---
_id: '17378'
abstract:
- lang: eng
text: Generative Pre-trained Transformer models, known as GPT or OPT, set themselves
apart through breakthrough performance across complex language modelling tasks,
but also by their extremely high computational and storage costs. Specifically,
due to their massive size, even inference for large, highly-accurate GPT models
may require multiple performant GPUs, which limits the usability of such models.
While there is emerging work on relieving this pressure via model compression,
the applicability and performance of existing compression techniques is limited
by the scale and complexity of GPT models. In this paper, we address this challenge,
and propose OPTQ, a new one-shot weight quantization method based on approximate
second-order information, that is both highly-accurate and highly-efficient. Specifically,
OPTQ can quantize GPT models with 175 billion parameters in approximately four
GPU hours, reducing the bitwidth down to 3 or 4 bits per weight, with negligible
accuracy degradation relative to the uncompressed baseline. Our method more than
doubles the compression gains relative to previously-proposed one-shot quantization
methods, preserving accuracy, allowing us for the first time to execute an 175
billion-parameter model inside a single GPU for generative inference. Moreover,
we also show that our method can still provide reasonable accuracy in the extreme
quantization regime, in which weights are quantized to 2-bit or even ternary quantization
levels. We show experimentally that these improvements can be leveraged for end-to-end
inference speedups over FP16, of around 3.25x when using high-end GPUs (NVIDIA
A100) and 4.5x when using more cost-effective ones (NVIDIA A6000). The implementation
is available at https://github.com/IST-DASLab/gptq.
acknowledged_ssus:
- _id: ScienComp
acknowledgement: Elias Frantar and Dan Alistarh gratefully acknowledge funding from
the European Research Council (ERC) under the European Union’s Horizon 2020 programme
(grant agreement No. 805223 ScaleML), as well as experimental support from Eldar
Kurtic, and from the IST Austria IT department, in particular Stefano Elefante,
Andrei Hornoiu, and Alois Schloegl. The work of Saleh Ashkboos and Torsten Hoefler
was supported by the PASC DaCeMI project, received EuroHPC-JU funding under grant
MAELSTROM, No. 955513. We thank the Swiss National Supercomputing Center (CSCS)
for supporting us with compute infrastructure.
article_processing_charge: No
author:
- first_name: Elias
full_name: Frantar, Elias
id: 09a8f98d-ec99-11ea-ae11-c063a7b7fe5f
last_name: Frantar
- first_name: Saleh
full_name: Ashkboos, Saleh
last_name: Ashkboos
- first_name: Torsten
full_name: Hoefler, Torsten
last_name: Hoefler
- first_name: Dan-Adrian
full_name: Alistarh, Dan-Adrian
id: 4A899BFC-F248-11E8-B48F-1D18A9856A87
last_name: Alistarh
orcid: 0000-0003-3650-940X
citation:
ama: 'Frantar E, Ashkboos S, Hoefler T, Alistarh D-A. OPTQ: Accurate post-training
quantization for generative pre-trained transformers. In: 11th International
Conference on Learning Representations . International Conference on Learning
Representations; 2023.'
apa: 'Frantar, E., Ashkboos, S., Hoefler, T., & Alistarh, D.-A. (2023). OPTQ:
Accurate post-training quantization for generative pre-trained transformers. In
11th International Conference on Learning Representations . Kigali, Rwanda:
International Conference on Learning Representations.'
chicago: 'Frantar, Elias, Saleh Ashkboos, Torsten Hoefler, and Dan-Adrian Alistarh.
“OPTQ: Accurate Post-Training Quantization for Generative Pre-Trained Transformers.”
In 11th International Conference on Learning Representations . International
Conference on Learning Representations, 2023.'
ieee: 'E. Frantar, S. Ashkboos, T. Hoefler, and D.-A. Alistarh, “OPTQ: Accurate
post-training quantization for generative pre-trained transformers,” in 11th
International Conference on Learning Representations , Kigali, Rwanda, 2023.'
ista: 'Frantar E, Ashkboos S, Hoefler T, Alistarh D-A. 2023. OPTQ: Accurate post-training
quantization for generative pre-trained transformers. 11th International Conference
on Learning Representations . ICLR: International Conference on Learning Representations.'
mla: 'Frantar, Elias, et al. “OPTQ: Accurate Post-Training Quantization for Generative
Pre-Trained Transformers.” 11th International Conference on Learning Representations
, International Conference on Learning Representations, 2023.'
short: E. Frantar, S. Ashkboos, T. Hoefler, D.-A. Alistarh, in:, 11th International
Conference on Learning Representations , International Conference on Learning
Representations, 2023.
conference:
end_date: 2023-05-05
location: Kigali, Rwanda
name: 'ICLR: International Conference on Learning Representations'
start_date: 2023-05-01
corr_author: '1'
date_created: 2024-08-04T22:01:22Z
date_published: 2023-05-01T00:00:00Z
date_updated: 2026-07-29T13:48:39Z
day: '01'
ddc:
- '000'
department:
- _id: DaAl
ec_funded: 1
file:
- access_level: open_access
checksum: aacbf11dbd8b02a3e0bfd942a33e0593
content_type: application/pdf
creator: dernst
date_created: 2024-08-05T07:52:44Z
date_updated: 2024-08-05T07:52:44Z
file_id: '17385'
file_name: 2023_ICLR_Frantar.pdf
file_size: 437492
relation: main_file
success: 1
file_date_updated: 2024-08-05T07:52:44Z
has_accepted_license: '1'
language:
- iso: eng
month: '05'
oa: 1
oa_version: Published Version
project:
- _id: 268A44D6-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '805223'
name: Elastic Coordination for Scalable Machine Learning
publication: '11th International Conference on Learning Representations '
publication_status: published
publisher: International Conference on Learning Representations
quality_controlled: '1'
related_material:
link:
- relation: software
url: https://github.com/IST-DASLab/gptq
record:
- id: '17485'
relation: dissertation_contains
status: public
scopus_import: '1'
status: public
title: 'OPTQ: Accurate post-training quantization for generative pre-trained transformers'
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2023'
...
---
_id: '14458'
abstract:
- lang: eng
text: 'We show for the first time that large-scale generative pretrained transformer
(GPT) family models can be pruned to at least 50% sparsity in one-shot, without
any retraining, at minimal loss of accuracy. This is achieved via a new pruning
method called SparseGPT, specifically designed to work efficiently and accurately
on massive GPT-family models. We can execute SparseGPT on the largest available
open-source models, OPT-175B and BLOOM-176B, in under 4.5 hours, and can reach
60% unstructured sparsity with negligible increase in perplexity: remarkably,
more than 100 billion weights from these models can be ignored at inference time.
SparseGPT generalizes to semi-structured (2:4 and 4:8) patterns, and is compatible
with weight quantization approaches. The code is available at: https://github.com/IST-DASLab/sparsegpt.'
acknowledged_ssus:
- _id: ScienComp
acknowledgement: The authors gratefully acknowledge funding from the European Research
Council (ERC) under the European Union’s Horizon 2020 programme (grant agreement
No. 805223 ScaleML), as well as experimental support from Eldar Kurtic, and from
the IST Austria IT department, in particular Stefano Elefante, Andrei Hornoiu, and
Alois Schloegl.
alternative_title:
- PMLR
article_processing_charge: No
arxiv: 1
author:
- first_name: Elias
full_name: Frantar, Elias
id: 09a8f98d-ec99-11ea-ae11-c063a7b7fe5f
last_name: Frantar
- first_name: Dan-Adrian
full_name: Alistarh, Dan-Adrian
id: 4A899BFC-F248-11E8-B48F-1D18A9856A87
last_name: Alistarh
orcid: 0000-0003-3650-940X
citation:
ama: 'Frantar E, Alistarh D-A. SparseGPT: Massive language models can be accurately
pruned in one-shot. In: Proceedings of the 40th International Conference on
Machine Learning. Vol 202. ML Research Press; 2023:10323-10337.'
apa: 'Frantar, E., & Alistarh, D.-A. (2023). SparseGPT: Massive language models
can be accurately pruned in one-shot. In Proceedings of the 40th International
Conference on Machine Learning (Vol. 202, pp. 10323–10337). Honolulu, Hawaii,
HI, United States: ML Research Press.'
chicago: 'Frantar, Elias, and Dan-Adrian Alistarh. “SparseGPT: Massive Language
Models Can Be Accurately Pruned in One-Shot.” In Proceedings of the 40th International
Conference on Machine Learning, 202:10323–37. ML Research Press, 2023.'
ieee: 'E. Frantar and D.-A. Alistarh, “SparseGPT: Massive language models can be
accurately pruned in one-shot,” in Proceedings of the 40th International Conference
on Machine Learning, Honolulu, Hawaii, HI, United States, 2023, vol. 202,
pp. 10323–10337.'
ista: 'Frantar E, Alistarh D-A. 2023. SparseGPT: Massive language models can be
accurately pruned in one-shot. Proceedings of the 40th International Conference
on Machine Learning. ICML: International Conference on Machine Learning, PMLR,
vol. 202, 10323–10337.'
mla: 'Frantar, Elias, and Dan-Adrian Alistarh. “SparseGPT: Massive Language Models
Can Be Accurately Pruned in One-Shot.” Proceedings of the 40th International
Conference on Machine Learning, vol. 202, ML Research Press, 2023, pp. 10323–37.'
short: E. Frantar, D.-A. Alistarh, in:, Proceedings of the 40th International Conference
on Machine Learning, ML Research Press, 2023, pp. 10323–10337.
conference:
end_date: 2023-07-29
location: Honolulu, Hawaii, HI, United States
name: 'ICML: International Conference on Machine Learning'
start_date: 2023-07-23
corr_author: '1'
date_created: 2023-10-29T23:01:16Z
date_published: 2023-07-30T00:00:00Z
date_updated: 2026-07-29T13:48:39Z
day: '30'
department:
- _id: DaAl
ec_funded: 1
external_id:
arxiv:
- '2301.00774'
intvolume: ' 202'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2301.00774
month: '07'
oa: 1
oa_version: Preprint
page: 10323-10337
project:
- _id: 268A44D6-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '805223'
name: Elastic Coordination for Scalable Machine Learning
publication: Proceedings of the 40th International Conference on Machine Learning
publication_identifier:
eissn:
- 2640-3498
publication_status: published
publisher: ML Research Press
quality_controlled: '1'
related_material:
record:
- id: '17485'
relation: dissertation_contains
status: public
scopus_import: '1'
status: public
title: 'SparseGPT: Massive language models can be accurately pruned in one-shot'
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 202
year: '2023'
...
---
_id: '14619'
abstract:
- lang: eng
text: Data underlying the publication "A streamlined molecular-dynamics workflow
for computing solubilities of molecular and ionic crystals" (DOI https://doi.org/10.1063/5.0173341).
article_processing_charge: No
author:
- first_name: Bingqing
full_name: Cheng, Bingqing
id: cbe3cda4-d82c-11eb-8dc7-8ff94289fcc9
last_name: Cheng
orcid: 0000-0002-3584-9632
citation:
ama: 'Cheng B. BingqingCheng/solubility: V1.0. 2023. doi:10.5281/ZENODO.8398094'
apa: 'Cheng, B. (2023). BingqingCheng/solubility: V1.0. Zenodo. https://doi.org/10.5281/ZENODO.8398094'
chicago: 'Cheng, Bingqing. “BingqingCheng/Solubility: V1.0.” Zenodo, 2023. https://doi.org/10.5281/ZENODO.8398094.'
ieee: 'B. Cheng, “BingqingCheng/solubility: V1.0.” Zenodo, 2023.'
ista: 'Cheng B. 2023. BingqingCheng/solubility: V1.0, Zenodo, 10.5281/ZENODO.8398094.'
mla: 'Cheng, Bingqing. BingqingCheng/Solubility: V1.0. Zenodo, 2023, doi:10.5281/ZENODO.8398094.'
short: B. Cheng, (2023).
corr_author: '1'
date_created: 2023-11-28T08:32:18Z
date_published: 2023-10-02T00:00:00Z
date_updated: 2026-08-07T11:07:45Z
day: '02'
ddc:
- '530'
department:
- _id: BiCh
doi: 10.5281/ZENODO.8398094
has_accepted_license: '1'
main_file_link:
- open_access: '1'
url: https://doi.org/10.5281/zenodo.8398094
month: '10'
oa: 1
oa_version: Published Version
publisher: Zenodo
related_material:
record:
- id: '14603'
relation: used_in_publication
status: public
status: public
title: 'BingqingCheng/solubility: V1.0'
type: research_data_reference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2023'
...
---
_id: '12079'
abstract:
- lang: eng
text: We extend the recent rigorous convergence result of Abels and Moser (SIAM
J Math Anal 54(1):114–172, 2022. https://doi.org/10.1137/21M1424925) concerning
convergence rates for solutions of the Allen–Cahn equation with a nonlinear Robin
boundary condition towards evolution by mean curvature flow with constant contact
angle. More precisely, in the present work we manage to remove the perturbative
assumption on the contact angle being close to 90∘. We establish under usual double-well
type assumptions on the potential and for a certain class of boundary energy densities
the sub-optimal convergence rate of order ε12 for general contact angles α∈(0,π).
For a very specific form of the boundary energy density, we even obtain from our
methods a sharp convergence rate of order ε; again for general contact angles
α∈(0,π). Our proof deviates from the popular strategy based on rigorous asymptotic
expansions and stability estimates for the linearized Allen–Cahn operator. Instead,
we follow the recent approach by Fischer et al. (SIAM J Math Anal 52(6):6222–6233,
2020. https://doi.org/10.1137/20M1322182), thus relying on a relative entropy
technique. We develop a careful adaptation of their approach in order to encode
the constant contact angle condition. In fact, we perform this task at the level
of the notion of gradient flow calibrations. This concept was recently introduced
in the context of weak-strong uniqueness for multiphase mean curvature flow by
Fischer et al. (arXiv:2003.05478v2).
acknowledgement: "This Project has received funding from the European Research Council
(ERC) under the European Union’s Horizon 2020 research and innovation programme
(Grant Agreement No 948819) , and from the Deutsche Forschungsgemeinschaft (DFG,
German Research Foundation) under Germany’s Excellence Strategy—EXC-2047/1 - 390685813.\r\nOpen
Access funding enabled and organized by Projekt DEAL."
article_number: '201'
article_processing_charge: No
article_type: original
author:
- first_name: Sebastian
full_name: Hensel, Sebastian
id: 4D23B7DA-F248-11E8-B48F-1D18A9856A87
last_name: Hensel
orcid: 0000-0001-7252-8072
- first_name: Maximilian
full_name: Moser, Maximilian
id: a60047a9-da77-11eb-85b4-c4dc385ebb8c
last_name: Moser
citation:
ama: 'Hensel S, Moser M. Convergence rates for the Allen–Cahn equation with boundary
contact energy: The non-perturbative regime. Calculus of Variations and Partial
Differential Equations. 2022;61(6). doi:10.1007/s00526-022-02307-3'
apa: 'Hensel, S., & Moser, M. (2022). Convergence rates for the Allen–Cahn equation
with boundary contact energy: The non-perturbative regime. Calculus of Variations
and Partial Differential Equations. Springer Nature. https://doi.org/10.1007/s00526-022-02307-3'
chicago: 'Hensel, Sebastian, and Maximilian Moser. “Convergence Rates for the Allen–Cahn
Equation with Boundary Contact Energy: The Non-Perturbative Regime.” Calculus
of Variations and Partial Differential Equations. Springer Nature, 2022. https://doi.org/10.1007/s00526-022-02307-3.'
ieee: 'S. Hensel and M. Moser, “Convergence rates for the Allen–Cahn equation with
boundary contact energy: The non-perturbative regime,” Calculus of Variations
and Partial Differential Equations, vol. 61, no. 6. Springer Nature, 2022.'
ista: 'Hensel S, Moser M. 2022. Convergence rates for the Allen–Cahn equation with
boundary contact energy: The non-perturbative regime. Calculus of Variations and
Partial Differential Equations. 61(6), 201.'
mla: 'Hensel, Sebastian, and Maximilian Moser. “Convergence Rates for the Allen–Cahn
Equation with Boundary Contact Energy: The Non-Perturbative Regime.” Calculus
of Variations and Partial Differential Equations, vol. 61, no. 6, 201, Springer
Nature, 2022, doi:10.1007/s00526-022-02307-3.'
short: S. Hensel, M. Moser, Calculus of Variations and Partial Differential Equations
61 (2022).
date_created: 2022-09-11T22:01:54Z
date_published: 2022-08-24T00:00:00Z
date_updated: 2025-04-14T07:53:59Z
day: '24'
ddc:
- '510'
department:
- _id: JuFi
doi: 10.1007/s00526-022-02307-3
ec_funded: 1
external_id:
isi:
- '000844247300008'
file:
- access_level: open_access
checksum: b2da020ce50440080feedabeab5b09c4
content_type: application/pdf
creator: dernst
date_created: 2023-01-20T08:56:01Z
date_updated: 2023-01-20T08:56:01Z
file_id: '12320'
file_name: 2022_Calculus_Hensel.pdf
file_size: 1278493
relation: main_file
success: 1
file_date_updated: 2023-01-20T08:56:01Z
has_accepted_license: '1'
intvolume: ' 61'
isi: 1
issue: '6'
language:
- iso: eng
month: '08'
oa: 1
oa_version: Published Version
project:
- _id: 0aa76401-070f-11eb-9043-b5bb049fa26d
call_identifier: H2020
grant_number: '948819'
name: Bridging Scales in Random Materials
publication: Calculus of Variations and Partial Differential Equations
publication_identifier:
eissn:
- 1432-0835
issn:
- 0944-2669
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
scopus_import: '1'
status: public
title: 'Convergence rates for the Allen–Cahn equation with boundary contact energy:
The non-perturbative regime'
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: journal_article
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
volume: 61
year: '2022'
...
---
_id: '12179'
abstract:
- lang: eng
text: We derive an accurate lower tail estimate on the lowest singular value σ1(X−z)
of a real Gaussian (Ginibre) random matrix X shifted by a complex parameter z.
Such shift effectively changes the upper tail behavior of the condition number
κ(X−z) from the slower (κ(X−z)≥t)≲1/t decay typical for real Ginibre matrices
to the faster 1/t2 decay seen for complex Ginibre matrices as long as z is away
from the real axis. This sharpens and resolves a recent conjecture in [J. Banks
et al., https://arxiv.org/abs/2005.08930, 2020] on the regularizing effect of
the real Ginibre ensemble with a genuinely complex shift. As a consequence we
obtain an improved upper bound on the eigenvalue condition numbers (known also
as the eigenvector overlaps) for real Ginibre matrices. The main technical tool
is a rigorous supersymmetric analysis from our earlier work [Probab. Math. Phys.,
1 (2020), pp. 101--146].
article_processing_charge: No
article_type: original
arxiv: 1
author:
- first_name: Giorgio
full_name: Cipolloni, Giorgio
id: 42198EFA-F248-11E8-B48F-1D18A9856A87
last_name: Cipolloni
orcid: 0000-0002-4901-7992
- first_name: László
full_name: Erdös, László
id: 4DBD5372-F248-11E8-B48F-1D18A9856A87
last_name: Erdös
orcid: 0000-0001-5366-9603
- first_name: Dominik J
full_name: Schröder, Dominik J
id: 408ED176-F248-11E8-B48F-1D18A9856A87
last_name: Schröder
orcid: 0000-0002-2904-1856
citation:
ama: Cipolloni G, Erdös L, Schröder DJ. On the condition number of the shifted real
Ginibre ensemble. SIAM Journal on Matrix Analysis and Applications. 2022;43(3):1469-1487.
doi:10.1137/21m1424408
apa: Cipolloni, G., Erdös, L., & Schröder, D. J. (2022). On the condition number
of the shifted real Ginibre ensemble. SIAM Journal on Matrix Analysis and Applications.
Society for Industrial and Applied Mathematics. https://doi.org/10.1137/21m1424408
chicago: Cipolloni, Giorgio, László Erdös, and Dominik J Schröder. “On the Condition
Number of the Shifted Real Ginibre Ensemble.” SIAM Journal on Matrix Analysis
and Applications. Society for Industrial and Applied Mathematics, 2022. https://doi.org/10.1137/21m1424408.
ieee: G. Cipolloni, L. Erdös, and D. J. Schröder, “On the condition number of the
shifted real Ginibre ensemble,” SIAM Journal on Matrix Analysis and Applications,
vol. 43, no. 3. Society for Industrial and Applied Mathematics, pp. 1469–1487,
2022.
ista: Cipolloni G, Erdös L, Schröder DJ. 2022. On the condition number of the shifted
real Ginibre ensemble. SIAM Journal on Matrix Analysis and Applications. 43(3),
1469–1487.
mla: Cipolloni, Giorgio, et al. “On the Condition Number of the Shifted Real Ginibre
Ensemble.” SIAM Journal on Matrix Analysis and Applications, vol. 43, no.
3, Society for Industrial and Applied Mathematics, 2022, pp. 1469–87, doi:10.1137/21m1424408.
short: G. Cipolloni, L. Erdös, D.J. Schröder, SIAM Journal on Matrix Analysis and
Applications 43 (2022) 1469–1487.
corr_author: '1'
date_created: 2023-01-12T12:12:38Z
date_published: 2022-07-01T00:00:00Z
date_updated: 2025-09-10T09:51:27Z
day: '01'
department:
- _id: LaEr
doi: 10.1137/21m1424408
external_id:
arxiv:
- '2105.13719'
isi:
- '001125796400002'
intvolume: ' 43'
isi: 1
issue: '3'
keyword:
- Analysis
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2105.13719
month: '07'
oa: 1
oa_version: Preprint
page: 1469-1487
publication: SIAM Journal on Matrix Analysis and Applications
publication_identifier:
eissn:
- 1095-7162
issn:
- 0895-4798
publication_status: published
publisher: Society for Industrial and Applied Mathematics
quality_controlled: '1'
scopus_import: '1'
status: public
title: On the condition number of the shifted real Ginibre ensemble
type: journal_article
user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345
volume: 43
year: '2022'
...
---
_id: '12302'
abstract:
- lang: eng
text: 'We propose a novel algorithm to decide the language inclusion between (nondeterministic)
Büchi automata, a PSPACE-complete problem. Our approach, like others before, leverage
a notion of quasiorder to prune the search for a counterexample by discarding
candidates which are subsumed by others for the quasiorder. Discarded candidates
are guaranteed to not compromise the completeness of the algorithm. The novelty
of our work lies in the quasiorder used to discard candidates. We introduce FORQs
(family of right quasiorders) that we obtain by adapting the notion of family
of right congruences put forward by Maler and Staiger in 1993. We define a FORQ-based
inclusion algorithm which we prove correct and instantiate it for a specific FORQ,
called the structural FORQ, induced by the Büchi automaton to the right of the
inclusion sign. The resulting implementation, called FORKLIFT, scales up better
than the state-of-the-art on a variety of benchmarks including benchmarks from
program verification and theorem proving for word combinatorics. Artifact: https://doi.org/10.5281/zenodo.6552870'
acknowledgement: This work was partially funded by the ESF Investing in your future,
the Madrid regional project S2018/TCS-4339 BLOQUES, the Spanish project PGC2018-102210-B-I00
BOSCO, the Ramón y Cajal fellowship RYC-2016-20281, and the ERC grant PR1001ERC02.
alternative_title:
- LNCS
article_processing_charge: No
arxiv: 1
author:
- first_name: Kyveli
full_name: Doveri, Kyveli
last_name: Doveri
- first_name: Pierre
full_name: Ganty, Pierre
last_name: Ganty
- first_name: Nicolas Adrien
full_name: Mazzocchi, Nicolas Adrien
id: b26baa86-3308-11ec-87b0-8990f34baa85
last_name: Mazzocchi
citation:
ama: 'Doveri K, Ganty P, Mazzocchi NA. FORQ-based language inclusion formal testing.
In: Computer Aided Verification. Vol 13372. Springer Nature; 2022:109-129.
doi:10.1007/978-3-031-13188-2_6'
apa: 'Doveri, K., Ganty, P., & Mazzocchi, N. A. (2022). FORQ-based language
inclusion formal testing. In Computer Aided Verification (Vol. 13372, pp.
109–129). Haifa, Israel: Springer Nature. https://doi.org/10.1007/978-3-031-13188-2_6'
chicago: Doveri, Kyveli, Pierre Ganty, and Nicolas Adrien Mazzocchi. “FORQ-Based
Language Inclusion Formal Testing.” In Computer Aided Verification, 13372:109–29.
Springer Nature, 2022. https://doi.org/10.1007/978-3-031-13188-2_6.
ieee: K. Doveri, P. Ganty, and N. A. Mazzocchi, “FORQ-based language inclusion formal
testing,” in Computer Aided Verification, Haifa, Israel, 2022, vol. 13372,
pp. 109–129.
ista: 'Doveri K, Ganty P, Mazzocchi NA. 2022. FORQ-based language inclusion formal
testing. Computer Aided Verification. CAV: Computer Aided Verification, LNCS,
vol. 13372, 109–129.'
mla: Doveri, Kyveli, et al. “FORQ-Based Language Inclusion Formal Testing.” Computer
Aided Verification, vol. 13372, Springer Nature, 2022, pp. 109–29, doi:10.1007/978-3-031-13188-2_6.
short: K. Doveri, P. Ganty, N.A. Mazzocchi, in:, Computer Aided Verification, Springer
Nature, 2022, pp. 109–129.
conference:
end_date: 2022-08-10
location: Haifa, Israel
name: 'CAV: Computer Aided Verification'
start_date: 2022-08-07
date_created: 2023-01-16T10:06:31Z
date_published: 2022-08-06T00:00:00Z
date_updated: 2025-04-14T07:55:56Z
day: '06'
ddc:
- '000'
department:
- _id: ToHe
doi: 10.1007/978-3-031-13188-2_6
ec_funded: 1
external_id:
arxiv:
- '2207.13549'
isi:
- '000870310500006'
file:
- access_level: open_access
checksum: edc363b1be5447a09063e115c247918a
content_type: application/pdf
creator: dernst
date_created: 2023-01-30T12:51:02Z
date_updated: 2023-01-30T12:51:02Z
file_id: '12465'
file_name: 2022_LNCS_Doveri.pdf
file_size: 497682
relation: main_file
success: 1
file_date_updated: 2023-01-30T12:51:02Z
has_accepted_license: '1'
intvolume: ' 13372'
isi: 1
language:
- iso: eng
month: '08'
oa: 1
oa_version: Published Version
page: 109-129
project:
- _id: 62781420-2b32-11ec-9570-8d9b63373d4d
call_identifier: H2020
grant_number: '101020093'
name: Vigilant Algorithmic Monitoring of Software
publication: Computer Aided Verification
publication_identifier:
eisbn:
- '9783031131882'
eissn:
- 1611-3349
isbn:
- '9783031131875'
issn:
- 0302-9743
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
scopus_import: '1'
status: public
title: FORQ-based language inclusion formal testing
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: conference
user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1
volume: 13372
year: '2022'
...
---
_id: '12495'
abstract:
- lang: eng
text: "Fairness-aware learning aims at constructing classifiers that not only make
accurate predictions, but also do not discriminate against specific groups. It
is a fast-growing area of\r\nmachine learning with far-reaching societal impact.
However, existing fair learning methods\r\nare vulnerable to accidental or malicious
artifacts in the training data, which can cause\r\nthem to unknowingly produce
unfair classifiers. In this work we address the problem of\r\nfair learning from
unreliable training data in the robust multisource setting, where the\r\navailable
training data comes from multiple sources, a fraction of which might not be representative
of the true data distribution. We introduce FLEA, a filtering-based algorithm\r\nthat
identifies and suppresses those data sources that would have a negative impact
on\r\nfairness or accuracy if they were used for training. As such, FLEA is not
a replacement of\r\nprior fairness-aware learning methods but rather an augmentation
that makes any of them\r\nrobust against unreliable training data. We show the
effectiveness of our approach by a\r\ndiverse range of experiments on multiple
datasets. Additionally, we prove formally that\r\n–given enough data– FLEA protects
the learner against corruptions as long as the fraction of\r\naffected data sources
is less than half. Our source code and documentation are available at\r\nhttps://github.com/ISTAustria-CVML/FLEA."
acknowledged_ssus:
- _id: ScienComp
acknowledgement: 'The authors would like to thank Bernd Prach, Elias Frantar, Alexandra
Peste, Mahdi Nikdan, and Peter Súkeník for their helpful feedback. This research
was supported by the Scientific Service Units (SSU) of IST Austria through resources
provided by Scientific Computing (SciComp). This publication was made possible by
an ETH AI Center postdoctoral fellowship granted to Nikola Konstantinov. Eugenia
Iofinova was supported in part by the FWF DK VGSCO, grant agreement number W1260-N35. '
article_processing_charge: No
article_type: original
arxiv: 1
author:
- first_name: Eugenia B
full_name: Iofinova, Eugenia B
id: f9a17499-f6e0-11ea-865d-fdf9a3f77117
last_name: Iofinova
orcid: 0000-0002-7778-3221
- first_name: Nikola H
full_name: Konstantinov, Nikola H
id: 4B9D76E4-F248-11E8-B48F-1D18A9856A87
last_name: Konstantinov
orcid: 0009-0009-5204-7621
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
citation:
ama: 'Iofinova EB, Konstantinov NH, Lampert C. FLEA: Provably robust fair multisource
learning from unreliable training data. Transactions on Machine Learning Research.
2022.'
apa: 'Iofinova, E. B., Konstantinov, N. H., & Lampert, C. (2022). FLEA: Provably
robust fair multisource learning from unreliable training data. Transactions
on Machine Learning Research. ML Research Press.'
chicago: 'Iofinova, Eugenia B, Nikola H Konstantinov, and Christoph Lampert. “FLEA:
Provably Robust Fair Multisource Learning from Unreliable Training Data.” Transactions
on Machine Learning Research. ML Research Press, 2022.'
ieee: 'E. B. Iofinova, N. H. Konstantinov, and C. Lampert, “FLEA: Provably robust
fair multisource learning from unreliable training data,” Transactions on Machine
Learning Research. ML Research Press, 2022.'
ista: 'Iofinova EB, Konstantinov NH, Lampert C. 2022. FLEA: Provably robust fair
multisource learning from unreliable training data. Transactions on Machine Learning
Research.'
mla: 'Iofinova, Eugenia B., et al. “FLEA: Provably Robust Fair Multisource Learning
from Unreliable Training Data.” Transactions on Machine Learning Research,
ML Research Press, 2022.'
short: E.B. Iofinova, N.H. Konstantinov, C. Lampert, Transactions on Machine Learning
Research (2022).
corr_author: '1'
date_created: 2023-02-02T20:29:57Z
date_published: 2022-12-22T00:00:00Z
date_updated: 2025-12-30T11:04:31Z
day: '22'
ddc:
- '000'
department:
- _id: ChLa
external_id:
arxiv:
- '2106.11732'
file:
- access_level: open_access
checksum: 97c8a8470759cab597abb973ca137a3b
content_type: application/pdf
creator: dernst
date_created: 2023-02-23T10:30:04Z
date_updated: 2023-02-23T10:30:04Z
file_id: '12673'
file_name: 2022_TMLR_Iofinova.pdf
file_size: 1948063
relation: main_file
success: 1
file_date_updated: 2023-02-23T10:30:04Z
has_accepted_license: '1'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://openreview.net/forum?id=XsPopigZXV
month: '12'
oa: 1
oa_version: Published Version
project:
- _id: 9B9290DE-BA93-11EA-9121-9846C619BF3A
grant_number: W1260-N35
name: Vienna Graduate School on Computational Optimization
publication: Transactions on Machine Learning Research
publication_identifier:
issn:
- 2835-8856
publication_status: published
publisher: ML Research Press
quality_controlled: '1'
related_material:
link:
- description: source code
relation: software
url: https://github.com/ISTAustria-CVML/FLEA
status: public
title: 'FLEA: Provably robust fair multisource learning from unreliable training 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: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2022'
...
---
_id: '17088'
abstract:
- lang: eng
text: 'In this paper, we consider the problem of sparsifying BERT models, which
are a key building block for natural language processing, in order to reduce their
storage and computational cost. We introduce the Optimal BERT Surgeon (oBERT),
an efficient and accurate pruning method based on approximate second-order information,
which we show to yield state-of-the-art results in both stages of language tasks:
pre-training and fine-tuning. Specifically, oBERT extends existing work on second-order
pruning by allowing for pruning weight blocks, and is the first such method that
is applicable at BERT scale. Second, we investigate compounding compression approaches
to obtain highly compressed but accurate models for deployment on edge devices.
These models significantly push boundaries of the current state-of-the-art sparse
BERT models with respect to all metrics: model size, inference speed and task
accuracy. For example, relative to the dense BERT-base, we obtain 10x model size
compression with < 1% accuracy drop, 10x CPU-inference speedup with < 2% accuracy
drop, and 29x CPU-inference speedup with < 7.5% accuracy drop. Our code, fully
integrated with Transformers and SparseML, is available at https://github.com/neuralmagic/sparseml/tree/main/research/optimal_BERT_surgeon_oBERT.'
article_processing_charge: Yes
arxiv: 1
author:
- first_name: Eldar
full_name: Kurtic, Eldar
id: 47beb3a5-07b5-11eb-9b87-b108ec578218
last_name: Kurtic
- first_name: Daniel
full_name: Campos, Daniel
last_name: Campos
- first_name: Tuan
full_name: Nguyen, Tuan
last_name: Nguyen
- first_name: Elias
full_name: Frantar, Elias
id: 09a8f98d-ec99-11ea-ae11-c063a7b7fe5f
last_name: Frantar
- first_name: Mark
full_name: Kurtz, Mark
last_name: Kurtz
- first_name: Benjamin
full_name: Fineran, Benjamin
last_name: Fineran
- first_name: Michael
full_name: Goin, Michael
last_name: Goin
- first_name: Dan-Adrian
full_name: Alistarh, Dan-Adrian
id: 4A899BFC-F248-11E8-B48F-1D18A9856A87
last_name: Alistarh
orcid: 0000-0003-3650-940X
citation:
ama: 'Kurtic E, Campos D, Nguyen T, et al. The optimal BERT surgeon: Scalable and
accurate second-order pruning for large language models. In: Proceedings of
the 2022 Conference on Empirical Methods in Natural Language Processing. Association
for Computational Linguistics; 2022:4163-4181. doi:10.18653/v1/2022.emnlp-main.279'
apa: 'Kurtic, E., Campos, D., Nguyen, T., Frantar, E., Kurtz, M., Fineran, B., …
Alistarh, D.-A. (2022). The optimal BERT surgeon: Scalable and accurate second-order
pruning for large language models. In Proceedings of the 2022 Conference on
Empirical Methods in Natural Language Processing (pp. 4163–4181). Abu Dhabi,
United Arab Emirates: Association for Computational Linguistics. https://doi.org/10.18653/v1/2022.emnlp-main.279'
chicago: 'Kurtic, Eldar, Daniel Campos, Tuan Nguyen, Elias Frantar, Mark Kurtz,
Benjamin Fineran, Michael Goin, and Dan-Adrian Alistarh. “The Optimal BERT Surgeon:
Scalable and Accurate Second-Order Pruning for Large Language Models.” In Proceedings
of the 2022 Conference on Empirical Methods in Natural Language Processing,
4163–81. Association for Computational Linguistics, 2022. https://doi.org/10.18653/v1/2022.emnlp-main.279.'
ieee: 'E. Kurtic et al., “The optimal BERT surgeon: Scalable and accurate
second-order pruning for large language models,” in Proceedings of the 2022
Conference on Empirical Methods in Natural Language Processing, Abu Dhabi,
United Arab Emirates, 2022, pp. 4163–4181.'
ista: 'Kurtic E, Campos D, Nguyen T, Frantar E, Kurtz M, Fineran B, Goin M, Alistarh
D-A. 2022. The optimal BERT surgeon: Scalable and accurate second-order pruning
for large language models. Proceedings of the 2022 Conference on Empirical Methods
in Natural Language Processing. EMNLP: Conference on Empirical Methods in Natural
Language Processing, 4163–4181.'
mla: 'Kurtic, Eldar, et al. “The Optimal BERT Surgeon: Scalable and Accurate Second-Order
Pruning for Large Language Models.” Proceedings of the 2022 Conference on Empirical
Methods in Natural Language Processing, Association for Computational Linguistics,
2022, pp. 4163–81, doi:10.18653/v1/2022.emnlp-main.279.'
short: E. Kurtic, D. Campos, T. Nguyen, E. Frantar, M. Kurtz, B. Fineran, M. Goin,
D.-A. Alistarh, in:, Proceedings of the 2022 Conference on Empirical Methods in
Natural Language Processing, Association for Computational Linguistics, 2022,
pp. 4163–4181.
conference:
end_date: 2022-12-11
location: Abu Dhabi, United Arab Emirates
name: 'EMNLP: Conference on Empirical Methods in Natural Language Processing'
start_date: 2022-12-07
corr_author: '1'
date_created: 2024-05-29T06:40:55Z
date_published: 2022-12-01T00:00:00Z
date_updated: 2024-07-31T11:05:32Z
day: '01'
ddc:
- '000'
department:
- _id: DaAl
doi: 10.18653/v1/2022.emnlp-main.279
external_id:
arxiv:
- '2203.07259'
file:
- access_level: open_access
checksum: c47b9edd8a9f743ac77a593de6d2e84a
content_type: application/pdf
creator: dernst
date_created: 2024-07-31T11:03:34Z
date_updated: 2024-07-31T11:03:34Z
file_id: '17354'
file_name: 2022_EMNLP_Kurtic.pdf
file_size: 522563
relation: main_file
success: 1
file_date_updated: 2024-07-31T11:03:34Z
has_accepted_license: '1'
language:
- iso: eng
month: '12'
oa: 1
oa_version: Published Version
page: 4163-4181
publication: Proceedings of the 2022 Conference on Empirical Methods in Natural Language
Processing
publication_status: published
publisher: Association for Computational Linguistics
quality_controlled: '1'
related_material:
link:
- relation: software
url: https://github.com/neuralmagic/sparseml/tree/main/research/optimal_BERT_surgeon_oBERT
scopus_import: '1'
status: public
title: 'The optimal BERT surgeon: Scalable and accurate second-order pruning for large
language models'
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: '2022'
...
---
DOAJ_listed: '1'
OA_place: publisher
OA_type: gold
_id: '10769'
abstract:
- lang: eng
text: studiamos aspectos de Teoría Cuántica de Campos a densidad finita usando técnicas
y conceptos de información cuántica. Nos enfocamos en fermiones de Dirac masivos
con potencial químico en 1+1 dimensiones espacio-temporales. Usando la entropía
de entrelazamiento en un intervalo, construimos la función c entrópica que es
finita. Esta función c no es monótona, e incorpora el entrelazamiento de largo
alcance proveniente de la superficie de Fermi. Motivados por trabajos previos
de modelos en la red, calculamos numéricamente las entropías de Renyi y encontramos
oscilaciones de Friedel. Seguidamente, analizamos la información mutua como una
medida de correlación entre diferentes regiones. Usando una expansión de distancia
grande desarrollada por Cardy, argumentamos que la información mutua detecta las
correlaciones inducidas por la superficie de Fermi todavía al orden dominante
en la expansión. Finalmente, analizamos la entropía relativa y sus generalizaciones
de Renyi para distinguir estados con diferente carga. Encontramos que estados
en diferentes sectores de superselección dan origen a un comportamiento super-extensivo
en la entropía relativa.
acknowledgement: "Se agradece a Horacio Casini por distintas discusiones y comentarios
a lo largo del trabajo. LD cuenta con el apoyo de CNEA y UNCuyo, Inst. GT cuenta
con el apoyo de CONICET,\r\nANPCyT, CNEA, y UNCuyo, Inst. Balseiro. RM cuenta con
el apoyo de IST Austria. MS cuenta con el apoyode CONICET y UNCuyo, Inst. Balseiro.
También se agradece a la Asociación Argentina de Física por la posibilidad de presentar
este artículo en el marco de una Mención Especial por el Premio Luis Másperi 2020."
article_processing_charge: No
article_type: original
author:
- first_name: L.
full_name: Daguerre, L.
last_name: Daguerre
- first_name: G.
full_name: Torroba, G.
last_name: Torroba
- first_name: Raimel A
full_name: Medina Ramos, Raimel A
id: CE680B90-D85A-11E9-B684-C920E6697425
last_name: Medina Ramos
orcid: 0000-0002-5383-2869
- first_name: M.
full_name: Solís, M.
last_name: Solís
citation:
ama: 'Daguerre L, Torroba G, Medina Ramos RA, Solís M. Non relativistic quantum
field theory: Dynamics and irreversibility. Anales de la Asociacion Fisica
Argentina. 2022;32(4):93-98. doi:10.31527/analesafa.2021.32.4.93'
apa: 'Daguerre, L., Torroba, G., Medina Ramos, R. A., & Solís, M. (2022). Non
relativistic quantum field theory: Dynamics and irreversibility. Anales de
la Asociacion Fisica Argentina. Asociación Física Argentina. https://doi.org/10.31527/analesafa.2021.32.4.93'
chicago: 'Daguerre, L., G. Torroba, Raimel A Medina Ramos, and M. Solís. “Non relativistic
quantum field theory: Dynamics and irreversibility.” Anales de la Asociacion
Fisica Argentina. Asociación Física Argentina, 2022. https://doi.org/10.31527/analesafa.2021.32.4.93.'
ieee: 'L. Daguerre, G. Torroba, R. A. Medina Ramos, and M. Solís, “Non relativistic
quantum field theory: Dynamics and irreversibility,” Anales de la Asociacion
Fisica Argentina, vol. 32, no. 4. Asociación Física Argentina, pp. 93–98,
2022.'
ista: 'Daguerre L, Torroba G, Medina Ramos RA, Solís M. 2022. Non relativistic quantum
field theory: Dynamics and irreversibility. Anales de la Asociacion Fisica Argentina.
32(4), 93–98.'
mla: 'Daguerre, L., et al. “Non relativistic quantum field theory: Dynamics and
irreversibility.” Anales de la Asociacion Fisica Argentina, vol. 32, no.
4, Asociación Física Argentina, 2022, pp. 93–98, doi:10.31527/analesafa.2021.32.4.93.'
short: L. Daguerre, G. Torroba, R.A. Medina Ramos, M. Solís, Anales de la Asociacion
Fisica Argentina 32 (2022) 93–98.
date_created: 2022-02-20T23:01:32Z
date_published: 2022-01-13T00:00:00Z
date_updated: 2026-04-02T12:30:12Z
day: '13'
ddc:
- '530'
department:
- _id: MaSe
doi: 10.31527/analesafa.2021.32.4.93
file:
- access_level: open_access
checksum: ca66a3017205677c5b4d22b3bb74fb0b
content_type: application/pdf
creator: dernst
date_created: 2022-02-21T09:32:44Z
date_updated: 2022-02-21T09:32:44Z
file_id: '10782'
file_name: 2022_AnalesAFA_Daguerre.pdf
file_size: 4505751
relation: main_file
success: 1
file_date_updated: 2022-02-21T09:32:44Z
has_accepted_license: '1'
intvolume: ' 32'
issue: '4'
language:
- iso: spa
month: '01'
oa: 1
oa_version: Published Version
page: 93-98
publication: Anales de la Asociacion Fisica Argentina
publication_identifier:
eissn:
- 1850-1168
publication_status: published
publisher: Asociación Física Argentina
quality_controlled: '1'
scopus_import: '1'
status: public
title: 'Non relativistic quantum field theory: Dynamics and irreversibility'
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: ba8df636-2132-11f1-aed0-ed93e2281fdd
volume: 32
year: '2022'
...
---
_id: '10797'
abstract:
- lang: eng
text: We consider symmetric partial exclusion and inclusion processes in a general
graph in contact with reservoirs, where we allow both for edge disorder and well-chosen
site disorder. We extend the classical dualities to this context and then we derive
new orthogonal polynomial dualities. From the classical dualities, we derive the
uniqueness of the non-equilibrium steady state and obtain correlation inequalities.
Starting from the orthogonal polynomial dualities, we show universal properties
of n-point correlation functions in the non-equilibrium steady state for systems
with at most two different reservoir parameters, such as a chain with reservoirs
at left and right ends.
- lang: fre
text: Nous considérons des processus d’exclusion partielle, et des processus d’inclusion
sur un graphe général en contact avec des réservoirs. Nous autorisons la présence
de inhomogenéités sur les arrêts ainsi que sur les sommets du graph. Nous généralisons
les “dualités classiques” dans ce contexte et nous démontrons des nouvelles dualités
orthogonales. À partir des dualités classiques, nous démontrons l’unicité de l’état
stationnaire non-équilibre, ainsi que des inégalités de corrélation. À partir
des dualités orthogonales nous démontrons des propriétés universelles des fonctions
de corrélation à n points dans l’état stationnaire non-équilibre pour des systèmes
avec deux paramètres de réservoirs inégaux, comme par exemple une chaîne avec
des réservoirs à droite et à gauche.
acknowledgement: The authors would like to thank Gioia Carinci and Cristian Giardinà
for useful discussions. F.R. and S.F. thank Jean-René Chazottes for a stay at CPHT
(Institut Polytechnique de Paris), in the realm of Chaire d’Alembert (Paris-Saclay
University), where part of this work was performed. S.F. acknowledges Simona Villa
for her support in creating the picture. S.F. acknowledges financial support from
NWO via the grant TOP1.17.019. F.S. acknowledges financial support from the European
Union’s Horizon 2020 research and innovation programme under the Marie-Skłodowska-Curie
grant agreement No. 754411.
article_processing_charge: No
article_type: original
arxiv: 1
author:
- first_name: Simone
full_name: Floreani, Simone
last_name: Floreani
- first_name: Frank
full_name: Redig, Frank
last_name: Redig
- first_name: Federico
full_name: Sau, Federico
id: E1836206-9F16-11E9-8814-AEFDE5697425
last_name: Sau
citation:
ama: Floreani S, Redig F, Sau F. Orthogonal polynomial duality of boundary driven
particle systems and non-equilibrium correlations. Annales de l’institut Henri
Poincare (B) Probability and Statistics. 2022;58(1):220-247. doi:10.1214/21-AIHP1163
apa: Floreani, S., Redig, F., & Sau, F. (2022). Orthogonal polynomial duality
of boundary driven particle systems and non-equilibrium correlations. Annales
de l’institut Henri Poincare (B) Probability and Statistics. Institute of
Mathematical Statistics. https://doi.org/10.1214/21-AIHP1163
chicago: Floreani, Simone, Frank Redig, and Federico Sau. “Orthogonal Polynomial
Duality of Boundary Driven Particle Systems and Non-Equilibrium Correlations.”
Annales de l’institut Henri Poincare (B) Probability and Statistics. Institute
of Mathematical Statistics, 2022. https://doi.org/10.1214/21-AIHP1163.
ieee: S. Floreani, F. Redig, and F. Sau, “Orthogonal polynomial duality of boundary
driven particle systems and non-equilibrium correlations,” Annales de l’institut
Henri Poincare (B) Probability and Statistics, vol. 58, no. 1. Institute of
Mathematical Statistics, pp. 220–247, 2022.
ista: Floreani S, Redig F, Sau F. 2022. Orthogonal polynomial duality of boundary
driven particle systems and non-equilibrium correlations. Annales de l’institut
Henri Poincare (B) Probability and Statistics. 58(1), 220–247.
mla: Floreani, Simone, et al. “Orthogonal Polynomial Duality of Boundary Driven
Particle Systems and Non-Equilibrium Correlations.” Annales de l’institut Henri
Poincare (B) Probability and Statistics, vol. 58, no. 1, Institute of Mathematical
Statistics, 2022, pp. 220–47, doi:10.1214/21-AIHP1163.
short: S. Floreani, F. Redig, F. Sau, Annales de l’institut Henri Poincare (B) Probability
and Statistics 58 (2022) 220–247.
date_created: 2022-02-27T23:01:50Z
date_published: 2022-02-01T00:00:00Z
date_updated: 2025-04-14T07:43:48Z
day: '01'
department:
- _id: JaMa
doi: 10.1214/21-AIHP1163
ec_funded: 1
external_id:
arxiv:
- '2007.08272'
isi:
- '000752489300010'
intvolume: ' 58'
isi: 1
issue: '1'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/2007.08272
month: '02'
oa: 1
oa_version: Preprint
page: 220-247
project:
- _id: 260C2330-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '754411'
name: ISTplus - Postdoctoral Fellowships
publication: Annales de l'institut Henri Poincare (B) Probability and Statistics
publication_identifier:
issn:
- 0246-0203
publication_status: published
publisher: Institute of Mathematical Statistics
quality_controlled: '1'
scopus_import: '1'
status: public
title: Orthogonal polynomial duality of boundary driven particle systems and non-equilibrium
correlations
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 58
year: '2022'
...
---
_id: '18229'
abstract:
- lang: eng
text: We present Self-Classifier – a novel self-supervised end-to-end classification
learning approach. Self-Classifier learns labels and representations simultaneously
in a single-stage end-to-end manner by optimizing for same-class prediction of
two augmented views of the same sample. To guarantee non-degenerate solutions
(i.e., solutions where all labels are assigned to the same class) we propose a
mathematically motivated variant of the cross-entropy loss that has a uniform
prior asserted on the predicted labels. In our theoretical analysis, we prove
that degenerate solutions are not in the set of optimal solutions of our approach.
Self-Classifier is simple to implement and scalable. Unlike other popular unsupervised
classification and contrastive representation learning approaches, it does not
require any form of pre-training, expectation-maximization, pseudo-labeling, external
clustering, a second network, stop-gradient operation, or negative pairs. Despite
its simplicity, our approach sets a new state of the art for unsupervised classification
of ImageNet; and even achieves comparable to state-of-the-art results for unsupervised
representation learning. Code is available at https://github.com/elad-amrani/self-classifier.
alternative_title:
- LNCS
article_processing_charge: No
arxiv: 1
author:
- first_name: Elad
full_name: Amrani, Elad
last_name: Amrani
- first_name: Leonid
full_name: Karlinsky, Leonid
last_name: Karlinsky
- first_name: Alexander
full_name: Bronstein, Alexander
id: 58f3726e-7cba-11ef-ad8b-e6e8cb3904e6
last_name: Bronstein
orcid: 0000-0001-9699-8730
citation:
ama: 'Amrani E, Karlinsky L, Bronstein AM. Self-supervised classification network.
In: 17th European Conference on Computer Vision. Vol 13691. Springer Nature;
2022:116-132. doi:10.1007/978-3-031-19821-2_7'
apa: 'Amrani, E., Karlinsky, L., & Bronstein, A. M. (2022). Self-supervised
classification network. In 17th European Conference on Computer Vision
(Vol. 13691, pp. 116–132). Tel Aviv, Israel: Springer Nature. https://doi.org/10.1007/978-3-031-19821-2_7'
chicago: Amrani, Elad, Leonid Karlinsky, and Alex M. Bronstein. “Self-Supervised
Classification Network.” In 17th European Conference on Computer Vision,
13691:116–32. Springer Nature, 2022. https://doi.org/10.1007/978-3-031-19821-2_7.
ieee: E. Amrani, L. Karlinsky, and A. M. Bronstein, “Self-supervised classification
network,” in 17th European Conference on Computer Vision, Tel Aviv, Israel,
2022, vol. 13691, pp. 116–132.
ista: 'Amrani E, Karlinsky L, Bronstein AM. 2022. Self-supervised classification
network. 17th European Conference on Computer Vision. ECCV: European Conference
on Computer Vision, LNCS, vol. 13691, 116–132.'
mla: Amrani, Elad, et al. “Self-Supervised Classification Network.” 17th European
Conference on Computer Vision, vol. 13691, Springer Nature, 2022, pp. 116–32,
doi:10.1007/978-3-031-19821-2_7.
short: E. Amrani, L. Karlinsky, A.M. Bronstein, in:, 17th European Conference on
Computer Vision, Springer Nature, 2022, pp. 116–132.
conference:
end_date: 2022-10-27
location: Tel Aviv, Israel
name: 'ECCV: European Conference on Computer Vision'
start_date: 2022-10-23
date_created: 2024-10-08T12:55:44Z
date_published: 2022-10-23T00:00:00Z
date_updated: 2024-10-15T07:04:39Z
day: '23'
doi: 10.1007/978-3-031-19821-2_7
extern: '1'
external_id:
arxiv:
- '2103.10994'
intvolume: ' 13691'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2103.10994
month: '10'
oa: 1
oa_version: None
page: 116-132
publication: 17th European Conference on Computer Vision
publication_identifier:
eisbn:
- '9783031198212'
eissn:
- 1611-3349
isbn:
- '9783031198205'
issn:
- 0302-9743
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
related_material:
link:
- relation: software
url: https://github.com/elad-amrani/self-classifier
scopus_import: '1'
status: public
title: Self-supervised classification network
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 13691
year: '2022'
...
---
OA_place: repository
OA_type: green
_id: '18231'
abstract:
- lang: eng
text: 'The success of learning with noisy labels (LNL) methods relies heavily on
the success of a warm-up stage where standard supervised training is performed
using the full (noisy) training set. In this paper, we identify a "warm-up obstacle":
the inability of standard warm-up stages to train high quality feature extractors
and avert memorization of noisy labels. We propose "Contrast to Divide" (C2D),
a simple framework that solves this problem by pre-training the feature extractor
in a self-supervised fashion. Using self-supervised pre-training boosts the performance
of existing LNL approaches by drastically reducing the warm-up stage''s susceptibility
to noise level, shortening its duration, and improving extracted feature quality.
C2D works out of the box with existing methods and demonstrates markedly improved
performance, especially in the high noise regime, where we get a boost of more
than 27% for CIFAR-100 with 90% noise over the previous state of the art. In real-life
noise settings, C2D trained on mini-WebVision outperforms previous works both
in WebVision and ImageNet validation sets by 3% top-1 accuracy. We perform an
in-depth analysis of the framework, including investigating the performance of
different pre-training approaches and estimating the effective upper bound of
the LNL performance with semi-supervised learning. Code for reproducing our experiments
is available at https://github.com/ContrastToDivide/C2D.'
article_processing_charge: No
arxiv: 1
author:
- first_name: Evgenii
full_name: Zheltonozhskii, Evgenii
last_name: Zheltonozhskii
- first_name: Chaim
full_name: Baskin, Chaim
last_name: Baskin
- first_name: Avi
full_name: Mendelson, Avi
last_name: Mendelson
- first_name: Alexander
full_name: Bronstein, Alexander
id: 58f3726e-7cba-11ef-ad8b-e6e8cb3904e6
last_name: Bronstein
orcid: 0000-0001-9699-8730
- first_name: Or
full_name: Litany, Or
last_name: Litany
citation:
ama: 'Zheltonozhskii E, Baskin C, Mendelson A, Bronstein AM, Litany O. Contrast
to divide: Self-supervised pre-training for learning with noisy labels. In: IEEE/CVF
Winter Conference on Applications of Computer Vision. Institute of Electrical
and Electronics Engineers; 2022:387-397. doi:10.1109/wacv51458.2022.00046'
apa: 'Zheltonozhskii, E., Baskin, C., Mendelson, A., Bronstein, A. M., & Litany,
O. (2022). Contrast to divide: Self-supervised pre-training for learning with
noisy labels. In IEEE/CVF Winter Conference on Applications of Computer Vision
(pp. 387–397). Waikoloa, HI, United States: Institute of Electrical and Electronics
Engineers. https://doi.org/10.1109/wacv51458.2022.00046'
chicago: 'Zheltonozhskii, Evgenii, Chaim Baskin, Avi Mendelson, Alex M. Bronstein,
and Or Litany. “Contrast to Divide: Self-Supervised Pre-Training for Learning
with Noisy Labels.” In IEEE/CVF Winter Conference on Applications of Computer
Vision, 387–97. Institute of Electrical and Electronics Engineers, 2022. https://doi.org/10.1109/wacv51458.2022.00046.'
ieee: 'E. Zheltonozhskii, C. Baskin, A. Mendelson, A. M. Bronstein, and O. Litany,
“Contrast to divide: Self-supervised pre-training for learning with noisy labels,”
in IEEE/CVF Winter Conference on Applications of Computer Vision, Waikoloa,
HI, United States, 2022, pp. 387–397.'
ista: 'Zheltonozhskii E, Baskin C, Mendelson A, Bronstein AM, Litany O. 2022. Contrast
to divide: Self-supervised pre-training for learning with noisy labels. IEEE/CVF
Winter Conference on Applications of Computer Vision. WACV: Winter Conference
on Applications of Computer Vision, 387–397.'
mla: 'Zheltonozhskii, Evgenii, et al. “Contrast to Divide: Self-Supervised Pre-Training
for Learning with Noisy Labels.” IEEE/CVF Winter Conference on Applications
of Computer Vision, Institute of Electrical and Electronics Engineers, 2022,
pp. 387–97, doi:10.1109/wacv51458.2022.00046.'
short: E. Zheltonozhskii, C. Baskin, A. Mendelson, A.M. Bronstein, O. Litany, in:,
IEEE/CVF Winter Conference on Applications of Computer Vision, Institute of Electrical
and Electronics Engineers, 2022, pp. 387–397.
conference:
end_date: 2022-01-08
location: Waikoloa, HI, United States
name: 'WACV: Winter Conference on Applications of Computer Vision'
start_date: 2022-01-03
date_created: 2024-10-08T12:56:20Z
date_published: 2022-02-15T00:00:00Z
date_updated: 2024-10-15T07:27:12Z
day: '15'
doi: 10.1109/wacv51458.2022.00046
extern: '1'
external_id:
arxiv:
- '2103.13646'
language:
- iso: eng
month: '02'
oa_version: Preprint
page: 387-397
publication: IEEE/CVF Winter Conference on Applications of Computer Vision
publication_identifier:
eisbn:
- '9781665409155'
publication_status: published
publisher: Institute of Electrical and Electronics Engineers
quality_controlled: '1'
related_material:
link:
- relation: software
url: https://github.com/ContrastToDivide/C2D
scopus_import: '1'
status: public
title: 'Contrast to divide: Self-supervised pre-training for learning with noisy labels'
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2022'
...
---
_id: '11839'
abstract:
- lang: eng
text: "It is a highly desirable property for deep networks to be robust against\r\nsmall
input changes. One popular way to achieve this property is by designing\r\nnetworks
with a small Lipschitz constant. In this work, we propose a new\r\ntechnique for
constructing such Lipschitz networks that has a number of\r\ndesirable properties:
it can be applied to any linear network layer\r\n(fully-connected or convolutional),
it provides formal guarantees on the\r\nLipschitz constant, it is easy to implement
and efficient to run, and it can be\r\ncombined with any training objective and
optimization method. In fact, our\r\ntechnique is the first one in the literature
that achieves all of these\r\nproperties simultaneously. Our main contribution
is a rescaling-based weight\r\nmatrix parametrization that guarantees each network
layer to have a Lipschitz\r\nconstant of at most 1 and results in the learned
weight matrices to be close to\r\northogonal. Hence we call such layers almost-orthogonal
Lipschitz (AOL).\r\nExperiments and ablation studies in the context of image classification
with\r\ncertified robust accuracy confirm that AOL layers achieve results that
are on\r\npar with most existing methods. Yet, they are simpler to implement and
more\r\nbroadly applicable, because they do not require computationally expensive\r\nmatrix
orthogonalization or inversion steps as part of the network\r\narchitecture. We
provide code at https://github.com/berndprach/AOL."
alternative_title:
- LNCS
article_processing_charge: No
arxiv: 1
author:
- first_name: Bernd
full_name: Prach, Bernd
id: 2D561D42-C427-11E9-89B4-9C1AE6697425
last_name: Prach
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
citation:
ama: 'Prach B, Lampert C. Almost-orthogonal layers for efficient general-purpose
Lipschitz networks. In: Computer Vision – ECCV 2022. Vol 13681. Springer
Nature; 2022:350-365. doi:10.1007/978-3-031-19803-8_21'
apa: 'Prach, B., & Lampert, C. (2022). Almost-orthogonal layers for efficient
general-purpose Lipschitz networks. In Computer Vision – ECCV 2022 (Vol.
13681, pp. 350–365). Tel Aviv, Israel: Springer Nature. https://doi.org/10.1007/978-3-031-19803-8_21'
chicago: Prach, Bernd, and Christoph Lampert. “Almost-Orthogonal Layers for Efficient
General-Purpose Lipschitz Networks.” In Computer Vision – ECCV 2022, 13681:350–65.
Springer Nature, 2022. https://doi.org/10.1007/978-3-031-19803-8_21.
ieee: B. Prach and C. Lampert, “Almost-orthogonal layers for efficient general-purpose
Lipschitz networks,” in Computer Vision – ECCV 2022, Tel Aviv, Israel,
2022, vol. 13681, pp. 350–365.
ista: 'Prach B, Lampert C. 2022. Almost-orthogonal layers for efficient general-purpose
Lipschitz networks. Computer Vision – ECCV 2022. ECCV: European Conference on
Computer Vision, LNCS, vol. 13681, 350–365.'
mla: Prach, Bernd, and Christoph Lampert. “Almost-Orthogonal Layers for Efficient
General-Purpose Lipschitz Networks.” Computer Vision – ECCV 2022, vol.
13681, Springer Nature, 2022, pp. 350–65, doi:10.1007/978-3-031-19803-8_21.
short: B. Prach, C. Lampert, in:, Computer Vision – ECCV 2022, Springer Nature,
2022, pp. 350–365.
conference:
end_date: 2022-10-27
location: Tel Aviv, Israel
name: 'ECCV: European Conference on Computer Vision'
start_date: 2022-10-23
corr_author: '1'
date_created: 2022-08-12T15:09:47Z
date_published: 2022-10-23T00:00:00Z
date_updated: 2026-07-27T12:47:43Z
day: '23'
department:
- _id: GradSch
- _id: ChLa
doi: 10.1007/978-3-031-19803-8_21
external_id:
arxiv:
- '2208.03160'
isi:
- '000904104000021'
intvolume: ' 13681'
isi: 1
language:
- iso: eng
main_file_link:
- open_access: '1'
url: ' https://doi.org/10.48550/arXiv.2208.03160'
month: '10'
oa: 1
oa_version: Preprint
page: 350-365
publication: Computer Vision – ECCV 2022
publication_identifier:
eisbn:
- '9783031198038'
isbn:
- '9783031198021'
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
related_material:
record:
- id: '19759'
relation: dissertation_contains
status: public
scopus_import: '1'
status: public
title: Almost-orthogonal layers for efficient general-purpose Lipschitz networks
type: conference
user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345
volume: 13681
year: '2022'
...
---
_id: '12299'
abstract:
- lang: eng
text: 'Transfer learning is a classic paradigm by which models pretrained on large
“upstream” datasets are adapted to yield good results on “downstream” specialized
datasets. Generally, more accurate models on the “upstream” dataset tend to provide
better transfer accuracy “downstream”. In this work, we perform an in-depth investigation
of this phenomenon in the context of convolutional neural networks (CNNs) trained
on the ImageNet dataset, which have been pruned-that is, compressed by sparsifiying
their connections. We consider transfer using unstructured pruned models obtained
by applying several state-of-the-art pruning methods, including magnitude-based,
second-order, regrowth, lottery-ticket, and regularization approaches, in the
context of twelve standard transfer tasks. In a nutshell, our study shows that
sparse models can match or even outperform the transfer performance of dense models,
even at high sparsities, and, while doing so, can lead to significant inference
and even training speedups. At the same time, we observe and analyze significant
differences in the behaviour of different pruning methods. The code is available
at: https://github.com/IST-DASLab/sparse-imagenet-transfer.'
acknowledgement: he authors would like to sincerely thank Christoph Lampert and Nir
Shavit for fruitful discussions during the development of this work, and Eldar Kurtic
for experimental support. EI was supported in part by the FWF DK VGSCO, grant agreement
number W1260-N35, while AP and DA acknowledge generous support by the ERC, via Starting
Grant 805223 ScaleML.
article_processing_charge: No
arxiv: 1
author:
- first_name: Eugenia B
full_name: Iofinova, Eugenia B
id: f9a17499-f6e0-11ea-865d-fdf9a3f77117
last_name: Iofinova
orcid: 0000-0002-7778-3221
- first_name: Elena-Alexandra
full_name: Peste, Elena-Alexandra
id: 32D78294-F248-11E8-B48F-1D18A9856A87
last_name: Peste
- first_name: Mark
full_name: Kurtz, Mark
last_name: Kurtz
- first_name: Dan-Adrian
full_name: Alistarh, Dan-Adrian
id: 4A899BFC-F248-11E8-B48F-1D18A9856A87
last_name: Alistarh
orcid: 0000-0003-3650-940X
citation:
ama: 'Iofinova EB, Krumes A, Kurtz M, Alistarh D-A. How well do sparse ImageNet
models transfer? In: 2022 IEEE/CVF Conference on Computer Vision and Pattern
Recognition. IEEE; 2022:12256-12266. doi:10.1109/cvpr52688.2022.01195'
apa: 'Iofinova, E. B., Krumes, A., Kurtz, M., & Alistarh, D.-A. (2022). How
well do sparse ImageNet models transfer? In 2022 IEEE/CVF Conference on Computer
Vision and Pattern Recognition (pp. 12256–12266). New Orleans, LA, United
States: IEEE. https://doi.org/10.1109/cvpr52688.2022.01195'
chicago: Iofinova, Eugenia B, Alexandra Krumes, Mark Kurtz, and Dan-Adrian Alistarh.
“How Well Do Sparse ImageNet Models Transfer?” In 2022 IEEE/CVF Conference
on Computer Vision and Pattern Recognition, 12256–66. IEEE, 2022. https://doi.org/10.1109/cvpr52688.2022.01195.
ieee: E. B. Iofinova, A. Krumes, M. Kurtz, and D.-A. Alistarh, “How well do sparse
ImageNet models transfer?,” in 2022 IEEE/CVF Conference on Computer Vision
and Pattern Recognition, New Orleans, LA, United States, 2022, pp. 12256–12266.
ista: 'Iofinova EB, Krumes A, Kurtz M, Alistarh D-A. 2022. How well do sparse ImageNet
models transfer? 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
CVPR: Computer Vision and Pattern Recognition, 12256–12266.'
mla: Iofinova, Eugenia B., et al. “How Well Do Sparse ImageNet Models Transfer?”
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, IEEE,
2022, pp. 12256–66, doi:10.1109/cvpr52688.2022.01195.
short: E.B. Iofinova, A. Krumes, M. Kurtz, D.-A. Alistarh, in:, 2022 IEEE/CVF Conference
on Computer Vision and Pattern Recognition, IEEE, 2022, pp. 12256–12266.
conference:
end_date: 2022-06-24
location: New Orleans, LA, United States
name: 'CVPR: Computer Vision and Pattern Recognition'
start_date: 2022-06-18
corr_author: '1'
date_created: 2023-01-16T10:06:00Z
date_published: 2022-09-27T00:00:00Z
date_updated: 2026-08-12T06:34:08Z
day: '27'
department:
- _id: DaAl
- _id: ChLa
doi: 10.1109/cvpr52688.2022.01195
ec_funded: 1
external_id:
arxiv:
- '2111.13445'
isi:
- '000870759105034'
isi: 1
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2111.13445
month: '09'
oa: 1
oa_version: Preprint
page: 12256-12266
project:
- _id: 9B9290DE-BA93-11EA-9121-9846C619BF3A
grant_number: W1260-N35
name: Vienna Graduate School on Computational Optimization
- _id: 268A44D6-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '805223'
name: Elastic Coordination for Scalable Machine Learning
publication: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition
publication_identifier:
eissn:
- 2575-7075
publication_status: published
publisher: IEEE
quality_controlled: '1'
related_material:
record:
- id: '13074'
relation: dissertation_contains
status: public
scopus_import: '1'
status: public
title: How well do sparse ImageNet models transfer?
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2022'
...
---
_id: '11458'
abstract:
- lang: eng
text: 'The increasing computational requirements of deep neural networks (DNNs)
have led to significant interest in obtaining DNN models that are sparse, yet
accurate. Recent work has investigated the even harder case of sparse training,
where the DNN weights are, for as much as possible, already sparse to reduce computational
costs during training. Existing sparse training methods are often empirical and
can have lower accuracy relative to the dense baseline. In this paper, we present
a general approach called Alternating Compressed/DeCompressed (AC/DC) training
of DNNs, demonstrate convergence for a variant of the algorithm, and show that
AC/DC outperforms existing sparse training methods in accuracy at similar computational
budgets; at high sparsity levels, AC/DC even outperforms existing methods that
rely on accurate pre-trained dense models. An important property of AC/DC is that
it allows co-training of dense and sparse models, yielding accurate sparse–dense
model pairs at the end of the training process. This is useful in practice, where
compressed variants may be desirable for deployment in resource-constrained settings
without re-doing the entire training flow, and also provides us with insights
into the accuracy gap between dense and compressed models. The code is available
at: https://github.com/IST-DASLab/ACDC.'
acknowledged_ssus:
- _id: ScienComp
acknowledgement: This project has received funding from the European Research Council
(ERC) under the European Union’s Horizon 2020 research and innovation programme
(grant agreement No 805223 ScaleML), and a CNRS PEPS grant. This research was supported
by the Scientific Service Units (SSU) of IST Austria through resources provided
by Scientific Computing (SciComp). We would also like to thank Christoph Lampert
for his feedback on an earlier version of this work, as well as for providing hardware
for the Transformer-XL experiments.
alternative_title:
- Advances in Neural Information Processing Systems
article_processing_charge: No
arxiv: 1
author:
- first_name: Elena-Alexandra
full_name: Peste, Elena-Alexandra
id: 32D78294-F248-11E8-B48F-1D18A9856A87
last_name: Peste
- first_name: Eugenia B
full_name: Iofinova, Eugenia B
id: f9a17499-f6e0-11ea-865d-fdf9a3f77117
last_name: Iofinova
orcid: 0000-0002-7778-3221
- first_name: Adrian
full_name: Vladu, Adrian
last_name: Vladu
- first_name: Dan-Adrian
full_name: Alistarh, Dan-Adrian
id: 4A899BFC-F248-11E8-B48F-1D18A9856A87
last_name: Alistarh
orcid: 0000-0003-3650-940X
citation:
ama: 'Krumes A, Iofinova EB, Vladu A, Alistarh D-A. AC/DC: Alternating Compressed/DeCompressed
training of deep neural networks. In: 35th Conference on Neural Information
Processing Systems. Vol 34. Neural Information Processing Systems Foundation;
2021:8557-8570.'
apa: 'Krumes, A., Iofinova, E. B., Vladu, A., & Alistarh, D.-A. (2021). AC/DC:
Alternating Compressed/DeCompressed training of deep neural networks. In 35th
Conference on Neural Information Processing Systems (Vol. 34, pp. 8557–8570).
Virtual, Online: Neural Information Processing Systems Foundation.'
chicago: 'Krumes, Alexandra, Eugenia B Iofinova, Adrian Vladu, and Dan-Adrian Alistarh.
“AC/DC: Alternating Compressed/DeCompressed Training of Deep Neural Networks.”
In 35th Conference on Neural Information Processing Systems, 34:8557–70.
Neural Information Processing Systems Foundation, 2021.'
ieee: 'A. Krumes, E. B. Iofinova, A. Vladu, and D.-A. Alistarh, “AC/DC: Alternating
Compressed/DeCompressed training of deep neural networks,” in 35th Conference
on Neural Information Processing Systems, Virtual, Online, 2021, vol. 34,
pp. 8557–8570.'
ista: 'Krumes A, Iofinova EB, Vladu A, Alistarh D-A. 2021. AC/DC: Alternating Compressed/DeCompressed
training of deep neural networks. 35th Conference on Neural Information Processing
Systems. NeurIPS: Neural Information Processing Systems, Advances in Neural Information
Processing Systems, vol. 34, 8557–8570.'
mla: 'Krumes, Alexandra, et al. “AC/DC: Alternating Compressed/DeCompressed Training
of Deep Neural Networks.” 35th Conference on Neural Information Processing
Systems, vol. 34, Neural Information Processing Systems Foundation, 2021,
pp. 8557–70.'
short: A. Krumes, E.B. Iofinova, A. Vladu, D.-A. Alistarh, in:, 35th Conference
on Neural Information Processing Systems, Neural Information Processing Systems
Foundation, 2021, pp. 8557–8570.
conference:
end_date: 2021-12-14
location: Virtual, Online
name: 'NeurIPS: Neural Information Processing Systems'
start_date: 2021-12-06
corr_author: '1'
date_created: 2022-06-20T12:11:53Z
date_published: 2021-12-06T00:00:00Z
date_updated: 2026-06-18T17:18:20Z
day: '06'
ddc:
- '000'
department:
- _id: GradSch
- _id: DaAl
ec_funded: 1
external_id:
arxiv:
- '2106.12379'
intvolume: ' 34'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://proceedings.neurips.cc/paper/2021/file/48000647b315f6f00f913caa757a70b3-Paper.pdf
month: '12'
oa: 1
oa_version: Published Version
page: 8557-8570
project:
- _id: 268A44D6-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '805223'
name: Elastic Coordination for Scalable Machine Learning
publication: 35th Conference on Neural Information Processing Systems
publication_identifier:
isbn:
- '9781713845393'
issn:
- 1049-5258
publication_status: published
publisher: Neural Information Processing Systems Foundation
quality_controlled: '1'
related_material:
record:
- id: '13074'
relation: dissertation_contains
status: public
scopus_import: '1'
status: public
title: 'AC/DC: Alternating Compressed/DeCompressed training of deep neural networks'
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 34
year: '2021'
...
---
_id: '11604'
abstract:
- lang: eng
text: The NASA Transiting Exoplanet Survey Satellite (TESS) is observing tens of
millions of stars with time spans ranging from ∼27 days to about 1 yr of continuous
observations. This vast amount of data contains a wealth of information for variability,
exoplanet, and stellar astrophysics studies but requires a number of processing
steps before it can be fully utilized. In order to efficiently process all the
TESS data and make it available to the wider scientific community, the TESS Data
for Asteroseismology working group, as part of the TESS Asteroseismic Science
Consortium, has created an automated open-source processing pipeline to produce
light curves corrected for systematics from the short- and long-cadence raw photometry
data and to classify these according to stellar variability type. We will process
all stars down to a TESS magnitude of 15. This paper is the next in a series detailing
how the pipeline works. Here, we present our methodology for the automatic variability
classification of TESS photometry using an ensemble of supervised learners that
are combined into a metaclassifier. We successfully validate our method using
a carefully constructed labeled sample of Kepler Q9 light curves with a 27.4 days
time span mimicking single-sector TESS observations, on which we obtain an overall
accuracy of 94.9%. We demonstrate that our methodology can successfully classify
stars outside of our labeled sample by applying it to all ∼167,000 stars observed
in Q9 of the Kepler space mission.
acknowledgement: "The research leading to these results has received funding from
the European Research Council (ERC) under the European Union's Horizon 2020 research
and innovation program (grant agreement No. 670519: MAMSIE), from the KU Leuven
Research Council (grant C16/18/005: PARADISE), from the Research Foundation Flanders
(FWO) under grant agreement G0H5416N (ERC Runner Up Project), as well as from the
BELgian federal Science Policy Office (BELSPO) through PRODEX grant PLATO. D.J.A
acknowledges support from the STFC via an Ernest Rutherford Fellowship (ST/R00384X/1).
Funding for the Stellar Astrophysics Centre is provided by The Danish National Research
Foundation (grant agreement No.: DNRF106). R.H. and M.N.L. acknowledge the ESA PRODEX
program. This research was supported by the National Aeronautics and Space Administration
(80NSSC18K1585 and 80NSSC19K0379) awarded through the TESS Guest Investigator Program.
K.J.B. is supported by the National Science Foundation under Award AST-1903828.
J.S.K and K.J.B. were supported by funding from the European Research Council under
the European Community's Seventh Framework Programme (FP7/2007-2013)/ERC grant agreement
no. 338251 (StellarAges). D.M.B. gratefully acknowledges funding from a senior postdoctoral
fellowship from the Research Foundation Flanders (FWO) with grant agreement No.
1286521N. The research leading to these results has received funding from the Research
Foundation Flanders (FWO) under grant agreement G0A2917N (BlackGEM). R.A.G. acknowledges
support from the GOLF and PLATO CNES grants. L.M. was supported by the Premium Postdoctoral
Research Program of the Hungarian Academy of Sciences. The research leading to these
results has been supported by the Hungarian National Research, Development, and
Innovation Office (NKFIH) grant KH_18 130405 and the Lendület LP2014-17 and LP2018-7/2020
grants of the Hungarian Academy of Sciences. D.B. acknowledges support from the
NASA TESS Guest Investigator Program under award 80NSSC19K0385.\r\n\r\nThis paper
includes data collected by the TESS mission, which are publicly available from the
Mikulski Archive for Space Telescopes (MAST). Funding for the TESS mission is provided
by NASA's Science Mission directorate. This research has made use of NASA's Astrophysics
Data System as well as the NASA/IPAC Extragalactic Database (NED) which is operated
by the Jet Propulsion Laboratory, California Institute of Technology, under contract
with the National Aeronautics and Space Administration. Funding for the TESS Asteroseismic
Science Operations Centre is provided by the Danish National Research Foundation
(Grant agreement no.: DNRF106), ESA PRODEX (PEA 4000119301), and the Stellar Astrophysics
Centre (SAC) at Aarhus University. We thank the TESS team and staff and TASC/TASOC
for their support of the present work.\r\n\r\nThis paper includes data collected
by the Kepler mission. Funding for the Kepler and K2 mission was provided by NASA's
Science Mission Directorate. The authors acknowledge the efforts of the Kepler Mission
team in obtaining the light-curve data and data validation products used in this
publication. These data were generated by the Kepler Mission science pipeline through
the efforts of the Kepler Science Operations Center and Science Office. The Kepler
light curves are archived at the Mikulski Archive for Space Telescopes.\r\n\r\nThe
numerical results presented in this work were obtained at the Centre for Scientific
Computing, Aarhus. 37 This research made use of Astropy, a community-developed core
Python package for Astronomy (Astropy Collaboration et al. 2013, 2018).\r\n\r\nSoftware:
Scikit-learn (Pedregosa et al. 2011), Numpy (Harris et al. 2020), Astropy (Astropy
Collaboration et al. 2013, 2018), Scipy (Virtanen et al. 2020), Pandas (McKinney
2010; Pandas Development Team 2020), Lightkurve (Lightkurve Collaboration et al.
2018), XGBoost (Chen & Guestrin 2016), Tensorflow (Abadi et al. 2015)."
article_number: '209'
article_processing_charge: No
article_type: original
arxiv: 1
author:
- first_name: J.
full_name: Audenaert, J.
last_name: Audenaert
- first_name: J. S.
full_name: Kuszlewicz, J. S.
last_name: Kuszlewicz
- first_name: R.
full_name: Handberg, R.
last_name: Handberg
- first_name: A.
full_name: Tkachenko, A.
last_name: Tkachenko
- first_name: D. J.
full_name: Armstrong, D. J.
last_name: Armstrong
- first_name: M.
full_name: Hon, M.
last_name: Hon
- first_name: R.
full_name: Kgoadi, R.
last_name: Kgoadi
- first_name: M. N.
full_name: Lund, M. N.
last_name: Lund
- first_name: K. J.
full_name: Bell, K. J.
last_name: Bell
- first_name: Lisa Annabelle
full_name: Bugnet, Lisa Annabelle
id: d9edb345-f866-11ec-9b37-d119b5234501
last_name: Bugnet
orcid: 0000-0003-0142-4000
- first_name: D. M.
full_name: Bowman, D. M.
last_name: Bowman
- first_name: C.
full_name: Johnston, C.
last_name: Johnston
- first_name: R. A.
full_name: García, R. A.
last_name: García
- first_name: D.
full_name: Stello, D.
last_name: Stello
- first_name: L.
full_name: Molnár, L.
last_name: Molnár
- first_name: E.
full_name: Plachy, E.
last_name: Plachy
- first_name: D.
full_name: Buzasi, D.
last_name: Buzasi
- first_name: C.
full_name: Aerts, C.
last_name: Aerts
citation:
ama: 'Audenaert J, Kuszlewicz JS, Handberg R, et al. TESS Data for Asteroseismology
(T’DA) stellar variability classification pipeline: Setup and application to the
Kepler Q9 data. The Astronomical Journal. 2021;162(5). doi:10.3847/1538-3881/ac166a'
apa: 'Audenaert, J., Kuszlewicz, J. S., Handberg, R., Tkachenko, A., Armstrong,
D. J., Hon, M., … Aerts, C. (2021). TESS Data for Asteroseismology (T’DA) stellar
variability classification pipeline: Setup and application to the Kepler Q9 data.
The Astronomical Journal. IOP Publishing. https://doi.org/10.3847/1538-3881/ac166a'
chicago: 'Audenaert, J., J. S. Kuszlewicz, R. Handberg, A. Tkachenko, D. J. Armstrong,
M. Hon, R. Kgoadi, et al. “TESS Data for Asteroseismology (T’DA) Stellar Variability
Classification Pipeline: Setup and Application to the Kepler Q9 Data.” The
Astronomical Journal. IOP Publishing, 2021. https://doi.org/10.3847/1538-3881/ac166a.'
ieee: 'J. Audenaert et al., “TESS Data for Asteroseismology (T’DA) stellar
variability classification pipeline: Setup and application to the Kepler Q9 data,”
The Astronomical Journal, vol. 162, no. 5. IOP Publishing, 2021.'
ista: 'Audenaert J, Kuszlewicz JS, Handberg R, Tkachenko A, Armstrong DJ, Hon M,
Kgoadi R, Lund MN, Bell KJ, Bugnet LA, Bowman DM, Johnston C, García RA, Stello
D, Molnár L, Plachy E, Buzasi D, Aerts C. 2021. TESS Data for Asteroseismology
(T’DA) stellar variability classification pipeline: Setup and application to the
Kepler Q9 data. The Astronomical Journal. 162(5), 209.'
mla: 'Audenaert, J., et al. “TESS Data for Asteroseismology (T’DA) Stellar Variability
Classification Pipeline: Setup and Application to the Kepler Q9 Data.” The
Astronomical Journal, vol. 162, no. 5, 209, IOP Publishing, 2021, doi:10.3847/1538-3881/ac166a.'
short: J. Audenaert, J.S. Kuszlewicz, R. Handberg, A. Tkachenko, D.J. Armstrong,
M. Hon, R. Kgoadi, M.N. Lund, K.J. Bell, L.A. Bugnet, D.M. Bowman, C. Johnston,
R.A. García, D. Stello, L. Molnár, E. Plachy, D. Buzasi, C. Aerts, The Astronomical
Journal 162 (2021).
date_created: 2022-07-18T11:54:55Z
date_published: 2021-10-21T00:00:00Z
date_updated: 2022-08-19T10:01:56Z
day: '21'
doi: 10.3847/1538-3881/ac166a
extern: '1'
external_id:
arxiv:
- '2107.06301'
intvolume: ' 162'
issue: '5'
keyword:
- Space and Planetary Science
- Astronomy and Astrophysics
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/2107.06301
month: '10'
oa: 1
oa_version: Preprint
publication: The Astronomical Journal
publication_identifier:
eissn:
- 1538-3881
issn:
- 0004-6256
publication_status: published
publisher: IOP Publishing
quality_controlled: '1'
scopus_import: '1'
status: public
title: 'TESS Data for Asteroseismology (T’DA) stellar variability classification pipeline:
Setup and application to the Kepler Q9 data'
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 162
year: '2021'
...
---
_id: '10545'
abstract:
- lang: eng
text: Classical models with complex energy landscapes represent a perspective avenue
for the near-term application of quantum simulators. Until now, many theoretical
works studied the performance of quantum algorithms for models with a unique ground
state. However, when the classical problem is in a so-called clustering phase,
the ground state manifold is highly degenerate. As an example, we consider a 3-XORSAT
model defined on simple hypergraphs. The degeneracy of classical ground state
manifold translates into the emergence of an extensive number of Z2 symmetries,
which remain intact even in the presence of a quantum transverse magnetic field.
We establish a general duality approach that restricts the quantum problem to
a given sector of conserved Z2 charges and use it to study how the outcome of
the quantum adiabatic algorithm depends on the hypergraph geometry. We show that
the tree hypergraph which corresponds to a classically solvable instance of the
3-XORSAT problem features a constant gap, whereas the closed hypergraph encounters
a second-order phase transition with a gap vanishing as a power-law in the problem
size. The duality developed in this work provides a practical tool for studies
of quantum models with classically degenerate energy manifold and reveals potential
connections between glasses and gauge theories.
acknowledgement: We would like to thank S. De Nicola, A. Michaidilis, T. Gulden, Y.
Nez-Fernndez, P. Brighi, and S. Sack for fruitful discussions and valuable feedback
on the manuscript. M.S. acknowledges useful discussions with E. Altman, L. Cugliandolo,
and C. Laumann. We acknowledge support from the European Research Council (ERC)
under the European Union's Horizon 2020 Research and Innovation Programme Grant
Agreement No. 850899.
article_number: '062423'
article_processing_charge: No
article_type: original
arxiv: 1
author:
- first_name: Raimel A
full_name: Medina Ramos, Raimel A
id: CE680B90-D85A-11E9-B684-C920E6697425
last_name: Medina Ramos
orcid: 0000-0002-5383-2869
- first_name: Maksym
full_name: Serbyn, Maksym
id: 47809E7E-F248-11E8-B48F-1D18A9856A87
last_name: Serbyn
orcid: 0000-0002-2399-5827
citation:
ama: Medina Ramos RA, Serbyn M. Duality approach to quantum annealing of the 3-variable
exclusive-or satisfiability problem (3-XORSAT). Physical Review A. 2021;104(6).
doi:10.1103/physreva.104.062423
apa: Medina Ramos, R. A., & Serbyn, M. (2021). Duality approach to quantum annealing
of the 3-variable exclusive-or satisfiability problem (3-XORSAT). Physical
Review A. American Physical Society. https://doi.org/10.1103/physreva.104.062423
chicago: Medina Ramos, Raimel A, and Maksym Serbyn. “Duality Approach to Quantum
Annealing of the 3-Variable Exclusive-or Satisfiability Problem (3-XORSAT).” Physical
Review A. American Physical Society, 2021. https://doi.org/10.1103/physreva.104.062423.
ieee: R. A. Medina Ramos and M. Serbyn, “Duality approach to quantum annealing of
the 3-variable exclusive-or satisfiability problem (3-XORSAT),” Physical Review
A, vol. 104, no. 6. American Physical Society, 2021.
ista: Medina Ramos RA, Serbyn M. 2021. Duality approach to quantum annealing of
the 3-variable exclusive-or satisfiability problem (3-XORSAT). Physical Review
A. 104(6), 062423.
mla: Medina Ramos, Raimel A., and Maksym Serbyn. “Duality Approach to Quantum Annealing
of the 3-Variable Exclusive-or Satisfiability Problem (3-XORSAT).” Physical
Review A, vol. 104, no. 6, 062423, American Physical Society, 2021, doi:10.1103/physreva.104.062423.
short: R.A. Medina Ramos, M. Serbyn, Physical Review A 104 (2021).
date_created: 2021-12-14T20:46:07Z
date_published: 2021-12-14T00:00:00Z
date_updated: 2026-04-07T12:43:22Z
day: '14'
department:
- _id: MaSe
doi: 10.1103/physreva.104.062423
ec_funded: 1
external_id:
arxiv:
- '2106.06344'
isi:
- '000753659200004'
intvolume: ' 104'
isi: 1
issue: '6'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/2106.06344
month: '12'
oa: 1
oa_version: Preprint
project:
- _id: 23841C26-32DE-11EA-91FC-C7463DDC885E
call_identifier: H2020
grant_number: '850899'
name: 'Non-Ergodic Quantum Matter: Universality, Dynamics and Control'
publication: Physical Review A
publication_identifier:
eissn:
- 2469-9934
issn:
- 2469-9926
publication_status: published
publisher: American Physical Society
quality_controlled: '1'
related_material:
record:
- id: '17208'
relation: dissertation_contains
status: public
scopus_import: '1'
status: public
title: Duality approach to quantum annealing of the 3-variable exclusive-or satisfiability
problem (3-XORSAT)
type: journal_article
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
volume: 104
year: '2021'
...
---
_id: '9291'
abstract:
- lang: eng
text: "This .zip File contains the transport data for figures presented in the main
text and supplementary material of \"Enhancement of Proximity Induced Superconductivity
in Planar Germanium\" by K. Aggarwal, et. al. \r\nThe measurements were done using
Labber Software and the data is stored in the hdf5 file format. The files can
be opened using either the Labber Log Browser (https://labber.org/overview/) or
Labber Python API (http://labber.org/online-doc/api/LogFile.html)."
article_processing_charge: No
author:
- first_name: Georgios
full_name: Katsaros, Georgios
id: 38DB5788-F248-11E8-B48F-1D18A9856A87
last_name: Katsaros
orcid: 0000-0001-8342-202X
citation:
ama: 'Katsaros G. Raw transport data for: Enhancement of proximity induced superconductivity
in planar germanium. 2021. doi:10.15479/AT:ISTA:9291'
apa: 'Katsaros, G. (2021). Raw transport data for: Enhancement of proximity induced
superconductivity in planar germanium. Institute of Science and Technology Austria.
https://doi.org/10.15479/AT:ISTA:9291'
chicago: 'Katsaros, Georgios. “Raw Transport Data for: Enhancement of Proximity
Induced Superconductivity in Planar Germanium.” Institute of Science and Technology
Austria, 2021. https://doi.org/10.15479/AT:ISTA:9291.'
ieee: 'G. Katsaros, “Raw transport data for: Enhancement of proximity induced superconductivity
in planar germanium.” Institute of Science and Technology Austria, 2021.'
ista: 'Katsaros G. 2021. Raw transport data for: Enhancement of proximity induced
superconductivity in planar germanium, Institute of Science and Technology Austria,
10.15479/AT:ISTA:9291.'
mla: 'Katsaros, Georgios. Raw Transport Data for: Enhancement of Proximity Induced
Superconductivity in Planar Germanium. Institute of Science and Technology
Austria, 2021, doi:10.15479/AT:ISTA:9291.'
short: G. Katsaros, (2021).
date_created: 2021-03-27T13:47:49Z
date_published: 2021-03-29T00:00:00Z
date_updated: 2024-02-21T12:37:14Z
day: '29'
ddc:
- '530'
department:
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doi: 10.15479/AT:ISTA:9291
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checksum: 635df3c08fc13c3dac008cd421aefbe4
content_type: application/x-zip-compressed
creator: gkatsaro
date_created: 2021-03-27T13:46:17Z
date_updated: 2021-03-27T13:46:17Z
file_id: '9292'
file_name: Raw Data- Enhancement of Superconductivity in a Planar Ge hole gas.zip
file_size: 10616071
relation: main_file
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content_type: text/plain
creator: dernst
date_created: 2021-04-01T07:52:56Z
date_updated: 2021-04-01T07:52:56Z
file_id: '9302'
file_name: README.txt
file_size: 470
relation: main_file
success: 1
file_date_updated: 2021-04-01T07:52:56Z
has_accepted_license: '1'
month: '03'
oa: 1
oa_version: Published Version
publisher: Institute of Science and Technology Austria
status: public
title: 'Raw transport data for: Enhancement of proximity induced superconductivity
in planar germanium'
tmp:
image: /images/cc_0.png
legal_code_url: https://creativecommons.org/publicdomain/zero/1.0/legalcode
name: Creative Commons Public Domain Dedication (CC0 1.0)
short: CC0 (1.0)
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...
---
_id: '9323'
abstract:
- lang: eng
text: This .zip File contains the data for figures presented in the main text and
supplementary material of "A singlet triplet hole spin qubit in planar Ge" by
D. Jirovec, et. al. The measurements were done using Labber Software and the data
is stored in the hdf5 file format. The files can be opened using either the Labber
Log Browser (https://labber.org/overview/) or Labber Python API (http://labber.org/online-doc/api/LogFile.html).
A single file is acquired with QCodes and features the corresponding data type.
XRD data are in .dat format and a code to open the data is provided. The code
for simulations is as well provided in Python.
article_processing_charge: No
author:
- first_name: Daniel
full_name: Jirovec, Daniel
id: 4C473F58-F248-11E8-B48F-1D18A9856A87
last_name: Jirovec
orcid: 0000-0002-7197-4801
citation:
ama: Jirovec D. Research data for “A singlet-triplet hole spin qubit planar Ge.”
2021. doi:10.15479/AT:ISTA:9323
apa: Jirovec, D. (2021). Research data for “A singlet-triplet hole spin qubit planar
Ge.” Institute of Science and Technology Austria. https://doi.org/10.15479/AT:ISTA:9323
chicago: Jirovec, Daniel. “Research Data for ‘A Singlet-Triplet Hole Spin Qubit
Planar Ge.’” Institute of Science and Technology Austria, 2021. https://doi.org/10.15479/AT:ISTA:9323.
ieee: D. Jirovec, “Research data for ‘A singlet-triplet hole spin qubit planar Ge.’”
Institute of Science and Technology Austria, 2021.
ista: Jirovec D. 2021. Research data for ‘A singlet-triplet hole spin qubit planar
Ge’, Institute of Science and Technology Austria, 10.15479/AT:ISTA:9323.
mla: Jirovec, Daniel. Research Data for “A Singlet-Triplet Hole Spin Qubit Planar
Ge.” Institute of Science and Technology Austria, 2021, doi:10.15479/AT:ISTA:9323.
short: D. Jirovec, (2021).
contributor:
- contributor_type: project_member
first_name: Daniel
id: 4C473F58-F248-11E8-B48F-1D18A9856A87
last_name: Jirovec
date_created: 2021-04-14T09:50:22Z
date_published: 2021-04-14T00:00:00Z
date_updated: 2025-06-12T06:57:18Z
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department:
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- _id: GeKa
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creator: djirovec
date_created: 2021-04-14T09:48:47Z
date_updated: 2021-04-14T09:48:47Z
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file_name: DataRepositorySTqubit.zip
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creator: djirovec
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oa: 1
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title: Research data for "A singlet-triplet hole spin qubit planar Ge"
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type: research_data
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2021'
...
---
_id: '9327'
abstract:
- lang: eng
text: "This archive contains the missing sweater mesh animations and displacement
models for the code of \"Mechanics-Aware Deformation of Yarn Pattern Geometry\"\r\n\r\nCode
Repository: https://git.ist.ac.at/gsperl/MADYPG"
author:
- first_name: Georg
full_name: Sperl, Georg
id: 4DD40360-F248-11E8-B48F-1D18A9856A87
last_name: Sperl
- first_name: Rahul
full_name: Narain, Rahul
last_name: Narain
- first_name: Christopher J
full_name: Wojtan, Christopher J
id: 3C61F1D2-F248-11E8-B48F-1D18A9856A87
last_name: Wojtan
orcid: 0000-0001-6646-5546
citation:
ama: Sperl G, Narain R, Wojtan C. Mechanics-Aware Deformation of Yarn Pattern Geometry
(Additional Animation/Model Data). 2021. doi:10.15479/AT:ISTA:9327
apa: Sperl, G., Narain, R., & Wojtan, C. (2021). Mechanics-Aware Deformation
of Yarn Pattern Geometry (Additional Animation/Model Data). IST Austria. https://doi.org/10.15479/AT:ISTA:9327
chicago: Sperl, Georg, Rahul Narain, and Chris Wojtan. “Mechanics-Aware Deformation
of Yarn Pattern Geometry (Additional Animation/Model Data).” IST Austria, 2021.
https://doi.org/10.15479/AT:ISTA:9327.
ieee: G. Sperl, R. Narain, and C. Wojtan, “Mechanics-Aware Deformation of Yarn Pattern
Geometry (Additional Animation/Model Data).” IST Austria, 2021.
ista: Sperl G, Narain R, Wojtan C. 2021. Mechanics-Aware Deformation of Yarn Pattern
Geometry (Additional Animation/Model Data), IST Austria, 10.15479/AT:ISTA:9327.
mla: Sperl, Georg, et al. Mechanics-Aware Deformation of Yarn Pattern Geometry
(Additional Animation/Model Data). IST Austria, 2021, doi:10.15479/AT:ISTA:9327.
short: G. Sperl, R. Narain, C. Wojtan, (2021).
date_created: 2021-04-16T14:26:19Z
date_published: 2021-05-01T00:00:00Z
date_updated: 2026-06-18T19:57:47Z
ddc:
- '005'
department:
- _id: GradSch
- _id: ChWo
doi: 10.15479/AT:ISTA:9327
file:
- access_level: open_access
checksum: 0324cb519273371708743f3282e7c081
content_type: application/zip
creator: gsperl
date_created: 2021-04-16T14:15:12Z
date_updated: 2021-04-16T14:15:12Z
file_id: '9328'
file_name: MADYPG_extra_data.zip
file_size: 802586232
relation: main_file
success: 1
- access_level: open_access
checksum: 4c224551adf852b136ec21a4e13f0c1b
content_type: application/gzip
creator: pub-gitlab-bot
date_created: 2021-04-26T09:33:44Z
date_updated: 2021-04-26T09:33:44Z
file_id: '9353'
file_name: MADYPG.zip
file_size: 64962865
relation: main_file
file_date_updated: 2021-04-26T09:33:44Z
gitlab_commit_id: 6a77e7e22769230ae5f5edaa090fb4b828e57573
gitlab_url: https://git.ist.ac.at/gsperl/MADYPG
has_accepted_license: '1'
month: '05'
oa: 1
publisher: IST Austria
related_material:
record:
- id: '9818'
relation: used_for_analysis_in
status: public
status: public
title: Mechanics-Aware Deformation of Yarn Pattern Geometry (Additional Animation/Model
Data)
tmp:
legal_code_url: https://opensource.org/licenses/MIT
name: The MIT License
short: MIT
type: software
user_id: 8b945eb4-e2f2-11eb-945a-df72226e66a9
year: '2021'
...