---
OA_place: publisher
OA_type: gold
_id: '21949'
abstract:
- lang: eng
text: "Cardiac T1 mapping provides critical quantitative insights into myocardial
tissue composition, enabling the assessment of pathologies such as fibrosis, inflammation,
and edema.\r\nHowever, the inherently dynamic nature of the heart imposes strict
limits on acquisition\r\ntimes, making high-resolution T1 mapping a persistent
challenge. Compressed sensing (CS)\r\napproaches have reduced scan durations by
undersampling k-space and reconstructing images from partial data, and recent
studies show that jointly optimizing the undersampling\r\npatterns with the reconstruction
network can substantially improve performance. Still,\r\nmost current T1 mapping
pipelines rely on static, hand-crafted masks that do not exploit\r\nthe full acceleration
and accuracy potential. Furthermore, most existing methods do not\r\nlevarage
the physical T1 decay model in optimization. In this work, we introduce T1-\r\nPILOT:
an end-to-end method that explicitly incorporates the T1 signal relaxation model\r\ninto
the sampling–reconstruction framework to guide the learning of non-Cartesian trajectories,
cross-frame alignment, and T1 decay estimation. Through extensive experiments\r\non
the CMRxRecon dataset, T1-PILOT significantly outperforms several baseline strategies
(including learned single-mask and fixed radial or golden-angle sampling schemes),\r\nachieving
higher T1 map fidelity at greater acceleration factors. In particular, we observe
consistent gains in PSNR and VIF relative to existing methods, along with marked\r\nimprovements
in delineating finer myocardial structures. Our results highlight that optimizing
sampling trajectories in tandem with the physical relaxation model leads to both\r\nenhanced
quantitative accuracy and reduced acquisition times. Code for reproducing all\r\nexperiments
and results is available at https://github.com/tamirshor7/T1-PILOT"
alternative_title:
- PMLR
article_processing_charge: No
author:
- first_name: Tamir
full_name: Shor, Tamir
last_name: Shor
- first_name: Moti
full_name: Freiman, Moti
last_name: Freiman
- first_name: Chaim
full_name: Baskin, Chaim
last_name: Baskin
- first_name: Alexander
full_name: Bronstein, Alexander
id: 58f3726e-7cba-11ef-ad8b-e6e8cb3904e6
last_name: Bronstein
orcid: 0000-0001-9699-8730
citation:
ama: 'Shor T, Freiman M, Baskin C, Bronstein AM. T1-PILOT: Physics-informed learned
optimized trajectories for T1 mapping acceleration. In: Medical Imaging with
Deep Learning. Vol 315. ML Research Press; :1969-1982.'
apa: 'Shor, T., Freiman, M., Baskin, C., & Bronstein, A. M. (n.d.). T1-PILOT:
Physics-informed learned optimized trajectories for T1 mapping acceleration. In
Medical Imaging with Deep Learning (Vol. 315, pp. 1969–1982). Taipei, Taiwan:
ML Research Press.'
chicago: 'Shor, Tamir, Moti Freiman, Chaim Baskin, and Alex M. Bronstein. “T1-PILOT:
Physics-Informed Learned Optimized Trajectories for T1 Mapping Acceleration.”
In Medical Imaging with Deep Learning, 315:1969–82. ML Research Press,
n.d.'
ieee: 'T. Shor, M. Freiman, C. Baskin, and A. M. Bronstein, “T1-PILOT: Physics-informed
learned optimized trajectories for T1 mapping acceleration,” in Medical Imaging
with Deep Learning, Taipei, Taiwan, vol. 315, pp. 1969–1982.'
ista: 'Shor T, Freiman M, Baskin C, Bronstein AM. T1-PILOT: Physics-informed learned
optimized trajectories for T1 mapping acceleration. Medical Imaging with Deep
Learning. MIDL: Medical Imaging with Deep Learning, PMLR, vol. 315, 1969–1982.'
mla: 'Shor, Tamir, et al. “T1-PILOT: Physics-Informed Learned Optimized Trajectories
for T1 Mapping Acceleration.” Medical Imaging with Deep Learning, vol.
315, ML Research Press, pp. 1969–82.'
short: T. Shor, M. Freiman, C. Baskin, A.M. Bronstein, in:, Medical Imaging with
Deep Learning, ML Research Press, n.d., pp. 1969–1982.
conference:
end_date: 2026-07-10
location: Taipei, Taiwan
name: 'MIDL: Medical Imaging with Deep Learning'
start_date: 2026-07-08
corr_author: '1'
date_created: 2026-06-07T22:01:36Z
date_published: 2026-03-17T00:00:00Z
date_updated: 2026-06-08T08:05:24Z
day: '17'
ddc:
- '000'
department:
- _id: AlBr
has_accepted_license: '1'
intvolume: ' 315'
keyword:
- Cardiac T1 Mapping
- Trajectory Optimization and Reconstruction
- PhysicsInformed Deep-Learning
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://openreview.net/forum?id=nZaPtHbd6N#discussion
month: '03'
oa: 1
oa_version: Published Version
page: 1969-1982
publication: Medical Imaging with Deep Learning
publication_identifier:
eissn:
- 2640-3498
publication_status: accepted
publisher: ML Research Press
quality_controlled: '1'
related_material:
link:
- relation: software
url: https://github.com/tamirshor7/T1-PILOT
scopus_import: '1'
status: public
title: 'T1-PILOT: Physics-informed learned optimized trajectories for T1 mapping acceleration'
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
volume: 315
year: '2026'
...
---
OA_place: publisher
OA_type: gold
_id: '22146'
abstract:
- lang: eng
text: We study differentially private model training with stochastic gradient descent
under learning rate scheduling and correlated noise. Although correlated noise,
in particular via matrix factorizations, has been shown to improve accuracy, prior
theoretical work focused primarily on the prefix-sum workload. That workload assumes
a constant learning rate, whereas in practice learning rate schedules are widely
used to accelerate training and improve convergence. We close this gap by deriving
general upper and lower bounds for a broad class of learning rate schedules in
both single- and multi-epoch settings. Building on these results, we propose a
learning-rate-aware factorization that achieves improvements over prefix-sum factorizations
under both MaxSE and MeanSE error metrics. Our theoretical analysis yields memory-efficient
constructions suitable for practical deployment, and experiments on CIFAR-10 and
IMDB datasets confirm that schedule-aware factorizations improve accuracy in private
training.
acknowledgement: "We thank Rasmus Pagh, Christoph Lampert and Jalaj Upadhyay for valuable\r\ncomments
on an early draft. We thank Ryan Mckenna for a fruitful discussion on the experiment\r\ndesign.
We thank Antti Honkela for sharing insights on learning rate scheduling and DP.\r\nNikita
P. Kalinin: Funded in part by the Austrian Science Fund (FWF) [10.55776/COE12].\r\nJoel
Daniel Andersson: Funded by the European Union. Views and opinions expressed are
however\r\nthose of the author(s) only and do not necessarily reflect those of the
European Union or the European\r\nResearch Council Executive Agency. Neither the
European Union nor the granting authority can be\r\nheld responsible for them. This
project has received funding from the European Research Council\r\n(ERC) under the
European Union’s Horizon 2020 research and innovation programme (MoDynStruct,\r\nNo.
101019564). Additional funding by Providentia, a Data Science Distinguished Investigator
grant\r\nfrom Novo Nordisk Fonden, with additional support from VILLUM Investigator
grant 54451.\r\n"
alternative_title:
- LIPIcs
article_number: 2:1-2:21
article_processing_charge: No
arxiv: 1
author:
- first_name: Nikita
full_name: Kalinin, Nikita
id: 4b14526e-14d2-11ed-ba64-c14c9553d137
last_name: Kalinin
- first_name: Joel D
full_name: Andersson, Joel D
id: 4a893819-d954-11f0-89b1-e360bad9ccc5
last_name: Andersson
citation:
ama: 'Kalinin N, Andersson JD. Learning rate scheduling with matrix factorization
for private training. In: 7th Symposium on Foundations of Responsible Computing.
Vol 368. Schloss Dagstuhl - Leibniz-Zentrum für Informatik; 2026. doi:10.4230/LIPIcs.FORC.2026.2'
apa: 'Kalinin, N., & Andersson, J. D. (2026). Learning rate scheduling with
matrix factorization for private training. In 7th Symposium on Foundations
of Responsible Computing (Vol. 368). Cambridge, MA; United States: Schloss
Dagstuhl - Leibniz-Zentrum für Informatik. https://doi.org/10.4230/LIPIcs.FORC.2026.2'
chicago: Kalinin, Nikita, and Joel D Andersson. “Learning Rate Scheduling with Matrix
Factorization for Private Training.” In 7th Symposium on Foundations of Responsible
Computing, Vol. 368. Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2026.
https://doi.org/10.4230/LIPIcs.FORC.2026.2.
ieee: N. Kalinin and J. D. Andersson, “Learning rate scheduling with matrix factorization
for private training,” in 7th Symposium on Foundations of Responsible Computing,
Cambridge, MA; United States, 2026, vol. 368.
ista: 'Kalinin N, Andersson JD. 2026. Learning rate scheduling with matrix factorization
for private training. 7th Symposium on Foundations of Responsible Computing. FORC:
Symposium on Foundations of Responsible Computing, LIPIcs, vol. 368, 2:1-2:21.'
mla: Kalinin, Nikita, and Joel D. Andersson. “Learning Rate Scheduling with Matrix
Factorization for Private Training.” 7th Symposium on Foundations of Responsible
Computing, vol. 368, 2:1-2:21, Schloss Dagstuhl - Leibniz-Zentrum für Informatik,
2026, doi:10.4230/LIPIcs.FORC.2026.2.
short: N. Kalinin, J.D. Andersson, in:, 7th Symposium on Foundations of Responsible
Computing, Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2026.
conference:
end_date: 2026-06-05
location: Cambridge, MA; United States
name: 'FORC: Symposium on Foundations of Responsible Computing'
start_date: 2026-06-03
corr_author: '1'
das_tickbox: '0'
date_created: 2026-06-28T22:01:34Z
date_published: 2026-06-01T00:00:00Z
date_updated: 2026-06-29T06:56:34Z
day: '01'
ddc:
- '000'
department:
- _id: ChLa
- _id: GradSch
- _id: MoHe
doi: 10.4230/LIPIcs.FORC.2026.2
ec_funded: 1
external_id:
arxiv:
- '2511.17994'
file:
- access_level: open_access
checksum: c661f016d3861a1c1b590b87a744d087
content_type: application/pdf
creator: dernst
date_created: 2026-06-29T06:55:23Z
date_updated: 2026-06-29T06:55:23Z
file_id: '22149'
file_name: 2026_LIPIcsFORC_Kalinin.pdf
file_size: 1231914
relation: main_file
success: 1
file_date_updated: 2026-06-29T06:55:23Z
fulldoi: https://doi.org/10.4230/LIPIcs.FORC.2026.2
has_accepted_license: '1'
intvolume: ' 368'
keyword:
- differential privacy
- machine learning
- matrix factorization
language:
- iso: eng
month: '06'
oa: 1
oa_version: Published Version
project:
- _id: bd9ca328-d553-11ed-ba76-dc4f890cfe62
call_identifier: H2020
grant_number: '101019564'
name: The design and evaluation of modern fully dynamic data structures
publication: 7th Symposium on Foundations of Responsible Computing
publication_identifier:
eissn:
- 1868-8969
isbn:
- '9783959774192'
publication_status: published
publisher: Schloss Dagstuhl - Leibniz-Zentrum für Informatik
quality_controlled: '1'
researchdata_availability: no
scopus_import: '1'
status: public
supplementarymaterial: no
title: Learning rate scheduling with matrix factorization for private training
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
volume: 368
year: '2026'
...
---
OA_place: repository
OA_type: green
_id: '22294'
abstract:
- lang: eng
text: 'Modern computer systems store vast amounts of personal data, enabling advances
in AI and ML but risking user privacy and trust. For privacy reasons, it is sometimes
desired for an ML model to forget part of the data it was trained on. In this
paper, we introduce a novel unlearning approach based on Forgetting Neural Networks
(FNNs), a neuroscience-inspired architecture that explicitly encodes forgetting
through multiplicative decay factors. While FNNs had previously been studied as
a theoretical construct, we provide the first concrete implementation and demonstrate
their effectiveness for targeted unlearning. We propose several variants with
per-neuron forgetting factors, including rank-based assignments guided by activation
levels, and evaluate them on MNIST and Fashion-MNIST benchmarks. Our method systematically
removes information associated with forget sets while preserving performance on
retained data. Membership inference attacks confirm the effectiveness of FNN-based
unlearning in erasing information about the training data from the neural network.
These results establish FNNs as a promising foundation for efficient and interpretable
unlearning. '
article_processing_charge: No
arxiv: 1
author:
- first_name: Amartya
full_name: Hatua, Amartya
last_name: Hatua
- first_name: Trung
full_name: Nguyen, Trung
last_name: Nguyen
- first_name: Filip
full_name: Cano Cordoba, Filip
id: 708cad98-e86a-11ef-8098-bdae2d7c6af1
last_name: Cano Cordoba
orcid: 0000-0002-0783-904X
- first_name: Andrew
full_name: Sung, Andrew
last_name: Sung
citation:
ama: 'Hatua A, Nguyen T, Cano Cordoba F, Sung A. Machine unlearning using forgetting
neural networks. In: Proceedings of the 18th International Conference on Agents
and Artificial Intelligence. Vol 2. SciTePress; 2026:1536-1546. doi:10.5220/0014326500004052'
apa: 'Hatua, A., Nguyen, T., Cano Cordoba, F., & Sung, A. (2026). Machine unlearning
using forgetting neural networks. In Proceedings of the 18th International
Conference on Agents and Artificial Intelligence (Vol. 2, pp. 1536–1546).
Marbella, Spain: SciTePress. https://doi.org/10.5220/0014326500004052'
chicago: Hatua, Amartya, Trung Nguyen, Filip Cano Cordoba, and Andrew Sung. “Machine
Unlearning Using Forgetting Neural Networks.” In Proceedings of the 18th International
Conference on Agents and Artificial Intelligence, 2:1536–46. SciTePress, 2026.
https://doi.org/10.5220/0014326500004052.
ieee: A. Hatua, T. Nguyen, F. Cano Cordoba, and A. Sung, “Machine unlearning using
forgetting neural networks,” in Proceedings of the 18th International Conference
on Agents and Artificial Intelligence, Marbella, Spain, 2026, vol. 2, pp.
1536–1546.
ista: 'Hatua A, Nguyen T, Cano Cordoba F, Sung A. 2026. Machine unlearning using
forgetting neural networks. Proceedings of the 18th International Conference on
Agents and Artificial Intelligence. ICAART: International Conference on Agents
and Artificial Intelligence vol. 2, 1536–1546.'
mla: Hatua, Amartya, et al. “Machine Unlearning Using Forgetting Neural Networks.”
Proceedings of the 18th International Conference on Agents and Artificial Intelligence,
vol. 2, SciTePress, 2026, pp. 1536–46, doi:10.5220/0014326500004052.
short: A. Hatua, T. Nguyen, F. Cano Cordoba, A. Sung, in:, Proceedings of the 18th
International Conference on Agents and Artificial Intelligence, SciTePress, 2026,
pp. 1536–1546.
conference:
end_date: 2026-03-08
location: Marbella, Spain
name: 'ICAART: International Conference on Agents and Artificial Intelligence'
start_date: 2026-03-05
das_tickbox: '1'
date_created: 2026-07-13T09:46:46Z
date_published: 2026-06-30T00:00:00Z
date_updated: 2026-07-16T09:02:53Z
day: '30'
department:
- _id: ToHe
doi: 10.5220/0014326500004052
external_id:
arxiv:
- '2410.22374'
fulldoi: https://doi.org/10.5220/0014326500004052
intvolume: ' 2'
keyword:
- Machine Unlearning
- Neuroscience-Inspired Machine Learning
- Membership Inference Attacks
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2410.22374
month: '06'
oa: 1
oa_version: Preprint
page: 1536-1546
publication: Proceedings of the 18th International Conference on Agents and Artificial
Intelligence
publication_identifier:
eissn:
- 2184-433X
isbn:
- '9789897587962'
publication_status: published
publisher: SciTePress
quality_controlled: '1'
scopus_import: '1'
status: public
title: Machine unlearning using forgetting neural networks
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 2
year: '2026'
...
---
OA_embargo: 6 months
OA_place: publisher
_id: '19456'
abstract:
- lang: eng
text: "Making decisions requires flexibly adapting to changing environments, a process
that\r\ndepends on accurately interpreting current contingencies and integrating
them with\r\npast experience. Two brain regions are particularly critical for
this process, the medial\r\nprefrontal cortex (mPFC) and the hippocampus. Using
contextual information from the\r\nhippocampus, the mPFC selects relevant cognitive
frameworks and suppresses\r\nirrelevant ones to guide appropriate actions. Several
studies have shown that some\r\nmPFC pyramidal neurons become spatially tuned
when spatial information is required\r\nto guide goal-directed behavior. However,
the role of prefrontal spatial representations\r\nin learning and decision making
is not well understood. This work aims to characterize\r\nthe role of mPFC spatial
tuning in supporting a contextual association task. Rats were\r\ntrained to learn
two cue–location associations on a radial arm maze over multiple days,\r\nwhile
we simultaneously recorded from dorsal CA1 of the hippocampus and the\r\nprelimbic
area of the mPFC. We describe a subset of spatially tuned hippocampal and\r\nprefrontal
pyramidal neurons that “flicker” between multiple spatial representations on\r\ndifferent
trials, suggesting dynamic, context-dependent coding. This flickering may\r\nprovide
a substrate for how the network reorganizes in response to task demands,\r\nlikely
by enabling the flexible evaluation of competing representations. "
acknowledged_ssus:
- _id: PreCl
- _id: Bio
- _id: LifeSc
- _id: M-Shop
alternative_title:
- ISTA Thesis
article_processing_charge: No
author:
- first_name: Andrea D
full_name: Cumpelik, Andrea D
id: 3F158B32-F248-11E8-B48F-1D18A9856A87
last_name: Cumpelik
orcid: 0000-0003-1727-6612
citation:
ama: Cumpelik AD. The role of prefrontal spatial coding in supporting a contextual
association task. 2025. doi:10.15479/AT-ISTA-19456
apa: Cumpelik, A. D. (2025). The role of prefrontal spatial coding in supporting
a contextual association task. Institute of Science and Technology Austria.
https://doi.org/10.15479/AT-ISTA-19456
chicago: Cumpelik, Andrea D. “The Role of Prefrontal Spatial Coding in Supporting
a Contextual Association Task.” Institute of Science and Technology Austria, 2025.
https://doi.org/10.15479/AT-ISTA-19456.
ieee: A. D. Cumpelik, “The role of prefrontal spatial coding in supporting a contextual
association task,” Institute of Science and Technology Austria, 2025.
ista: Cumpelik AD. 2025. The role of prefrontal spatial coding in supporting a contextual
association task. Institute of Science and Technology Austria.
mla: Cumpelik, Andrea D. The Role of Prefrontal Spatial Coding in Supporting
a Contextual Association Task. Institute of Science and Technology Austria,
2025, doi:10.15479/AT-ISTA-19456.
short: A.D. Cumpelik, The Role of Prefrontal Spatial Coding in Supporting a Contextual
Association Task, Institute of Science and Technology Austria, 2025.
corr_author: '1'
date_created: 2025-03-25T11:22:38Z
date_published: 2025-02-18T00:00:00Z
date_updated: 2026-04-07T12:37:58Z
day: '18'
ddc:
- '612'
degree_awarded: PhD
department:
- _id: GradSch
- _id: JoCs
doi: 10.15479/AT-ISTA-19456
file:
- access_level: open_access
checksum: 1c7573303d8e5f6da3eb03d59055390f
content_type: application/pdf
creator: acumpeli
date_created: 2025-03-25T11:07:55Z
date_updated: 2025-09-30T22:30:02Z
embargo: 2025-09-30
file_id: '19457'
file_name: 2025_Thesis_Cumpelik_corrections_PDFA.pdf
file_size: 11869040
relation: main_file
- access_level: closed
checksum: b93265ebd9a53f7a14100d0d48b4ff5b
content_type: application/vnd.openxmlformats-officedocument.wordprocessingml.document
creator: acumpeli
date_created: 2025-03-25T11:08:05Z
date_updated: 2025-09-30T22:30:02Z
embargo_to: open_access
file_id: '19458'
file_name: 2025_Thesis_Cumpelik_corrections.docx
file_size: 20436467
relation: source_file
file_date_updated: 2025-09-30T22:30:02Z
fulldoi: https://doi.org/10.15479/AT-ISTA-19456
has_accepted_license: '1'
keyword:
- neuroscience
- decision making
- learning
- cognitive flexibility
- medial prefrontal cortex
- hippocampus
- electrophysiology
language:
- iso: eng
month: '02'
oa: 1
oa_version: Published Version
page: '96'
publication_identifier:
isbn:
- 978-3-99078-056-5
issn:
- 2663-337X
publication_status: published
publisher: Institute of Science and Technology Austria
status: public
supervisor:
- first_name: Jozsef L
full_name: Csicsvari, Jozsef L
id: 3FA14672-F248-11E8-B48F-1D18A9856A87
last_name: Csicsvari
orcid: 0000-0002-5193-4036
title: The role of prefrontal spatial coding in supporting a contextual association
task
type: dissertation
user_id: ba8df636-2132-11f1-aed0-ed93e2281fdd
year: '2025'
...
---
_id: '12976'
abstract:
- lang: eng
text: "3D printing based on continuous deposition of materials, such as filament-based
3D printing, has seen widespread adoption thanks to its versatility in working
with a wide range of materials. An important shortcoming of this type of technology
is its limited multi-material capabilities. While there are simple hardware designs
that enable multi-material printing in principle, the required software is heavily
underdeveloped. A typical hardware design fuses together individual materials
fed into a single chamber from multiple inlets before they are deposited. This
design, however, introduces a time delay between the intended material mixture
and its actual deposition. In this work, inspired by diverse path planning research
in robotics, we show that this mechanical challenge can be addressed via improved
printer control. We propose to formulate the search for optimal multi-material
printing policies in a reinforcement\r\nlearning setup. We put forward a simple
numerical deposition model that takes into account the non-linear material mixing
and delayed material deposition. To validate our system we focus on color fabrication,
a problem known for its strict requirements for varying material mixtures at a
high spatial frequency. We demonstrate that our learned control policy outperforms
state-of-the-art hand-crafted algorithms."
acknowledgement: This work is graciously supported by FWF Lise Meitner (Grant M 3319).
Kang Liao sincerely thank Emiliano Luci, Chunyu Lin, and Yao Zhao for their huge
support.
article_processing_charge: No
author:
- first_name: Kang
full_name: Liao, Kang
last_name: Liao
- first_name: Thibault
full_name: Tricard, Thibault
last_name: Tricard
- first_name: Michael
full_name: Piovarci, Michael
id: 62E473F4-5C99-11EA-A40E-AF823DDC885E
last_name: Piovarci
orcid: 0000-0002-5062-4474
- first_name: Hans-Peter
full_name: Seidel, Hans-Peter
last_name: Seidel
- first_name: Vahid
full_name: Babaei, Vahid
last_name: Babaei
citation:
ama: 'Liao K, Tricard T, Piovarci M, Seidel H-P, Babaei V. Learning deposition policies
for fused multi-material 3D printing. In: 2023 IEEE International Conference
on Robotics and Automation. Vol 2023. IEEE; 2023:12345-12352. doi:10.1109/ICRA48891.2023.10160465'
apa: 'Liao, K., Tricard, T., Piovarci, M., Seidel, H.-P., & Babaei, V. (2023).
Learning deposition policies for fused multi-material 3D printing. In 2023
IEEE International Conference on Robotics and Automation (Vol. 2023, pp. 12345–12352).
London, United Kingdom: IEEE. https://doi.org/10.1109/ICRA48891.2023.10160465'
chicago: Liao, Kang, Thibault Tricard, Michael Piovarci, Hans-Peter Seidel, and
Vahid Babaei. “Learning Deposition Policies for Fused Multi-Material 3D Printing.”
In 2023 IEEE International Conference on Robotics and Automation, 2023:12345–52.
IEEE, 2023. https://doi.org/10.1109/ICRA48891.2023.10160465.
ieee: K. Liao, T. Tricard, M. Piovarci, H.-P. Seidel, and V. Babaei, “Learning deposition
policies for fused multi-material 3D printing,” in 2023 IEEE International
Conference on Robotics and Automation, London, United Kingdom, 2023, vol.
2023, pp. 12345–12352.
ista: 'Liao K, Tricard T, Piovarci M, Seidel H-P, Babaei V. 2023. Learning deposition
policies for fused multi-material 3D printing. 2023 IEEE International Conference
on Robotics and Automation. ICRA: International Conference on Robotics and Automation
vol. 2023, 12345–12352.'
mla: Liao, Kang, et al. “Learning Deposition Policies for Fused Multi-Material 3D
Printing.” 2023 IEEE International Conference on Robotics and Automation,
vol. 2023, IEEE, 2023, pp. 12345–52, doi:10.1109/ICRA48891.2023.10160465.
short: K. Liao, T. Tricard, M. Piovarci, H.-P. Seidel, V. Babaei, in:, 2023 IEEE
International Conference on Robotics and Automation, IEEE, 2023, pp. 12345–12352.
conference:
end_date: 2023-06-02
location: London, United Kingdom
name: 'ICRA: International Conference on Robotics and Automation'
start_date: 2023-05-29
date_created: 2023-05-16T09:14:09Z
date_published: 2023-07-04T00:00:00Z
date_updated: 2025-04-15T07:43:52Z
day: '04'
ddc:
- '004'
department:
- _id: BeBi
doi: 10.1109/ICRA48891.2023.10160465
external_id:
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intvolume: ' 2023'
isi: 1
keyword:
- reinforcement learning
- deposition
- control
- color
- multi-filament
language:
- iso: eng
month: '07'
oa: 1
oa_version: Submitted Version
page: 12345-12352
project:
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grant_number: M03319
name: Perception-Aware Appearance Fabrication
publication: 2023 IEEE International Conference on Robotics and Automation
publication_identifier:
eisbn:
- '9798350323658'
issn:
- 1050-4729
publication_status: published
publisher: IEEE
quality_controlled: '1'
scopus_import: '1'
status: public
title: Learning deposition policies for fused multi-material 3D printing
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 2023
year: '2023'
...
---
OA_place: publisher
_id: '10799'
abstract:
- lang: eng
text: "Because of the increasing popularity of machine learning methods, it is becoming
important to understand the impact of learned components on automated decision-making
systems and to guarantee that their consequences are beneficial to society. In
other words, it is necessary to ensure that machine learning is sufficiently trustworthy
to be used in real-world applications. This thesis studies two properties of machine
learning models that are highly desirable for the\r\nsake of reliability: robustness
and fairness. In the first part of the thesis we study the robustness of learning
algorithms to training data corruption. Previous work has shown that machine learning
models are vulnerable to a range\r\nof training set issues, varying from label
noise through systematic biases to worst-case data manipulations. This is an especially
relevant problem from a present perspective, since modern machine learning methods
are particularly data hungry and therefore practitioners often have to rely on
data collected from various external sources, e.g. from the Internet, from app
users or via crowdsourcing. Naturally, such sources vary greatly in the quality
and reliability of the\r\ndata they provide. With these considerations in mind,
we study the problem of designing machine learning algorithms that are robust
to corruptions in data coming from multiple sources. We show that, in contrast
to the case of a single dataset with outliers, successful learning within this
model is possible both theoretically and practically, even under worst-case data
corruptions. The second part of this thesis deals with fairness-aware machine
learning. There are multiple areas where machine learning models have shown promising
results, but where careful considerations are required, in order to avoid discrimanative
decisions taken by such learned components. Ensuring fairness can be particularly
challenging, because real-world training datasets are expected to contain various
forms of historical bias that may affect the learning process. In this thesis
we show that data corruption can indeed render the problem of achieving fairness
impossible, by tightly characterizing the theoretical limits of fair learning
under worst-case data manipulations. However, assuming access to clean data, we
also show how fairness-aware learning can be made practical in contexts beyond
binary classification, in particular in the challenging learning to rank setting."
alternative_title:
- ISTA Thesis
article_processing_charge: No
author:
- first_name: Nikola H
full_name: Konstantinov, Nikola H
id: 4B9D76E4-F248-11E8-B48F-1D18A9856A87
last_name: Konstantinov
orcid: 0009-0009-5204-7621
citation:
ama: Konstantinov NH. Robustness and fairness in machine learning. 2022. doi:10.15479/at:ista:10799
apa: Konstantinov, N. H. (2022). Robustness and fairness in machine learning.
Institute of Science and Technology Austria. https://doi.org/10.15479/at:ista:10799
chicago: Konstantinov, Nikola H. “Robustness and Fairness in Machine Learning.”
Institute of Science and Technology Austria, 2022. https://doi.org/10.15479/at:ista:10799.
ieee: N. H. Konstantinov, “Robustness and fairness in machine learning,” Institute
of Science and Technology Austria, 2022.
ista: Konstantinov NH. 2022. Robustness and fairness in machine learning. Institute
of Science and Technology Austria.
mla: Konstantinov, Nikola H. Robustness and Fairness in Machine Learning.
Institute of Science and Technology Austria, 2022, doi:10.15479/at:ista:10799.
short: N.H. Konstantinov, Robustness and Fairness in Machine Learning, Institute
of Science and Technology Austria, 2022.
corr_author: '1'
date_created: 2022-02-28T13:03:49Z
date_published: 2022-03-08T00:00:00Z
date_updated: 2026-04-07T14:19:48Z
day: '08'
ddc:
- '000'
degree_awarded: PhD
department:
- _id: GradSch
- _id: ChLa
doi: 10.15479/at:ista:10799
ec_funded: 1
file:
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has_accepted_license: '1'
keyword:
- robustness
- fairness
- machine learning
- PAC learning
- adversarial learning
language:
- iso: eng
month: '03'
oa: 1
oa_version: Published Version
page: '176'
project:
- _id: 2564DBCA-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '665385'
name: International IST Doctoral Program
publication_identifier:
isbn:
- 978-3-99078-015-2
issn:
- 2663-337X
publication_status: published
publisher: Institute of Science and Technology Austria
related_material:
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relation: part_of_dissertation
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status: public
- id: '8724'
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status: public
status: public
supervisor:
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
title: Robustness and fairness in machine learning
type: dissertation
user_id: ba8df636-2132-11f1-aed0-ed93e2281fdd
year: '2022'
...
---
_id: '10802'
abstract:
- lang: eng
text: "Addressing fairness concerns about machine learning models is a crucial step
towards their long-term adoption in real-world automated systems. While many approaches
have been developed for training fair models from data, little is known about
the robustness of these methods to data corruption. In this work we consider fairness-aware
learning under worst-case data manipulations. We show that an adversary can in
some situations force any learner to return an overly biased classifier, regardless
of the sample size and with or without degrading\r\naccuracy, and that the strength
of the excess bias increases for learning problems with underrepresented protected
groups in the data. We also prove that our hardness results are tight up to constant
factors. To this end, we study two natural learning algorithms that optimize for
both accuracy and fairness and show that these algorithms enjoy guarantees that
are order-optimal in terms of the corruption ratio and the protected groups frequencies
in the large data\r\nlimit."
acknowledgement: The authors thank Eugenia Iofinova and Bernd Prach for providing
feedback on early versions of this paper. This publication was made possible by
an ETH AI Center postdoctoral fellowship to Nikola Konstantinov.
article_processing_charge: No
article_type: original
arxiv: 1
author:
- 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: 0000-0002-4561-241X
citation:
ama: Konstantinov NH, Lampert C. Fairness-aware PAC learning from corrupted data.
Journal of Machine Learning Research. 2022;23:1-60.
apa: Konstantinov, N. H., & Lampert, C. (2022). Fairness-aware PAC learning
from corrupted data. Journal of Machine Learning Research. ML Research
Press.
chicago: Konstantinov, Nikola H, and Christoph Lampert. “Fairness-Aware PAC Learning
from Corrupted Data.” Journal of Machine Learning Research. ML Research
Press, 2022.
ieee: N. H. Konstantinov and C. Lampert, “Fairness-aware PAC learning from corrupted
data,” Journal of Machine Learning Research, vol. 23. ML Research Press,
pp. 1–60, 2022.
ista: Konstantinov NH, Lampert C. 2022. Fairness-aware PAC learning from corrupted
data. Journal of Machine Learning Research. 23, 1–60.
mla: Konstantinov, Nikola H., and Christoph Lampert. “Fairness-Aware PAC Learning
from Corrupted Data.” Journal of Machine Learning Research, vol. 23, ML
Research Press, 2022, pp. 1–60.
short: N.H. Konstantinov, C. Lampert, Journal of Machine Learning Research 23 (2022)
1–60.
corr_author: '1'
date_created: 2022-02-28T14:05:42Z
date_published: 2022-05-01T00:00:00Z
date_updated: 2026-04-07T14:19:48Z
day: '01'
ddc:
- '004'
department:
- _id: ChLa
external_id:
arxiv:
- '2102.06004'
file:
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checksum: 9cac897b54a0ddf3a553a2c33e88cfda
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creator: kschuh
date_created: 2022-07-12T15:08:28Z
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file_id: '11570'
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file_size: 551862
relation: main_file
success: 1
file_date_updated: 2022-07-12T15:08:28Z
has_accepted_license: '1'
intvolume: ' 23'
keyword:
- Fairness
- robustness
- data poisoning
- trustworthy machine learning
- PAC learning
language:
- iso: eng
month: '05'
oa: 1
oa_version: Published Version
page: 1-60
publication: Journal of Machine Learning Research
publication_identifier:
eissn:
- 1533-7928
issn:
- 1532-4435
publication_status: published
publisher: ML Research Press
quality_controlled: '1'
related_material:
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scopus_import: '1'
status: public
title: Fairness-aware PAC learning from corrupted 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
volume: 23
year: '2022'
...
---
OA_place: publisher
_id: '11362'
abstract:
- lang: eng
text: "Deep learning has enabled breakthroughs in challenging computing problems
and has emerged as the standard problem-solving tool for computer vision and natural
language processing tasks.\r\nOne exception to this trend is safety-critical tasks
where robustness and resilience requirements contradict the black-box nature of
neural networks. \r\nTo deploy deep learning methods for these tasks, it is vital
to provide guarantees on neural network agents' safety and robustness criteria.
\r\nThis can be achieved by developing formal verification methods to verify the
safety and robustness properties of neural networks.\r\n\r\nOur goal is to design,
develop and assess safety verification methods for neural networks to improve
their reliability and trustworthiness in real-world applications.\r\nThis thesis
establishes techniques for the verification of compressed and adversarially trained
models as well as the design of novel neural networks for verifiably safe decision-making.\r\n\r\nFirst,
we establish the problem of verifying quantized neural networks. Quantization
is a technique that trades numerical precision for the computational efficiency
of running a neural network and is widely adopted in industry.\r\nWe show that
neglecting the reduced precision when verifying a neural network can lead to wrong
conclusions about the robustness and safety of the network, highlighting that
novel techniques for quantized network verification are necessary. We introduce
several bit-exact verification methods explicitly designed for quantized neural
networks and experimentally confirm on realistic networks that the network's robustness
and other formal properties are affected by the quantization.\r\n\r\nFurthermore,
we perform a case study providing evidence that adversarial training, a standard
technique for making neural networks more robust, has detrimental effects on the
network's performance. This robustness-accuracy tradeoff has been studied before
regarding the accuracy obtained on classification datasets where each data point
is independent of all other data points. On the other hand, we investigate the
tradeoff empirically in robot learning settings where a both, a high accuracy
and a high robustness, are desirable.\r\nOur results suggest that the negative
side-effects of adversarial training outweigh its robustness benefits in practice.\r\n\r\nFinally,
we consider the problem of verifying safety when running a Bayesian neural network
policy in a feedback loop with systems over the infinite time horizon. Bayesian
neural networks are probabilistic models for learning uncertainties in the data
and are therefore often used on robotic and healthcare applications where data
is inherently stochastic.\r\nWe introduce a method for recalibrating Bayesian
neural networks so that they yield probability distributions over safe decisions
only.\r\nOur method learns a safety certificate that guarantees safety over the
infinite time horizon to determine which decisions are safe in every possible
state of the system.\r\nWe demonstrate the effectiveness of our approach on a
series of reinforcement learning benchmarks."
alternative_title:
- ISTA Thesis
article_processing_charge: No
author:
- first_name: Mathias
full_name: Lechner, Mathias
id: 3DC22916-F248-11E8-B48F-1D18A9856A87
last_name: Lechner
citation:
ama: Lechner M. Learning verifiable representations. 2022. doi:10.15479/at:ista:11362
apa: Lechner, M. (2022). Learning verifiable representations. Institute of
Science and Technology Austria. https://doi.org/10.15479/at:ista:11362
chicago: Lechner, Mathias. “Learning Verifiable Representations.” Institute of Science
and Technology Austria, 2022. https://doi.org/10.15479/at:ista:11362.
ieee: M. Lechner, “Learning verifiable representations,” Institute of Science and
Technology Austria, 2022.
ista: Lechner M. 2022. Learning verifiable representations. Institute of Science
and Technology Austria.
mla: Lechner, Mathias. Learning Verifiable Representations. Institute of
Science and Technology Austria, 2022, doi:10.15479/at:ista:11362.
short: M. Lechner, Learning Verifiable Representations, Institute of Science and
Technology Austria, 2022.
corr_author: '1'
date_created: 2022-05-12T07:14:01Z
date_published: 2022-05-12T00:00:00Z
date_updated: 2026-08-19T09:28:05Z
day: '12'
ddc:
- '004'
degree_awarded: PhD
department:
- _id: GradSch
- _id: ToHe
doi: 10.15479/at:ista:11362
ec_funded: 1
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creator: mlechner
date_created: 2022-05-13T12:33:26Z
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content_type: application/pdf
creator: mlechner
date_created: 2022-05-16T08:02:28Z
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file_id: '11382'
file_name: thesis_main-a2.pdf
file_size: 2732536
relation: main_file
file_date_updated: 2022-05-17T15:19:39Z
fulldoi: https://doi.org/10.15479/at:ista:11362
has_accepted_license: '1'
keyword:
- neural networks
- verification
- machine learning
language:
- iso: eng
month: '05'
oa: 1
oa_version: Published Version
page: '124'
project:
- _id: 25F42A32-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: Z211
name: Formal methods for the design and analysis of complex systems
- _id: 62781420-2b32-11ec-9570-8d9b63373d4d
call_identifier: H2020
grant_number: '101020093'
name: Vigilant Algorithmic Monitoring of Software
publication_identifier:
isbn:
- 978-3-99078-017-6
publication_status: published
publisher: Institute of Science and Technology Austria
related_material:
record:
- id: '11366'
relation: part_of_dissertation
status: public
- id: '7808'
relation: part_of_dissertation
status: public
- id: '10666'
relation: part_of_dissertation
status: public
- id: '10667'
relation: part_of_dissertation
status: public
- id: '10665'
relation: part_of_dissertation
status: public
status: public
supervisor:
- first_name: Thomas A
full_name: Henzinger, Thomas A
id: 40876CD8-F248-11E8-B48F-1D18A9856A87
last_name: Henzinger
orcid: 0000-0002-2985-7724
title: Learning verifiable representations
tmp:
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legal_code_url: https://creativecommons.org/licenses/by-nd/4.0/legalcode
name: Creative Commons Attribution-NoDerivatives 4.0 International (CC BY-ND 4.0)
short: CC BY-ND (4.0)
type: dissertation
user_id: ba8df636-2132-11f1-aed0-ed93e2281fdd
year: '2022'
...
---
_id: '9756'
abstract:
- lang: eng
text: High-resolution visualization and quantification of membrane proteins contribute
to the understanding of their functions and the roles they play in physiological
and pathological conditions. Sodium dodecyl sulfate-digested freeze-fracture replica
labeling (SDS-FRL) is a powerful electron microscopy method to study quantitatively
the two-dimensional distribution of transmembrane proteins and their tightly associated
proteins. During treatment with SDS, intracellular organelles and proteins not
anchored to the replica are dissolved, whereas integral membrane proteins captured
and stabilized by carbon/platinum deposition remain on the replica. Their intra-
and extracellular domains become exposed on the surface of the replica, facilitating
the accessibility of antibodies and, therefore, providing higher labeling efficiency
than those obtained with other immunoelectron microscopy techniques. In this chapter,
we describe the protocols of SDS-FRL adapted for mammalian brain samples, and
optimization of the SDS treatment to increase the labeling efficiency for quantification
of Cav2.1, the alpha subunit of P/Q-type voltage-dependent calcium channels utilizing
deep learning algorithms.
acknowledgement: This work was supported by the European Union (European Research
Council Advanced grant no. 694539 and Human Brain Project Ref. 720270 to R. S.)
and the Austrian Academy of Sciences (DOC fellowship to D.K.).
alternative_title:
- Neuromethods
article_processing_charge: No
author:
- first_name: Walter
full_name: Kaufmann, Walter
id: 3F99E422-F248-11E8-B48F-1D18A9856A87
last_name: Kaufmann
orcid: 0000-0001-9735-5315
- first_name: David
full_name: Kleindienst, David
id: 42E121A4-F248-11E8-B48F-1D18A9856A87
last_name: Kleindienst
- first_name: Harumi
full_name: Harada, Harumi
id: 2E55CDF2-F248-11E8-B48F-1D18A9856A87
last_name: Harada
orcid: 0000-0001-7429-7896
- first_name: Ryuichi
full_name: Shigemoto, Ryuichi
id: 499F3ABC-F248-11E8-B48F-1D18A9856A87
last_name: Shigemoto
orcid: 0000-0001-8761-9444
citation:
ama: 'Kaufmann W, Kleindienst D, Harada H, Shigemoto R. High-Resolution localization
and quantitation of membrane proteins by SDS-digested freeze-fracture replica
labeling (SDS-FRL). In: Receptor and Ion Channel Detection in the Brain.
Vol 169. Neuromethods. New York: Humana Press; 2021:267-283. doi:10.1007/978-1-0716-1522-5_19'
apa: 'Kaufmann, W., Kleindienst, D., Harada, H., & Shigemoto, R. (2021). High-Resolution
localization and quantitation of membrane proteins by SDS-digested freeze-fracture
replica labeling (SDS-FRL). In Receptor and Ion Channel Detection in the Brain
(Vol. 169, pp. 267–283). New York: Humana Press. https://doi.org/10.1007/978-1-0716-1522-5_19'
chicago: 'Kaufmann, Walter, David Kleindienst, Harumi Harada, and Ryuichi Shigemoto.
“High-Resolution Localization and Quantitation of Membrane Proteins by SDS-Digested
Freeze-Fracture Replica Labeling (SDS-FRL).” In Receptor and Ion Channel Detection
in the Brain, 169:267–83. Neuromethods. New York: Humana Press, 2021. https://doi.org/10.1007/978-1-0716-1522-5_19.'
ieee: 'W. Kaufmann, D. Kleindienst, H. Harada, and R. Shigemoto, “High-Resolution
localization and quantitation of membrane proteins by SDS-digested freeze-fracture
replica labeling (SDS-FRL),” in Receptor and Ion Channel Detection in the Brain,
vol. 169, New York: Humana Press, 2021, pp. 267–283.'
ista: 'Kaufmann W, Kleindienst D, Harada H, Shigemoto R. 2021.High-Resolution localization
and quantitation of membrane proteins by SDS-digested freeze-fracture replica
labeling (SDS-FRL). In: Receptor and Ion Channel Detection in the Brain. Neuromethods,
vol. 169, 267–283.'
mla: Kaufmann, Walter, et al. “High-Resolution Localization and Quantitation of
Membrane Proteins by SDS-Digested Freeze-Fracture Replica Labeling (SDS-FRL).”
Receptor and Ion Channel Detection in the Brain, vol. 169, Humana Press,
2021, pp. 267–83, doi:10.1007/978-1-0716-1522-5_19.
short: W. Kaufmann, D. Kleindienst, H. Harada, R. Shigemoto, in:, Receptor and Ion
Channel Detection in the Brain, Humana Press, New York, 2021, pp. 267–283.
corr_author: '1'
das_tickbox: '1'
date_created: 2021-07-30T09:34:56Z
date_published: 2021-07-27T00:00:00Z
date_updated: 2026-09-10T22:31:05Z
day: '27'
ddc:
- '573'
department:
- _id: RySh
- _id: EM-Fac
doi: 10.1007/978-1-0716-1522-5_19
ec_funded: 1
fulldoi: https://doi.org/10.1007/978-1-0716-1522-5_19
has_accepted_license: '1'
intvolume: ' 169'
keyword:
- 'Freeze-fracture replica: Deep learning'
- Immunogold labeling
- Integral membrane protein
- Electron microscopy
language:
- iso: eng
month: '07'
oa_version: None
page: 267-283
place: New York
project:
- _id: 25CA28EA-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '694539'
name: 'In situ analysis of single channel subunit composition in neurons: physiological
implication in synaptic plasticity and behaviour'
- _id: 25CBA828-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '720270'
name: Human Brain Project Specific Grant Agreement 1
publication: Receptor and Ion Channel Detection in the Brain
publication_identifier:
eisbn:
- '9781071615225'
isbn:
- '9781071615218'
publication_status: published
publisher: Humana Press
quality_controlled: '1'
related_material:
record:
- id: '9562'
relation: dissertation_contains
status: public
scopus_import: '1'
series_title: Neuromethods
status: public
title: High-Resolution localization and quantitation of membrane proteins by SDS-digested
freeze-fracture replica labeling (SDS-FRL)
type: book_chapter
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 169
year: '2021'
...
---
_id: '11627'
abstract:
- lang: eng
text: 'For a solar-like star, the surface rotation evolves with time, allowing in
principle to estimate the age of a star from its surface rotation period. Here
we are interested in measuring surface rotation periods of solar-like stars observed
by the NASA mission Kepler. Different methods have been developed to track rotation
signals in Kepler photometric light curves: time-frequency analysis based on wavelet
techniques, autocorrelation and composite spectrum. We use the learning abilities
of random forest classifiers to take decisions during two crucial steps of the
analysis. First, given some input parameters, we discriminate the considered Kepler
targets between rotating MS stars, non-rotating MS stars, red giants, binaries
and pulsators. We then use a second classifier only on the MS rotating targets
to decide the best data analysis treatment.'
article_number: '1906.09609'
article_processing_charge: No
arxiv: 1
author:
- first_name: S. N.
full_name: Breton, S. N.
last_name: Breton
- 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: A. R. G.
full_name: Santos, A. R. G.
last_name: Santos
- first_name: A. Le
full_name: Saux, A. Le
last_name: Saux
- first_name: S.
full_name: Mathur, S.
last_name: Mathur
- first_name: P. L.
full_name: Palle, P. L.
last_name: Palle
- first_name: R. A.
full_name: Garcia, R. A.
last_name: Garcia
citation:
ama: Breton SN, Bugnet LA, Santos ARG, et al. Determining surface rotation periods
of solar-like stars observed by the Kepler mission using machine learning techniques.
arXiv. doi:10.48550/arXiv.1906.09609
apa: Breton, S. N., Bugnet, L. A., Santos, A. R. G., Saux, A. L., Mathur, S., Palle,
P. L., & Garcia, R. A. (n.d.). Determining surface rotation periods of solar-like
stars observed by the Kepler mission using machine learning techniques. arXiv.
https://doi.org/10.48550/arXiv.1906.09609
chicago: Breton, S. N., Lisa Annabelle Bugnet, A. R. G. Santos, A. Le Saux, S. Mathur,
P. L. Palle, and R. A. Garcia. “Determining Surface Rotation Periods of Solar-like
Stars Observed by the Kepler Mission Using Machine Learning Techniques.” ArXiv,
n.d. https://doi.org/10.48550/arXiv.1906.09609.
ieee: S. N. Breton et al., “Determining surface rotation periods of solar-like
stars observed by the Kepler mission using machine learning techniques,” arXiv.
.
ista: Breton SN, Bugnet LA, Santos ARG, Saux AL, Mathur S, Palle PL, Garcia RA.
Determining surface rotation periods of solar-like stars observed by the Kepler
mission using machine learning techniques. arXiv, 1906.09609.
mla: Breton, S. N., et al. “Determining Surface Rotation Periods of Solar-like Stars
Observed by the Kepler Mission Using Machine Learning Techniques.” ArXiv,
1906.09609, doi:10.48550/arXiv.1906.09609.
short: S.N. Breton, L.A. Bugnet, A.R.G. Santos, A.L. Saux, S. Mathur, P.L. Palle,
R.A. Garcia, ArXiv (n.d.).
date_created: 2022-07-20T11:18:53Z
date_published: 2019-06-23T00:00:00Z
date_updated: 2022-08-22T08:16:53Z
day: '23'
doi: 10.48550/arXiv.1906.09609
extern: '1'
external_id:
arxiv:
- '1906.09609'
fulldoi: https://doi.org/10.48550/arXiv.1906.09609
keyword:
- asteroseismology
- rotation
- solar-like stars
- kepler
- machine learning
- random forest
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/1906.09609
month: '06'
oa: 1
oa_version: Preprint
publication: arXiv
publication_status: submitted
status: public
title: Determining surface rotation periods of solar-like stars observed by the Kepler
mission using machine learning techniques
type: preprint
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2019'
...
---
_id: '11630'
abstract:
- lang: eng
text: 'The second mission of NASA’s Kepler satellite, K2, has collected hundreds
of thousands of lightcurves for stars close to the ecliptic plane. This new sample
could increase the number of known pulsating stars and then improve our understanding
of those stars. For the moment only a few stars have been properly classified
and published. In this work, we present a method to automaticly classify K2 pulsating
stars using a Machine Learning technique called Random Forest. The objective is
to sort out the stars in four classes: red giant (RG), main-sequence Solar-like
stars (SL), classical pulsators (PULS) and Other. To do this we use the effective
temperatures and the luminosities of the stars as well as the FliPer features,
that measures the amount of power contained in the power spectral density. The
classifier now retrieves the right classification for more than 80% of the stars.'
article_number: '1906.09611'
article_processing_charge: No
arxiv: 1
author:
- first_name: A. Le
full_name: Saux, A. Le
last_name: Saux
- 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: S.
full_name: Mathur, S.
last_name: Mathur
- first_name: S. N.
full_name: Breton, S. N.
last_name: Breton
- first_name: R. A.
full_name: Garcia, R. A.
last_name: Garcia
citation:
ama: Saux AL, Bugnet LA, Mathur S, Breton SN, Garcia RA. Automatic classification
of K2 pulsating stars using machine learning techniques. arXiv. doi:10.48550/arXiv.1906.09611
apa: Saux, A. L., Bugnet, L. A., Mathur, S., Breton, S. N., & Garcia, R. A.
(n.d.). Automatic classification of K2 pulsating stars using machine learning
techniques. arXiv. https://doi.org/10.48550/arXiv.1906.09611
chicago: Saux, A. Le, Lisa Annabelle Bugnet, S. Mathur, S. N. Breton, and R. A.
Garcia. “Automatic Classification of K2 Pulsating Stars Using Machine Learning
Techniques.” ArXiv, n.d. https://doi.org/10.48550/arXiv.1906.09611.
ieee: A. L. Saux, L. A. Bugnet, S. Mathur, S. N. Breton, and R. A. Garcia, “Automatic
classification of K2 pulsating stars using machine learning techniques,” arXiv.
.
ista: Saux AL, Bugnet LA, Mathur S, Breton SN, Garcia RA. Automatic classification
of K2 pulsating stars using machine learning techniques. arXiv, 1906.09611.
mla: Saux, A. Le, et al. “Automatic Classification of K2 Pulsating Stars Using Machine
Learning Techniques.” ArXiv, 1906.09611, doi:10.48550/arXiv.1906.09611.
short: A.L. Saux, L.A. Bugnet, S. Mathur, S.N. Breton, R.A. Garcia, ArXiv (n.d.).
date_created: 2022-07-21T06:57:10Z
date_published: 2019-06-23T00:00:00Z
date_updated: 2022-08-22T08:20:29Z
day: '23'
doi: 10.48550/arXiv.1906.09611
extern: '1'
external_id:
arxiv:
- '1906.09611'
fulldoi: https://doi.org/10.48550/arXiv.1906.09611
keyword:
- asteroseismology - methods
- data analysis - thecniques
- machine learning - stars
- oscillations
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.1906.09611
month: '06'
oa: 1
oa_version: Preprint
publication: arXiv
publication_status: submitted
status: public
title: Automatic classification of K2 pulsating stars using machine learning techniques
type: preprint
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2019'
...