 2026-07-27T12:48:18Z
day: '29'
ddc:
- '000'
department:
- _id: GradSch
- _id: ToHe
doi: 10.1109/LICS52264.2021.9470547
external_id:
arxiv:
- '2105.08353'
isi:
- '000947350400021'
file:
- access_level: open_access
checksum: 6e4cba3f72775f479c5b1b75d1a4a0c4
content_type: application/pdf
creator: esarac
date_created: 2021-06-16T08:23:54Z
date_updated: 2021-06-16T08:23:54Z
file_id: '9557'
file_name: qam.pdf
file_size: 641990
relation: main_file
success: 1
file_date_updated: 2021-06-16T08:23:54Z
has_accepted_license: '1'
isi: 1
language:
- iso: eng
month: '06'
oa: 1
oa_version: Published Version
project:
- _id: 25F42A32-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: Z211
name: Formal methods for the design and analysis of complex systems
publication: Proceedings of the 36th Annual ACM/IEEE Symposium on Logic in Computer
Science
publication_status: published
publisher: Institute of Electrical and Electronics Engineers
quality_controlled: '1'
related_material:
record:
- id: '20147'
relation: dissertation_contains
status: public
scopus_import: '1'
status: public
title: Quantitative and approximate monitoring
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2021'
...
---
_id: '10861'
abstract:
- lang: eng
text: We introduce in this paper AMT2.0, a tool for qualitative and quantitative
analysis of hybrid continuous and Boolean signals that combine numerical values
and discrete events. The evaluation of the signals is based on rich temporal specifications
expressed in extended signal temporal logic, which integrates timed regular expressions
within signal temporal logic. The tool features qualitative monitoring (property
satisfaction checking), trace diagnostics for explaining and justifying property
violations and specification-driven measurement of quantitative features of the
signal. We demonstrate the tool functionality on several running examples and
case studies, and evaluate its performance.
article_processing_charge: No
article_type: original
author:
- first_name: Dejan
full_name: Nickovic, Dejan
id: 41BCEE5C-F248-11E8-B48F-1D18A9856A87
last_name: Nickovic
- first_name: Olivier
full_name: Lebeltel, Olivier
last_name: Lebeltel
- first_name: Oded
full_name: Maler, Oded
last_name: Maler
- first_name: Thomas
full_name: Ferrere, Thomas
id: 40960E6E-F248-11E8-B48F-1D18A9856A87
last_name: Ferrere
orcid: 0000-0001-5199-3143
- first_name: Dogan
full_name: Ulus, Dogan
last_name: Ulus
citation:
ama: 'Nickovic D, Lebeltel O, Maler O, Ferrere T, Ulus D. AMT 2.0: Qualitative and
quantitative trace analysis with extended signal temporal logic. International
Journal on Software Tools for Technology Transfer. 2020;22(6):741-758. doi:10.1007/s10009-020-00582-z'
apa: 'Nickovic, D., Lebeltel, O., Maler, O., Ferrere, T., & Ulus, D. (2020).
AMT 2.0: Qualitative and quantitative trace analysis with extended signal temporal
logic. International Journal on Software Tools for Technology Transfer.
Springer Nature. https://doi.org/10.1007/s10009-020-00582-z'
chicago: 'Nickovic, Dejan, Olivier Lebeltel, Oded Maler, Thomas Ferrere, and Dogan
Ulus. “AMT 2.0: Qualitative and Quantitative Trace Analysis with Extended Signal
Temporal Logic.” International Journal on Software Tools for Technology Transfer.
Springer Nature, 2020. https://doi.org/10.1007/s10009-020-00582-z.'
ieee: 'D. Nickovic, O. Lebeltel, O. Maler, T. Ferrere, and D. Ulus, “AMT 2.0: Qualitative
and quantitative trace analysis with extended signal temporal logic,” International
Journal on Software Tools for Technology Transfer, vol. 22, no. 6. Springer
Nature, pp. 741–758, 2020.'
ista: 'Nickovic D, Lebeltel O, Maler O, Ferrere T, Ulus D. 2020. AMT 2.0: Qualitative
and quantitative trace analysis with extended signal temporal logic. International
Journal on Software Tools for Technology Transfer. 22(6), 741–758.'
mla: 'Nickovic, Dejan, et al. “AMT 2.0: Qualitative and Quantitative Trace Analysis
with Extended Signal Temporal Logic.” International Journal on Software Tools
for Technology Transfer, vol. 22, no. 6, Springer Nature, 2020, pp. 741–58,
doi:10.1007/s10009-020-00582-z.'
short: D. Nickovic, O. Lebeltel, O. Maler, T. Ferrere, D. Ulus, International Journal
on Software Tools for Technology Transfer 22 (2020) 741–758.
date_created: 2022-03-18T10:10:53Z
date_published: 2020-08-03T00:00:00Z
date_updated: 2024-10-09T20:58:18Z
day: '03'
department:
- _id: ToHe
doi: 10.1007/s10009-020-00582-z
external_id:
isi:
- '000555398600001'
intvolume: ' 22'
isi: 1
issue: '6'
keyword:
- Information Systems
- Software
language:
- iso: eng
month: '08'
oa_version: None
page: 741-758
publication: International Journal on Software Tools for Technology Transfer
publication_identifier:
eissn:
- 1433-2787
issn:
- 1433-2779
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
related_material:
record:
- id: '299'
relation: earlier_version
status: public
scopus_import: '1'
status: public
title: 'AMT 2.0: Qualitative and quantitative trace analysis with extended signal
temporal logic'
type: journal_article
user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1
volume: 22
year: '2020'
...
---
_id: '10672'
abstract:
- lang: eng
text: The family of feedback alignment (FA) algorithms aims to provide a more biologically
motivated alternative to backpropagation (BP), by substituting the computations
that are unrealistic to be implemented in physical brains. While FA algorithms
have been shown to work well in practice, there is a lack of rigorous theory proofing
their learning capabilities. Here we introduce the first feedback alignment algorithm
with provable learning guarantees. In contrast to existing work, we do not require
any assumption about the size or depth of the network except that it has a single
output neuron, i.e., such as for binary classification tasks. We show that our
FA algorithm can deliver its theoretical promises in practice, surpassing the
learning performance of existing FA methods and matching backpropagation in binary
classification tasks. Finally, we demonstrate the limits of our FA variant when
the number of output neurons grows beyond a certain quantity.
acknowledgement: "This research was supported in part by the Austrian Science Fund
(FWF) under grant Z211-N23\r\n(Wittgenstein Award).\r\n"
article_processing_charge: No
author:
- first_name: Mathias
full_name: Lechner, Mathias
id: 3DC22916-F248-11E8-B48F-1D18A9856A87
last_name: Lechner
citation:
ama: 'Lechner M. Learning representations for binary-classification without backpropagation.
In: 8th International Conference on Learning Representations. ICLR; 2020.'
apa: 'Lechner, M. (2020). Learning representations for binary-classification without
backpropagation. In 8th International Conference on Learning Representations.
Virtual ; Addis Ababa, Ethiopia: ICLR.'
chicago: Lechner, Mathias. “Learning Representations for Binary-Classification without
Backpropagation.” In 8th International Conference on Learning Representations.
ICLR, 2020.
ieee: M. Lechner, “Learning representations for binary-classification without backpropagation,”
in 8th International Conference on Learning Representations, Virtual ;
Addis Ababa, Ethiopia, 2020.
ista: 'Lechner M. 2020. Learning representations for binary-classification without
backpropagation. 8th International Conference on Learning Representations. ICLR:
International Conference on Learning Representations.'
mla: Lechner, Mathias. “Learning Representations for Binary-Classification without
Backpropagation.” 8th International Conference on Learning Representations,
ICLR, 2020.
short: M. Lechner, in:, 8th International Conference on Learning Representations,
ICLR, 2020.
conference:
end_date: 2020-05-01
location: Virtual ; Addis Ababa, Ethiopia
name: 'ICLR: International Conference on Learning Representations'
start_date: 2020-04-26
corr_author: '1'
date_created: 2022-01-25T15:50:00Z
date_published: 2020-03-11T00:00:00Z
date_updated: 2025-04-15T06:25:56Z
day: '11'
ddc:
- '000'
department:
- _id: GradSch
- _id: ToHe
file:
- access_level: open_access
checksum: ea13d42dd4541ddb239b6a75821fd6c9
content_type: application/pdf
creator: mlechner
date_created: 2022-01-26T07:35:17Z
date_updated: 2022-01-26T07:35:17Z
file_id: '10677'
file_name: iclr_2020.pdf
file_size: 249431
relation: main_file
success: 1
file_date_updated: 2022-01-26T07:35:17Z
has_accepted_license: '1'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://openreview.net/forum?id=Bke61krFvS
month: '03'
oa: 1
oa_version: Published Version
project:
- _id: 25F42A32-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: Z211
name: Formal methods for the design and analysis of complex systems
publication: 8th International Conference on Learning Representations
publication_status: published
publisher: ICLR
quality_controlled: '1'
scopus_import: '1'
status: public
title: Learning representations for binary-classification without backpropagation
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: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2020'
...
---
_id: '10673'
abstract:
- lang: eng
text: We propose a neural information processing system obtained by re-purposing
the function of a biological neural circuit model to govern simulated and real-world
control tasks. Inspired by the structure of the nervous system of the soil-worm,
C. elegans, we introduce ordinary neural circuits (ONCs), defined as the model
of biological neural circuits reparameterized for the control of alternative tasks.
We first demonstrate that ONCs realize networks with higher maximum flow compared
to arbitrary wired networks. We then learn instances of ONCs to control a series
of robotic tasks, including the autonomous parking of a real-world rover robot.
For reconfiguration of the purpose of the neural circuit, we adopt a search-based
optimization algorithm. Ordinary neural circuits perform on par and, in some cases,
significantly surpass the performance of contemporary deep learning models. ONC
networks are compact, 77% sparser than their counterpart neural controllers, and
their neural dynamics are fully interpretable at the cell-level.
acknowledgement: "RH and RG are partially supported by Horizon-2020 ECSEL Project
grant No. 783163 (iDev40), Productive 4.0, and ATBMBFW CPS-IoT Ecosystem. ML was
supported in part by the Austrian Science Fund (FWF) under grant Z211-N23\r\n(Wittgenstein
Award). AA is supported by the National Science Foundation (NSF) Graduate Research
Fellowship\r\nProgram. RH and DR are partially supported by The Boeing Company and
JP Morgan Chase. This research work is\r\npartially drawn from the PhD dissertation
of RH.\r\n"
alternative_title:
- PMLR
article_processing_charge: No
author:
- first_name: Ramin
full_name: Hasani, Ramin
last_name: Hasani
- first_name: Mathias
full_name: Lechner, Mathias
id: 3DC22916-F248-11E8-B48F-1D18A9856A87
last_name: Lechner
- first_name: Alexander
full_name: Amini, Alexander
last_name: Amini
- first_name: Daniela
full_name: Rus, Daniela
last_name: Rus
- first_name: Radu
full_name: Grosu, Radu
last_name: Grosu
citation:
ama: 'Hasani R, Lechner M, Amini A, Rus D, Grosu R. A natural lottery ticket winner:
Reinforcement learning with ordinary neural circuits. In: Proceedings of the
37th International Conference on Machine Learning. PMLR. ; 2020:4082-4093.'
apa: 'Hasani, R., Lechner, M., Amini, A., Rus, D., & Grosu, R. (2020). A natural
lottery ticket winner: Reinforcement learning with ordinary neural circuits. In
Proceedings of the 37th International Conference on Machine Learning (pp.
4082–4093). Virtual.'
chicago: 'Hasani, Ramin, Mathias Lechner, Alexander Amini, Daniela Rus, and Radu
Grosu. “A Natural Lottery Ticket Winner: Reinforcement Learning with Ordinary
Neural Circuits.” In Proceedings of the 37th International Conference on Machine
Learning, 4082–93. PMLR, 2020.'
ieee: 'R. Hasani, M. Lechner, A. Amini, D. Rus, and R. Grosu, “A natural lottery
ticket winner: Reinforcement learning with ordinary neural circuits,” in Proceedings
of the 37th International Conference on Machine Learning, Virtual, 2020, pp.
4082–4093.'
ista: 'Hasani R, Lechner M, Amini A, Rus D, Grosu R. 2020. A natural lottery ticket
winner: Reinforcement learning with ordinary neural circuits. Proceedings of the
37th International Conference on Machine Learning. ML: Machine LearningPMLR, PMLR,
, 4082–4093.'
mla: 'Hasani, Ramin, et al. “A Natural Lottery Ticket Winner: Reinforcement Learning
with Ordinary Neural Circuits.” Proceedings of the 37th International Conference
on Machine Learning, 2020, pp. 4082–93.'
short: R. Hasani, M. Lechner, A. Amini, D. Rus, R. Grosu, in:, Proceedings of the
37th International Conference on Machine Learning, 2020, pp. 4082–4093.
conference:
end_date: 2020-07-18
location: Virtual
name: 'ML: Machine Learning'
start_date: 2020-07-12
date_created: 2022-01-25T15:50:34Z
date_published: 2020-01-01T00:00:00Z
date_updated: 2025-04-15T06:25:56Z
ddc:
- '000'
department:
- _id: GradSch
- _id: ToHe
file:
- access_level: open_access
checksum: c9a4a29161777fc1a89ef451c040e3b1
content_type: application/pdf
creator: cchlebak
date_created: 2022-01-26T11:08:51Z
date_updated: 2022-01-26T11:08:51Z
file_id: '10691'
file_name: 2020_PMLR_Hasani.pdf
file_size: 2329798
relation: main_file
success: 1
file_date_updated: 2022-01-26T11:08:51Z
has_accepted_license: '1'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: http://proceedings.mlr.press/v119/hasani20a.html
oa: 1
oa_version: Published Version
page: 4082-4093
project:
- _id: 25F42A32-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: Z211
name: Formal methods for the design and analysis of complex systems
publication: Proceedings of the 37th International Conference on Machine Learning
publication_identifier:
issn:
- 2640-3498
publication_status: published
quality_controlled: '1'
scopus_import: '1'
series_title: PMLR
status: public
title: 'A natural lottery ticket winner: Reinforcement learning with ordinary neural
circuits'
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: '2020'
...
---
_id: '7808'
abstract:
- lang: eng
text: Quantization converts neural networks into low-bit fixed-point computations
which can be carried out by efficient integer-only hardware, and is standard practice
for the deployment of neural networks on real-time embedded devices. However,
like their real-numbered counterpart, quantized networks are not immune to malicious
misclassification caused by adversarial attacks. We investigate how quantization
affects a network’s robustness to adversarial attacks, which is a formal verification
question. We show that neither robustness nor non-robustness are monotonic with
changing the number of bits for the representation and, also, neither are preserved
by quantization from a real-numbered network. For this reason, we introduce a
verification method for quantized neural networks which, using SMT solving over
bit-vectors, accounts for their exact, bit-precise semantics. We built a tool
and analyzed the effect of quantization on a classifier for the MNIST dataset.
We demonstrate that, compared to our method, existing methods for the analysis
of real-numbered networks often derive false conclusions about their quantizations,
both when determining robustness and when detecting attacks, and that existing
methods for quantized networks often miss attacks. Furthermore, we applied our
method beyond robustness, showing how the number of bits in quantization enlarges
the gender bias of a predictor for students’ grades.
alternative_title:
- LNCS
article_processing_charge: No
author:
- first_name: Mirco
full_name: Giacobbe, Mirco
id: 3444EA5E-F248-11E8-B48F-1D18A9856A87
last_name: Giacobbe
orcid: 0000-0001-8180-0904
- 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
citation:
ama: 'Giacobbe M, Henzinger TA, Lechner M. How many bits does it take to quantize
your neural network? In: International Conference on Tools and Algorithms for
the Construction and Analysis of Systems. Vol 12079. Springer Nature; 2020:79-97.
doi:10.1007/978-3-030-45237-7_5'
apa: 'Giacobbe, M., Henzinger, T. A., & Lechner, M. (2020). How many bits does
it take to quantize your neural network? In International Conference on Tools
and Algorithms for the Construction and Analysis of Systems (Vol. 12079, pp.
79–97). Dublin, Ireland: Springer Nature. https://doi.org/10.1007/978-3-030-45237-7_5'
chicago: Giacobbe, Mirco, Thomas A Henzinger, and Mathias Lechner. “How Many Bits
Does It Take to Quantize Your Neural Network?” In International Conference
on Tools and Algorithms for the Construction and Analysis of Systems, 12079:79–97.
Springer Nature, 2020. https://doi.org/10.1007/978-3-030-45237-7_5.
ieee: M. Giacobbe, T. A. Henzinger, and M. Lechner, “How many bits does it take
to quantize your neural network?,” in International Conference on Tools and
Algorithms for the Construction and Analysis of Systems, Dublin, Ireland,
2020, vol. 12079, pp. 79–97.
ista: 'Giacobbe M, Henzinger TA, Lechner M. 2020. How many bits does it take to
quantize your neural network? International Conference on Tools and Algorithms
for the Construction and Analysis of Systems. TACAS: Tools and Algorithms for
the Construction and Analysis of Systems, LNCS, vol. 12079, 79–97.'
mla: Giacobbe, Mirco, et al. “How Many Bits Does It Take to Quantize Your Neural
Network?” International Conference on Tools and Algorithms for the Construction
and Analysis of Systems, vol. 12079, Springer Nature, 2020, pp. 79–97, doi:10.1007/978-3-030-45237-7_5.
short: M. Giacobbe, T.A. Henzinger, M. Lechner, in:, International Conference on
Tools and Algorithms for the Construction and Analysis of Systems, Springer Nature,
2020, pp. 79–97.
conference:
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corr_author: '1'
date_created: 2020-05-10T22:00:49Z
date_published: 2020-04-17T00:00:00Z
date_updated: 2026-04-16T09:46:07Z
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title: How many bits does it take to quantize your neural network?
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