---
_id: '14954'
abstract:
- lang: eng
text: "When domain knowledge is limited and experimentation is restricted by ethical,
financial, or time constraints, practitioners turn to observational causal discovery
methods to recover the causal structure, exploiting the statistical properties
of their data. Because causal discovery without further assumptions is an ill-posed
problem, each algorithm comes with its own set of\r\nusually untestable assumptions,
some of which are hard to meet in real datasets. Motivated by these considerations,
this paper extensively benchmarks the empirical performance of recent causal discovery
methods on observational i.i.d. data generated under different background conditions,
allowing for violations of the critical assumptions required by each selected
approach. Our experimental findings show that score matching-based methods demonstrate\r\nsurprising
performance in the false positive and false negative rate of the inferred graph
in these challenging scenarios, and we provide theoretical insights into their
performance. This work is also the first effort to benchmark the stability of
causal discovery algorithms with respect to the values of their hyperparameters.
Finally, we hope this paper will set a new standard for the evaluation of causal
discovery methods and can serve as an accessible entry point for practitioners
interested in the field, highlighting the empirical implications of different
algorithm choices."
acknowledgement: "We thank Kun Zhang and Carl-Johann Simon-Gabriel for the insightful
discussions. This work\r\nhas been supported by AFOSR, grant n. FA8655-20-1-7035.
FM is supported by Programma\r\nOperativo Nazionale ricerca e innovazione 2014-2020.
FM partially contributed to this work during an internship at Amazon Web Services
with FL. FL partially contributed while at AWS."
article_number: '2310.13387'
article_processing_charge: No
arxiv: 1
author:
- first_name: Francesco
full_name: Montagna, Francesco
last_name: Montagna
- first_name: Atalanti A.
full_name: Mastakouri, Atalanti A.
last_name: Mastakouri
- first_name: Elias
full_name: Eulig, Elias
last_name: Eulig
- first_name: Nicoletta
full_name: Noceti, Nicoletta
last_name: Noceti
- first_name: Lorenzo
full_name: Rosasco, Lorenzo
last_name: Rosasco
- first_name: Dominik
full_name: Janzing, Dominik
last_name: Janzing
- first_name: Bryon
full_name: Aragam, Bryon
last_name: Aragam
- first_name: Francesco
full_name: Locatello, Francesco
id: 26cfd52f-2483-11ee-8040-88983bcc06d4
last_name: Locatello
orcid: 0000-0002-4850-0683
citation:
ama: Montagna F, Mastakouri AA, Eulig E, et al. Assumption violations in causal
discovery and the robustness of score matching. arXiv. doi:10.48550/arXiv.2310.13387
apa: Montagna, F., Mastakouri, A. A., Eulig, E., Noceti, N., Rosasco, L., Janzing,
D., … Locatello, F. (n.d.). Assumption violations in causal discovery and the
robustness of score matching. arXiv. https://doi.org/10.48550/arXiv.2310.13387
chicago: Montagna, Francesco, Atalanti A. Mastakouri, Elias Eulig, Nicoletta Noceti,
Lorenzo Rosasco, Dominik Janzing, Bryon Aragam, and Francesco Locatello. “Assumption
Violations in Causal Discovery and the Robustness of Score Matching.” ArXiv,
n.d. https://doi.org/10.48550/arXiv.2310.13387.
ieee: F. Montagna et al., “Assumption violations in causal discovery and
the robustness of score matching,” arXiv. .
ista: Montagna F, Mastakouri AA, Eulig E, Noceti N, Rosasco L, Janzing D, Aragam
B, Locatello F. Assumption violations in causal discovery and the robustness of
score matching. arXiv, 2310.13387.
mla: Montagna, Francesco, et al. “Assumption Violations in Causal Discovery and
the Robustness of Score Matching.” ArXiv, 2310.13387, doi:10.48550/arXiv.2310.13387.
short: F. Montagna, A.A. Mastakouri, E. Eulig, N. Noceti, L. Rosasco, D. Janzing,
B. Aragam, F. Locatello, ArXiv (n.d.).
date_created: 2024-02-07T15:11:56Z
date_published: 2023-10-20T00:00:00Z
date_updated: 2024-02-12T09:51:15Z
day: '20'
department:
- _id: FrLo
doi: 10.48550/arXiv.2310.13387
external_id:
arxiv:
- '2310.13387'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2310.13387
month: '10'
oa: 1
oa_version: Preprint
publication: arXiv
publication_status: submitted
status: public
title: Assumption violations in causal discovery and the robustness of score matching
type: preprint
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2023'
...
---
_id: '14958'
abstract:
- lang: eng
text: Causal representation learning (CRL) aims at identifying high-level causal
variables from low-level data, e.g. images. Current methods usually assume that
all causal variables are captured in the high-dimensional observations. In this
work, we focus on learning causal representations from data under partial observability,
i.e., when some of the causal variables are not observed in the measurements,
and the set of masked variables changes across the different samples. We introduce
some initial theoretical results for identifying causal variables under partial
observability by exploiting a sparsity regularizer, focusing in particular on
the linear and piecewise linear mixing function case. We provide a theorem that
allows us to identify the causal variables up to permutation and element-wise
linear transformations in the linear case and a lemma that allows us to identify
causal variables up to linear transformation in the piecewise case. Finally, we
provide a conjecture that would allow us to identify the causal variables up to
permutation and element-wise linear transformations also in the piecewise linear
case. We test the theorem and conjecture on simulated data, showing the effectiveness
of our method.
acknowledgement: "This work was initiated at the Second Bellairs Workshop on Causality
held at the Bellairs Research Institute, January 6–13, 2022; we thank all workshop
participants for providing a stimulating research environment. The research of DX
and SM was supported by the Air Force Office of Scientific Research under award
number FA8655-22-1-7155. Any opinions, findings, and conclusions or recommendations
expressed in this material are those of the author(s) and do not necessarily reflect
the views of the United States Air Force. We also thank SURF for the support in
using the Dutch National Supercomputer Snellius. DY was supported by an Amazon fellowship
and the International Max Planck Research School for Intelligent Systems (IMPRS-IS).
Work done outside of Amazon. SL was supported by an IVADO excellence PhD scholarship
and by Samsung Electronics Co., Ldt. JvK acknowledges support from the German Federal
Ministry of Education and Research (BMBF)\r\nthrough the Tübingen AI Center (FKZ:
01IS18039B).\r\n"
article_number: '54'
article_processing_charge: No
author:
- first_name: Danru
full_name: Xu, Danru
last_name: Xu
- first_name: Dingling
full_name: Yao, Dingling
id: d3e02e50-48a8-11ee-8f62-c108061797fa
last_name: Yao
- first_name: Sebastien
full_name: Lachapelle, Sebastien
last_name: Lachapelle
- first_name: Perouz
full_name: Taslakian, Perouz
last_name: Taslakian
- first_name: Julius
full_name: von Kügelgen, Julius
last_name: von Kügelgen
- first_name: Francesco
full_name: Locatello, Francesco
id: 26cfd52f-2483-11ee-8040-88983bcc06d4
last_name: Locatello
orcid: 0000-0002-4850-0683
- first_name: Sara
full_name: Magliacane, Sara
last_name: Magliacane
citation:
ama: 'Xu D, Yao D, Lachapelle S, et al. A sparsity principle for partially observable
causal representation learning. In: Causal Representation Learning Workshop
at NeurIPS 2023. OpenReview; 2023.'
apa: 'Xu, D., Yao, D., Lachapelle, S., Taslakian, P., von Kügelgen, J., Locatello,
F., & Magliacane, S. (2023). A sparsity principle for partially observable
causal representation learning. In Causal Representation Learning Workshop
at NeurIPS 2023. New Orleans, LA, United States: OpenReview.'
chicago: Xu, Danru, Dingling Yao, Sebastien Lachapelle, Perouz Taslakian, Julius
von Kügelgen, Francesco Locatello, and Sara Magliacane. “A Sparsity Principle
for Partially Observable Causal Representation Learning.” In Causal Representation
Learning Workshop at NeurIPS 2023. OpenReview, 2023.
ieee: D. Xu et al., “A sparsity principle for partially observable causal
representation learning,” in Causal Representation Learning Workshop at NeurIPS
2023, New Orleans, LA, United States, 2023.
ista: 'Xu D, Yao D, Lachapelle S, Taslakian P, von Kügelgen J, Locatello F, Magliacane
S. 2023. A sparsity principle for partially observable causal representation learning.
Causal Representation Learning Workshop at NeurIPS 2023. CRL: Causal Representation
Learning Workshop at NeurIPS, 54.'
mla: Xu, Danru, et al. “A Sparsity Principle for Partially Observable Causal Representation
Learning.” Causal Representation Learning Workshop at NeurIPS 2023, 54,
OpenReview, 2023.
short: D. Xu, D. Yao, S. Lachapelle, P. Taslakian, J. von Kügelgen, F. Locatello,
S. Magliacane, in:, Causal Representation Learning Workshop at NeurIPS 2023, OpenReview,
2023.
conference:
end_date: 2023-12-15
location: New Orleans, LA, United States
name: 'CRL: Causal Representation Learning Workshop at NeurIPS'
start_date: 2023-12-15
date_created: 2024-02-07T15:17:51Z
date_published: 2023-12-05T00:00:00Z
date_updated: 2024-02-13T08:59:27Z
day: '05'
ddc:
- '000'
department:
- _id: FrLo
file:
- access_level: open_access
checksum: 484efc27bda75ed6666044989695d9b6
content_type: application/pdf
creator: dernst
date_created: 2024-02-13T08:50:53Z
date_updated: 2024-02-13T08:50:53Z
file_id: '14982'
file_name: 2023_CRL_Xu.pdf
file_size: 552357
relation: main_file
success: 1
file_date_updated: 2024-02-13T08:50:53Z
has_accepted_license: '1'
language:
- iso: eng
license: https://creativecommons.org/licenses/by/4.0/
main_file_link:
- open_access: '1'
url: https://openreview.net/forum?id=Whr6uobelR
month: '12'
oa: 1
oa_version: Published Version
publication: Causal Representation Learning Workshop at NeurIPS 2023
publication_status: published
publisher: OpenReview
quality_controlled: '1'
status: public
title: A sparsity principle for partially observable causal representation learning
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: '2023'
...
---
_id: '14961'
abstract:
- lang: eng
text: "The use of simulated data in the field of causal discovery is ubiquitous
due to the scarcity of annotated real data. Recently, Reisach et al., 2021 highlighted
the emergence of patterns in simulated linear data, which displays increasing
marginal variance in the casual direction. As an ablation in their experiments,
Montagna et al., 2023 found that similar patterns may emerge in\r\nnonlinear models
for the variance of the score vector $\\nabla \\log p_{\\mathbf{X}}$, and introduced
the ScoreSort algorithm. In this work, we formally define and characterize this
score-sortability pattern of nonlinear additive noise models. We find that it
defines a class of identifiable (bivariate) causal models overlapping with nonlinear
additive noise models. We\r\ntheoretically demonstrate the advantages of ScoreSort
in terms of statistical efficiency compared to prior state-of-the-art score matching-based
methods and empirically show the score-sortability of the most common synthetic
benchmarks in the literature. Our findings remark (1) the lack of diversity in
the data as an important limitation in the evaluation of nonlinear causal discovery
approaches, (2) the importance of thoroughly testing different settings within
a problem class, and (3) the importance of analyzing statistical properties in\r\ncausal
discovery, where research is often limited to defining identifiability conditions
of the model. "
article_number: '2310.14246'
article_processing_charge: No
arxiv: 1
author:
- first_name: Francesco
full_name: Montagna, Francesco
last_name: Montagna
- first_name: Nicoletta
full_name: Noceti, Nicoletta
last_name: Noceti
- first_name: Lorenzo
full_name: Rosasco, Lorenzo
last_name: Rosasco
- first_name: Francesco
full_name: Locatello, Francesco
id: 26cfd52f-2483-11ee-8040-88983bcc06d4
last_name: Locatello
orcid: 0000-0002-4850-0683
citation:
ama: Montagna F, Noceti N, Rosasco L, Locatello F. Shortcuts for causal discovery
of nonlinear models by score matching. arXiv. doi:10.48550/arXiv.2310.14246
apa: Montagna, F., Noceti, N., Rosasco, L., & Locatello, F. (n.d.). Shortcuts
for causal discovery of nonlinear models by score matching. arXiv. https://doi.org/10.48550/arXiv.2310.14246
chicago: Montagna, Francesco, Nicoletta Noceti, Lorenzo Rosasco, and Francesco Locatello.
“Shortcuts for Causal Discovery of Nonlinear Models by Score Matching.” ArXiv,
n.d. https://doi.org/10.48550/arXiv.2310.14246.
ieee: F. Montagna, N. Noceti, L. Rosasco, and F. Locatello, “Shortcuts for causal
discovery of nonlinear models by score matching,” arXiv. .
ista: Montagna F, Noceti N, Rosasco L, Locatello F. Shortcuts for causal discovery
of nonlinear models by score matching. arXiv, 2310.14246.
mla: Montagna, Francesco, et al. “Shortcuts for Causal Discovery of Nonlinear Models
by Score Matching.” ArXiv, 2310.14246, doi:10.48550/arXiv.2310.14246.
short: F. Montagna, N. Noceti, L. Rosasco, F. Locatello, ArXiv (n.d.).
date_created: 2024-02-08T15:31:46Z
date_published: 2023-10-22T00:00:00Z
date_updated: 2024-02-12T10:03:33Z
day: '22'
department:
- _id: FrLo
doi: 10.48550/arXiv.2310.14246
external_id:
arxiv:
- '2310.14246'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2310.14246
month: '10'
oa: 1
oa_version: Preprint
publication: arXiv
publication_status: submitted
status: public
title: Shortcuts for causal discovery of nonlinear models by score matching
type: preprint
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2023'
...
---
_id: '14962'
abstract:
- lang: eng
text: "In this paper, we show that recent advances in video representation learning\r\nand
pre-trained vision-language models allow for substantial improvements in\r\nself-supervised
video object localization. We propose a method that first\r\nlocalizes objects
in videos via a slot attention approach and then assigns text\r\nto the obtained
slots. The latter is achieved by an unsupervised way to read\r\nlocalized semantic
information from the pre-trained CLIP model. The resulting\r\nvideo object localization
is entirely unsupervised apart from the implicit\r\nannotation contained in CLIP,
and it is effectively the first unsupervised\r\napproach that yields good results
on regular video benchmarks."
article_number: '2309.09858'
article_processing_charge: No
arxiv: 1
author:
- first_name: Ke
full_name: Fan, Ke
last_name: Fan
- first_name: Zechen
full_name: Bai, Zechen
last_name: Bai
- first_name: Tianjun
full_name: Xiao, Tianjun
last_name: Xiao
- first_name: Dominik
full_name: Zietlow, Dominik
last_name: Zietlow
- first_name: Max
full_name: Horn, Max
last_name: Horn
- first_name: Zixu
full_name: Zhao, Zixu
last_name: Zhao
- first_name: Carl-Johann Simon-Gabriel
full_name: Carl-Johann Simon-Gabriel, Carl-Johann Simon-Gabriel
last_name: Carl-Johann Simon-Gabriel
- first_name: Mike Zheng
full_name: Shou, Mike Zheng
last_name: Shou
- first_name: Francesco
full_name: Locatello, Francesco
id: 26cfd52f-2483-11ee-8040-88983bcc06d4
last_name: Locatello
orcid: 0000-0002-4850-0683
- first_name: Bernt
full_name: Schiele, Bernt
last_name: Schiele
- first_name: Thomas
full_name: Brox, Thomas
last_name: Brox
- first_name: Zheng
full_name: Zhang, Zheng
last_name: Zhang
- first_name: Yanwei
full_name: Fu, Yanwei
last_name: Fu
- first_name: Tong
full_name: He, Tong
last_name: He
citation:
ama: Fan K, Bai Z, Xiao T, et al. Unsupervised open-vocabulary object localization
in videos. arXiv. doi:10.48550/arXiv.2309.09858
apa: Fan, K., Bai, Z., Xiao, T., Zietlow, D., Horn, M., Zhao, Z., … He, T. (n.d.).
Unsupervised open-vocabulary object localization in videos. arXiv. https://doi.org/10.48550/arXiv.2309.09858
chicago: Fan, Ke, Zechen Bai, Tianjun Xiao, Dominik Zietlow, Max Horn, Zixu Zhao,
Carl-Johann Simon-Gabriel Carl-Johann Simon-Gabriel, et al. “Unsupervised Open-Vocabulary
Object Localization in Videos.” ArXiv, n.d. https://doi.org/10.48550/arXiv.2309.09858.
ieee: K. Fan et al., “Unsupervised open-vocabulary object localization in
videos,” arXiv. .
ista: Fan K, Bai Z, Xiao T, Zietlow D, Horn M, Zhao Z, Carl-Johann Simon-Gabriel
C-JS-G, Shou MZ, Locatello F, Schiele B, Brox T, Zhang Z, Fu Y, He T. Unsupervised
open-vocabulary object localization in videos. arXiv, 2309.09858.
mla: Fan, Ke, et al. “Unsupervised Open-Vocabulary Object Localization in Videos.”
ArXiv, 2309.09858, doi:10.48550/arXiv.2309.09858.
short: K. Fan, Z. Bai, T. Xiao, D. Zietlow, M. Horn, Z. Zhao, C.-J.S.-G. Carl-Johann
Simon-Gabriel, M.Z. Shou, F. Locatello, B. Schiele, T. Brox, Z. Zhang, Y. Fu,
T. He, ArXiv (n.d.).
date_created: 2024-02-08T15:33:39Z
date_published: 2023-09-18T00:00:00Z
date_updated: 2024-02-12T10:12:22Z
day: '18'
department:
- _id: FrLo
doi: 10.48550/arXiv.2309.09858
extern: '1'
external_id:
arxiv:
- '2309.09858'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2309.09858
month: '09'
oa: 1
oa_version: Preprint
publication: arXiv
publication_status: submitted
status: public
title: Unsupervised open-vocabulary object localization in videos
type: preprint
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2023'
...
---
_id: '14963'
abstract:
- lang: eng
text: "Unsupervised object-centric learning methods allow the partitioning of scenes\r\ninto
entities without additional localization information and are excellent\r\ncandidates
for reducing the annotation burden of multiple-object tracking (MOT)\r\npipelines.
Unfortunately, they lack two key properties: objects are often split\r\ninto parts
and are not consistently tracked over time. In fact,\r\nstate-of-the-art models
achieve pixel-level accuracy and temporal consistency\r\nby relying on supervised
object detection with additional ID labels for the\r\nassociation through time.
This paper proposes a video object-centric model for\r\nMOT. It consists of an
index-merge module that adapts the object-centric slots\r\ninto detection outputs
and an object memory module that builds complete object\r\nprototypes to handle
occlusions. Benefited from object-centric learning, we\r\nonly require sparse
detection labels (0%-6.25%) for object localization and\r\nfeature binding. Relying
on our self-supervised\r\nExpectation-Maximization-inspired loss for object association,
our approach\r\nrequires no ID labels. Our experiments significantly narrow the
gap between the\r\nexisting object-centric model and the fully supervised state-of-the-art
and\r\noutperform several unsupervised trackers."
article_number: '2309.00233'
article_processing_charge: No
arxiv: 1
author:
- first_name: Zixu
full_name: Zhao, Zixu
last_name: Zhao
- first_name: Jiaze
full_name: Wang, Jiaze
last_name: Wang
- first_name: Max
full_name: Horn, Max
last_name: Horn
- first_name: Yizhuo
full_name: Ding, Yizhuo
last_name: Ding
- first_name: Tong
full_name: He, Tong
last_name: He
- first_name: Zechen
full_name: Bai, Zechen
last_name: Bai
- first_name: Dominik
full_name: Zietlow, Dominik
last_name: Zietlow
- first_name: Carl-Johann Simon-Gabriel
full_name: Carl-Johann Simon-Gabriel, Carl-Johann Simon-Gabriel
last_name: Carl-Johann Simon-Gabriel
- first_name: Bing
full_name: Shuai, Bing
last_name: Shuai
- first_name: Zhuowen
full_name: Tu, Zhuowen
last_name: Tu
- first_name: Thomas
full_name: Brox, Thomas
last_name: Brox
- first_name: Bernt
full_name: Schiele, Bernt
last_name: Schiele
- first_name: Yanwei
full_name: Fu, Yanwei
last_name: Fu
- first_name: Francesco
full_name: Locatello, Francesco
id: 26cfd52f-2483-11ee-8040-88983bcc06d4
last_name: Locatello
orcid: 0000-0002-4850-0683
- first_name: Zheng
full_name: Zhang, Zheng
last_name: Zhang
- first_name: Tianjun
full_name: Xiao, Tianjun
last_name: Xiao
citation:
ama: Zhao Z, Wang J, Horn M, et al. Object-centric multiple object tracking. arXiv.
doi:10.48550/arXiv.2309.00233
apa: Zhao, Z., Wang, J., Horn, M., Ding, Y., He, T., Bai, Z., … Xiao, T. (n.d.).
Object-centric multiple object tracking. arXiv. https://doi.org/10.48550/arXiv.2309.00233
chicago: Zhao, Zixu, Jiaze Wang, Max Horn, Yizhuo Ding, Tong He, Zechen Bai, Dominik
Zietlow, et al. “Object-Centric Multiple Object Tracking.” ArXiv, n.d.
https://doi.org/10.48550/arXiv.2309.00233.
ieee: Z. Zhao et al., “Object-centric multiple object tracking,” arXiv.
.
ista: Zhao Z, Wang J, Horn M, Ding Y, He T, Bai Z, Zietlow D, Carl-Johann Simon-Gabriel
C-JS-G, Shuai B, Tu Z, Brox T, Schiele B, Fu Y, Locatello F, Zhang Z, Xiao T.
Object-centric multiple object tracking. arXiv, 2309.00233.
mla: Zhao, Zixu, et al. “Object-Centric Multiple Object Tracking.” ArXiv,
2309.00233, doi:10.48550/arXiv.2309.00233.
short: Z. Zhao, J. Wang, M. Horn, Y. Ding, T. He, Z. Bai, D. Zietlow, C.-J.S.-G.
Carl-Johann Simon-Gabriel, B. Shuai, Z. Tu, T. Brox, B. Schiele, Y. Fu, F. Locatello,
Z. Zhang, T. Xiao, ArXiv (n.d.).
date_created: 2024-02-08T15:34:43Z
date_published: 2023-09-01T00:00:00Z
date_updated: 2024-02-12T10:16:21Z
day: '01'
department:
- _id: FrLo
doi: 10.48550/arXiv.2309.00233
extern: '1'
external_id:
arxiv:
- '2309.00233'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: ' https://doi.org/10.48550/arXiv.2309.00233'
month: '09'
oa: 1
oa_version: Preprint
publication: arXiv
publication_status: submitted
status: public
title: Object-centric multiple object tracking
type: preprint
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2023'
...
---
_id: '14985'
abstract:
- lang: eng
text: Lead sulfide (PbS) presents large potential in thermoelectric application
due to its earth-abundant S element. However, its inferior average ZT (ZTave)
value makes PbS less competitive with its analogs PbTe and PbSe. To promote its
thermoelectric performance, this study implements strategies of continuous Se
alloying and Cu interstitial doping to synergistically tune thermal and electrical
transport properties in n-type PbS. First, the lattice parameter of 5.93 Å in
PbS is linearly expanded to 6.03 Å in PbS0.5Se0.5 with increasing Se alloying
content. This expanded lattice in Se-alloyed PbS not only intensifies phonon scattering
but also facilitates the formation of Cu interstitials. Based on the PbS0.6Se0.4
content with the minimal lattice thermal conductivity, Cu interstitials are introduced
to improve the electron density, thus boosting the peak power factor, from 3.88 μW cm−1 K−2
in PbS0.6Se0.4 to 20.58 μW cm−1 K−2 in PbS0.6Se0.4−1%Cu. Meanwhile, the lattice
thermal conductivity in PbS0.6Se0.4−x%Cu (x = 0–2) is further suppressed due to
the strong strain field caused by Cu interstitials. Finally, with the lowered
thermal conductivity and high electrical transport properties, a peak ZT ~1.1
and ZTave ~0.82 can be achieved in PbS0.6Se0.4 − 1%Cu at 300–773K, which outperforms
previously reported n-type PbS.
acknowledgement: 'The authors would like to acknowledge the strong supportof microstructure
observation from Center for HighPressure Science and Technology Advanced Research(HPSTAR).
We acknowledge the financial support fromthe National Natural Science Foundation of China:52172236,
the Fundamental Research Funds for theCentral Universities: xtr042021007, Top Young
TalentsProgramme of Xi''an Jiaotong University and NationalScience Fund for Distinguished
Young Scholars: 51925101.'
article_processing_charge: Yes
article_type: original
author:
- first_name: Zhengtao
full_name: Liu, Zhengtao
last_name: Liu
- first_name: Tao
full_name: Hong, Tao
last_name: Hong
- first_name: Liqing
full_name: Xu, Liqing
last_name: Xu
- first_name: Sining
full_name: Wang, Sining
last_name: Wang
- first_name: Xiang
full_name: Gao, Xiang
last_name: Gao
- first_name: Cheng
full_name: Chang, Cheng
id: 9E331C2E-9F27-11E9-AE48-5033E6697425
last_name: Chang
orcid: 0000-0002-9515-4277
- first_name: Xiangdong
full_name: Ding, Xiangdong
last_name: Ding
- first_name: Yu
full_name: Xiao, Yu
last_name: Xiao
- first_name: Li‐Dong
full_name: Zhao, Li‐Dong
last_name: Zhao
citation:
ama: Liu Z, Hong T, Xu L, et al. Lattice expansion enables interstitial doping to
achieve a high average ZT in n‐type PbS. Interdisciplinary Materials. 2023;2(1):161-170.
doi:10.1002/idm2.12056
apa: Liu, Z., Hong, T., Xu, L., Wang, S., Gao, X., Chang, C., … Zhao, L. (2023).
Lattice expansion enables interstitial doping to achieve a high average ZT in
n‐type PbS. Interdisciplinary Materials. Wiley. https://doi.org/10.1002/idm2.12056
chicago: Liu, Zhengtao, Tao Hong, Liqing Xu, Sining Wang, Xiang Gao, Cheng Chang,
Xiangdong Ding, Yu Xiao, and Li‐Dong Zhao. “Lattice Expansion Enables Interstitial
Doping to Achieve a High Average ZT in N‐type PbS.” Interdisciplinary Materials.
Wiley, 2023. https://doi.org/10.1002/idm2.12056.
ieee: Z. Liu et al., “Lattice expansion enables interstitial doping to achieve
a high average ZT in n‐type PbS,” Interdisciplinary Materials, vol. 2,
no. 1. Wiley, pp. 161–170, 2023.
ista: Liu Z, Hong T, Xu L, Wang S, Gao X, Chang C, Ding X, Xiao Y, Zhao L. 2023.
Lattice expansion enables interstitial doping to achieve a high average ZT in
n‐type PbS. Interdisciplinary Materials. 2(1), 161–170.
mla: Liu, Zhengtao, et al. “Lattice Expansion Enables Interstitial Doping to Achieve
a High Average ZT in N‐type PbS.” Interdisciplinary Materials, vol. 2,
no. 1, Wiley, 2023, pp. 161–70, doi:10.1002/idm2.12056.
short: Z. Liu, T. Hong, L. Xu, S. Wang, X. Gao, C. Chang, X. Ding, Y. Xiao, L. Zhao,
Interdisciplinary Materials 2 (2023) 161–170.
date_created: 2024-02-14T12:12:17Z
date_published: 2023-01-01T00:00:00Z
date_updated: 2024-02-19T10:01:26Z
day: '01'
ddc:
- '540'
department:
- _id: MaIb
doi: 10.1002/idm2.12056
file:
- access_level: open_access
checksum: 7b5e8210ef1434feb173022c6dbbee0c
content_type: application/pdf
creator: dernst
date_created: 2024-02-19T09:58:32Z
date_updated: 2024-02-19T09:58:32Z
file_id: '15015'
file_name: 2023_InterdiscMaterials_Liu.pdf
file_size: 4675941
relation: main_file
success: 1
file_date_updated: 2024-02-19T09:58:32Z
has_accepted_license: '1'
intvolume: ' 2'
issue: '1'
language:
- iso: eng
month: '01'
oa: 1
oa_version: Published Version
page: 161-170
publication: Interdisciplinary Materials
publication_identifier:
eissn:
- 2767-441X
publication_status: published
publisher: Wiley
quality_controlled: '1'
status: public
title: Lattice expansion enables interstitial doping to achieve a high average ZT
in n‐type PbS
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: 2
year: '2023'
...
---
_id: '14989'
abstract:
- lang: eng
text: "Encryption alone is not enough for secure end-to end encrypted messaging:
a server must also honestly serve public keys to users. Key transparency has been
presented as an efficient\r\nsolution for detecting (and hence deterring) a server
that attempts to dishonestly serve keys. Key transparency involves two major components:
(1) a username to public key mapping, stored and cryptographically committed to
by the server, and, (2) an outof-band consistency protocol for serving short commitments
to users. In the setting of real-world deployments and supporting production scale,
new challenges must be considered for both of these components. We enumerate these
challenges and provide solutions to address them. In particular, we design and
implement a memory-optimized and privacy-preserving verifiable data structure
for committing to the username to public key store.\r\nTo make this implementation
viable for production, we also integrate support for persistent and distributed
storage. We also propose a future-facing solution, termed “compaction”, as\r\na
mechanism for mitigating practical issues that arise from dealing with infinitely
growing server data structures. Finally, we implement a consensusless solution
that achieves the minimum requirements for a service that consistently distributes
commitments for a transparency application, providing a much more efficient protocol
for distributing small and consistent\r\ncommitments to users. This culminates
in our production-grade implementation of a key transparency system (Parakeet)
which we have open-sourced, along with a demonstration of feasibility through
our benchmarks."
acknowledgement: This work is supported by the Novi team at Meta and funded in part
by IC3 industry partners and NSF grant 1943499.
article_processing_charge: No
author:
- first_name: Harjasleen
full_name: Malvai, Harjasleen
last_name: Malvai
- first_name: Eleftherios
full_name: Kokoris Kogias, Eleftherios
id: f5983044-d7ef-11ea-ac6d-fd1430a26d30
last_name: Kokoris Kogias
- first_name: Alberto
full_name: Sonnino, Alberto
last_name: Sonnino
- first_name: Esha
full_name: Ghosh, Esha
last_name: Ghosh
- first_name: Ercan
full_name: Oztürk, Ercan
last_name: Oztürk
- first_name: Kevin
full_name: Lewi, Kevin
last_name: Lewi
- first_name: Sean
full_name: Lawlor, Sean
last_name: Lawlor
citation:
ama: 'Malvai H, Kokoris Kogias E, Sonnino A, et al. Parakeet: Practical key transparency
for end-to-end eEncrypted messaging. In: Proceedings of the 2023 Network and
Distributed System Security Symposium. Internet Society; 2023. doi:10.14722/ndss.2023.24545'
apa: 'Malvai, H., Kokoris Kogias, E., Sonnino, A., Ghosh, E., Oztürk, E., Lewi,
K., & Lawlor, S. (2023). Parakeet: Practical key transparency for end-to-end
eEncrypted messaging. In Proceedings of the 2023 Network and Distributed System
Security Symposium. San Diego, CA, United States: Internet Society. https://doi.org/10.14722/ndss.2023.24545'
chicago: 'Malvai, Harjasleen, Eleftherios Kokoris Kogias, Alberto Sonnino, Esha
Ghosh, Ercan Oztürk, Kevin Lewi, and Sean Lawlor. “Parakeet: Practical Key Transparency
for End-to-End EEncrypted Messaging.” In Proceedings of the 2023 Network and
Distributed System Security Symposium. Internet Society, 2023. https://doi.org/10.14722/ndss.2023.24545.'
ieee: 'H. Malvai et al., “Parakeet: Practical key transparency for end-to-end
eEncrypted messaging,” in Proceedings of the 2023 Network and Distributed System
Security Symposium, San Diego, CA, United States, 2023.'
ista: 'Malvai H, Kokoris Kogias E, Sonnino A, Ghosh E, Oztürk E, Lewi K, Lawlor
S. 2023. Parakeet: Practical key transparency for end-to-end eEncrypted messaging.
Proceedings of the 2023 Network and Distributed System Security Symposium. NDSS:
Network and Distributed Systems Security.'
mla: 'Malvai, Harjasleen, et al. “Parakeet: Practical Key Transparency for End-to-End
EEncrypted Messaging.” Proceedings of the 2023 Network and Distributed System
Security Symposium, Internet Society, 2023, doi:10.14722/ndss.2023.24545.'
short: H. Malvai, E. Kokoris Kogias, A. Sonnino, E. Ghosh, E. Oztürk, K. Lewi, S.
Lawlor, in:, Proceedings of the 2023 Network and Distributed System Security Symposium,
Internet Society, 2023.
conference:
end_date: 2023-03-03
location: San Diego, CA, United States
name: 'NDSS: Network and Distributed Systems Security'
start_date: 2023-02-27
date_created: 2024-02-14T14:20:40Z
date_published: 2023-03-01T00:00:00Z
date_updated: 2024-02-19T12:11:15Z
day: '01'
department:
- _id: ElKo
doi: 10.14722/ndss.2023.24545
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://eprint.iacr.org/2023/081
month: '03'
oa: 1
oa_version: Published Version
publication: Proceedings of the 2023 Network and Distributed System Security Symposium
publication_identifier:
isbn:
- '1891562835'
publication_status: published
publisher: Internet Society
quality_controlled: '1'
status: public
title: 'Parakeet: Practical key transparency for end-to-end eEncrypted messaging'
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2023'
...
---
_id: '14990'
abstract:
- lang: eng
text: The software artefact to evaluate the approximation of stationary distributions
implementation.
article_processing_charge: No
author:
- first_name: Tobias
full_name: Meggendorfer, Tobias
id: b21b0c15-30a2-11eb-80dc-f13ca25802e1
last_name: Meggendorfer
orcid: 0000-0002-1712-2165
citation:
ama: 'Meggendorfer T. Artefact for: Correct Approximation of Stationary Distributions.
2023. doi:10.5281/ZENODO.7548214'
apa: 'Meggendorfer, T. (2023). Artefact for: Correct Approximation of Stationary
Distributions. Zenodo. https://doi.org/10.5281/ZENODO.7548214'
chicago: 'Meggendorfer, Tobias. “Artefact for: Correct Approximation of Stationary
Distributions.” Zenodo, 2023. https://doi.org/10.5281/ZENODO.7548214.'
ieee: 'T. Meggendorfer, “Artefact for: Correct Approximation of Stationary Distributions.”
Zenodo, 2023.'
ista: 'Meggendorfer T. 2023. Artefact for: Correct Approximation of Stationary Distributions,
Zenodo, 10.5281/ZENODO.7548214.'
mla: 'Meggendorfer, Tobias. Artefact for: Correct Approximation of Stationary
Distributions. Zenodo, 2023, doi:10.5281/ZENODO.7548214.'
short: T. Meggendorfer, (2023).
date_created: 2024-02-14T14:27:06Z
date_published: 2023-01-18T00:00:00Z
date_updated: 2024-02-27T07:19:32Z
day: '18'
ddc:
- '000'
department:
- _id: KrCh
doi: 10.5281/ZENODO.7548214
has_accepted_license: '1'
main_file_link:
- open_access: '1'
url: https://doi.org/10.5281/zenodo.7548214
month: '01'
oa: 1
oa_version: Published Version
publisher: Zenodo
related_material:
record:
- id: '13139'
relation: used_in_publication
status: public
status: public
title: 'Artefact for: Correct Approximation of Stationary Distributions'
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: research_data_reference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2023'
...
---
_id: '14991'
abstract:
- lang: eng
text: This repository contains the data, scripts, WRF codes and files required to
reproduce the results of the manuscript "Assessing Memory in Convection Schemes
Using Idealized Tests" submitted to the Journal of Advances in Modeling Earth
Systems (JAMES).
article_processing_charge: No
author:
- first_name: Yi-Ling
full_name: Hwong, Yi-Ling
id: 1217aa61-4dd1-11ec-9ac3-f2ba3f17ee22
last_name: Hwong
orcid: 0000-0001-9281-3479
- first_name: Maxime
full_name: Colin, Maxime
last_name: Colin
- first_name: Philipp
full_name: Aglas, Philipp
id: 02eace56-97fc-11ee-b81a-f0939ca85a77
last_name: Aglas
- first_name: Caroline J
full_name: Muller, Caroline J
id: f978ccb0-3f7f-11eb-b193-b0e2bd13182b
last_name: Muller
orcid: 0000-0001-5836-5350
- first_name: Steven C.
full_name: Sherwood, Steven C.
last_name: Sherwood
citation:
ama: Hwong Y-L, Colin M, Aglas P, Muller CJ, Sherwood SC. Data-assessing memory
in convection schemes using idealized tests. 2023. doi:10.5281/ZENODO.7757041
apa: Hwong, Y.-L., Colin, M., Aglas, P., Muller, C. J., & Sherwood, S. C. (2023).
Data-assessing memory in convection schemes using idealized tests. Zenodo. https://doi.org/10.5281/ZENODO.7757041
chicago: Hwong, Yi-Ling, Maxime Colin, Philipp Aglas, Caroline J Muller, and Steven
C. Sherwood. “Data-Assessing Memory in Convection Schemes Using Idealized Tests.”
Zenodo, 2023. https://doi.org/10.5281/ZENODO.7757041.
ieee: Y.-L. Hwong, M. Colin, P. Aglas, C. J. Muller, and S. C. Sherwood, “Data-assessing
memory in convection schemes using idealized tests.” Zenodo, 2023.
ista: Hwong Y-L, Colin M, Aglas P, Muller CJ, Sherwood SC. 2023. Data-assessing
memory in convection schemes using idealized tests, Zenodo, 10.5281/ZENODO.7757041.
mla: Hwong, Yi-Ling, et al. Data-Assessing Memory in Convection Schemes Using
Idealized Tests. Zenodo, 2023, doi:10.5281/ZENODO.7757041.
short: Y.-L. Hwong, M. Colin, P. Aglas, C.J. Muller, S.C. Sherwood, (2023).
date_created: 2024-02-14T14:37:57Z
date_published: 2023-06-23T00:00:00Z
date_updated: 2024-02-27T07:26:31Z
day: '23'
ddc:
- '550'
department:
- _id: CaMu
doi: 10.5281/ZENODO.7757041
ec_funded: 1
has_accepted_license: '1'
main_file_link:
- open_access: '1'
url: https://doi.org/10.5281/zenodo.7757041
month: '06'
oa: 1
oa_version: Published Version
project:
- _id: fc2ed2f7-9c52-11eb-aca3-c01059dda49c
call_identifier: H2020
grant_number: '101034413'
name: 'IST-BRIDGE: International postdoctoral program'
publisher: Zenodo
related_material:
record:
- id: '14654'
relation: used_in_publication
status: public
status: public
title: Data-assessing memory in convection schemes using idealized tests
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: research_data_reference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2023'
...
---
_id: '14992'
abstract:
- lang: eng
text: In this chapter we first review the Levy–Lieb functional, which gives the
lowest kinetic and interaction energy that can be reached with all possible quantum
states having a given density. We discuss two possible convex generalizations
of this functional, corresponding to using mixed canonical and grand-canonical
states, respectively. We present some recent works about the local density approximation,
in which the functionals get replaced by purely local functionals constructed
using the uniform electron gas energy per unit volume. We then review the known
upper and lower bounds on the Levy–Lieb functionals. We start with the kinetic
energy alone, then turn to the classical interaction alone, before we are able
to put everything together. A later section is devoted to the Hohenberg–Kohn theorem
and the role of many-body unique continuation in its proof.
alternative_title:
- Mathematics and Molecular Modeling
article_processing_charge: No
arxiv: 1
author:
- first_name: Mathieu
full_name: Lewin, Mathieu
last_name: Lewin
- first_name: Elliott H.
full_name: Lieb, Elliott H.
last_name: Lieb
- first_name: Robert
full_name: Seiringer, Robert
id: 4AFD0470-F248-11E8-B48F-1D18A9856A87
last_name: Seiringer
orcid: 0000-0002-6781-0521
citation:
ama: 'Lewin M, Lieb EH, Seiringer R. Universal Functionals in Density Functional
Theory. In: Cances E, Friesecke G, eds. Density Functional Theory. 1st
ed. MAMOMO. Springer; 2023:115-182. doi:10.1007/978-3-031-22340-2_3'
apa: Lewin, M., Lieb, E. H., & Seiringer, R. (2023). Universal Functionals in
Density Functional Theory. In E. Cances & G. Friesecke (Eds.), Density
Functional Theory (1st ed., pp. 115–182). Springer. https://doi.org/10.1007/978-3-031-22340-2_3
chicago: Lewin, Mathieu, Elliott H. Lieb, and Robert Seiringer. “Universal Functionals
in Density Functional Theory.” In Density Functional Theory, edited by
Eric Cances and Gero Friesecke, 1st ed., 115–82. MAMOMO. Springer, 2023. https://doi.org/10.1007/978-3-031-22340-2_3.
ieee: M. Lewin, E. H. Lieb, and R. Seiringer, “Universal Functionals in Density
Functional Theory,” in Density Functional Theory, 1st ed., E. Cances and
G. Friesecke, Eds. Springer, 2023, pp. 115–182.
ista: 'Lewin M, Lieb EH, Seiringer R. 2023.Universal Functionals in Density Functional
Theory. In: Density Functional Theory. Mathematics and Molecular Modeling, , 115–182.'
mla: Lewin, Mathieu, et al. “Universal Functionals in Density Functional Theory.”
Density Functional Theory, edited by Eric Cances and Gero Friesecke, 1st
ed., Springer, 2023, pp. 115–82, doi:10.1007/978-3-031-22340-2_3.
short: M. Lewin, E.H. Lieb, R. Seiringer, in:, E. Cances, G. Friesecke (Eds.), Density
Functional Theory, 1st ed., Springer, 2023, pp. 115–182.
date_created: 2024-02-14T14:44:33Z
date_published: 2023-07-19T00:00:00Z
date_updated: 2024-02-20T08:33:06Z
day: '19'
department:
- _id: RoSe
doi: 10.1007/978-3-031-22340-2_3
edition: '1'
editor:
- first_name: Eric
full_name: Cances, Eric
last_name: Cances
- first_name: Gero
full_name: Friesecke, Gero
last_name: Friesecke
external_id:
arxiv:
- '1912.10424'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.1912.10424
month: '07'
oa: 1
oa_version: Preprint
page: 115-182
publication: Density Functional Theory
publication_identifier:
eisbn:
- '9783031223402'
isbn:
- '9783031223396'
issn:
- 3005-0286
publication_status: published
publisher: Springer
quality_controlled: '1'
series_title: MAMOMO
status: public
title: Universal Functionals in Density Functional Theory
type: book_chapter
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2023'
...
---
_id: '14993'
abstract:
- lang: eng
text: "Traditional top-down approaches for global health have historically failed
to achieve social progress (Hoffman et al., 2015; Hoffman & Røttingen, 2015).
Recently, however, a more holistic, multi-level approach termed One Health (OH)
(Osterhaus et al., 2020) is being adopted. Several sets of challenges have been
identified for the implementation of OH (dos S. Ribeiro et al., 2019), including
policy and funding, education and training, and multi-actor, multi-domain, and
multi-level collaborations. These exist despite the increasing accessibility to\r\nknowledge
and digital collaborative research tools through the internet. To address some
of these challenges, we propose a general framework for grassroots community-based
means of participatory research. Additionally, we present a specific roadmap to
create a Machine Learning for Global Health community in Africa. The proposed
framework aims to enable any small group of individuals with scarce resources
to build and sustain an online community within approximately two years. We provide
a discussion on the potential impact of the proposed framework for global health
research collaborations."
acknowledgement: "Houcemeddine Turki’s contributions to this final output have been
funded through the Adapting\r\nWikidata to support clinical practice using Data
Science, Semantic Web and Machine Learning\r\nproject, which is part of the Wikimedia
Research Fund maintained by the Wikimedia Foundation in San Francisco, California,
United States of America."
article_processing_charge: No
author:
- first_name: Christopher
full_name: Currin, Christopher
id: e8321fc5-3091-11eb-8a53-83f309a11ac9
last_name: Currin
orcid: 0000-0002-4809-5059
- first_name: Mercy Nyamewaa
full_name: Asiedu , Mercy Nyamewaa
last_name: 'Asiedu '
- first_name: Chris
full_name: Fourie, Chris
last_name: Fourie
- first_name: Benjamin
full_name: Rosman, Benjamin
last_name: Rosman
- first_name: Houcemeddine
full_name: Turki, Houcemeddine
last_name: Turki
- first_name: Atnafu
full_name: Lambebo Tonja, Atnafu
last_name: Lambebo Tonja
- first_name: Jade
full_name: Abbott, Jade
last_name: Abbott
- first_name: Marvellous
full_name: Ajala, Marvellous
last_name: Ajala
- first_name: Sadiq Adewale
full_name: Adedayo, Sadiq Adewale
last_name: Adedayo
- first_name: Chris Chinenye
full_name: Emezue, Chris Chinenye
last_name: Emezue
- first_name: Daphne
full_name: Machangara, Daphne
last_name: Machangara
citation:
ama: 'Currin C, Asiedu MN, Fourie C, et al. A framework for grassroots research
collaboration in machine learning and global health. In: 1st Workshop on Machine
Learning & Global Health. OpenReview; 2023.'
apa: 'Currin, C., Asiedu , M. N., Fourie, C., Rosman, B., Turki, H., Lambebo Tonja,
A., … Machangara, D. (2023). A framework for grassroots research collaboration
in machine learning and global health. In 1st Workshop on Machine Learning
& Global Health. Kigali, Rwanda: OpenReview.'
chicago: Currin, Christopher, Mercy Nyamewaa Asiedu , Chris Fourie, Benjamin Rosman,
Houcemeddine Turki, Atnafu Lambebo Tonja, Jade Abbott, et al. “A Framework for
Grassroots Research Collaboration in Machine Learning and Global Health.” In 1st
Workshop on Machine Learning & Global Health. OpenReview, 2023.
ieee: C. Currin et al., “A framework for grassroots research collaboration
in machine learning and global health,” in 1st Workshop on Machine Learning
& Global Health, Kigali, Rwanda, 2023.
ista: 'Currin C, Asiedu MN, Fourie C, Rosman B, Turki H, Lambebo Tonja A, Abbott
J, Ajala M, Adedayo SA, Emezue CC, Machangara D. 2023. A framework for grassroots
research collaboration in machine learning and global health. 1st Workshop on
Machine Learning & Global Health. ICLR: International Conference on Learning
Representations.'
mla: Currin, Christopher, et al. “A Framework for Grassroots Research Collaboration
in Machine Learning and Global Health.” 1st Workshop on Machine Learning &
Global Health, OpenReview, 2023.
short: C. Currin, M.N. Asiedu , C. Fourie, B. Rosman, H. Turki, A. Lambebo Tonja,
J. Abbott, M. Ajala, S.A. Adedayo, C.C. Emezue, D. Machangara, in:, 1st Workshop
on Machine Learning & Global Health, OpenReview, 2023.
conference:
end_date: 2023-05-05
location: Kigali, Rwanda
name: 'ICLR: International Conference on Learning Representations'
start_date: 2023-05-05
date_created: 2024-02-14T15:11:48Z
date_published: 2023-03-02T00:00:00Z
date_updated: 2024-02-28T12:12:00Z
day: '02'
department:
- _id: TiVo
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://openreview.net/forum?id=jHY_G91R880
month: '03'
oa: 1
oa_version: Published Version
publication: 1st Workshop on Machine Learning & Global Health
publication_status: published
publisher: OpenReview
quality_controlled: '1'
status: public
title: A framework for grassroots research collaboration in machine learning and global
health
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2023'
...
---
_id: '14994'
abstract:
- lang: eng
text: This resource contains the artifacts for reproducing the experimental results
presented in the paper titled "A Flexible Toolchain for Symbolic Rabin Games under
Fair and Stochastic Uncertainties" that has been submitted in CAV 2023.
article_processing_charge: No
author:
- first_name: Rupak
full_name: Majumdar, Rupak
last_name: Majumdar
- first_name: Kaushik
full_name: Mallik, Kaushik
id: 0834ff3c-6d72-11ec-94e0-b5b0a4fb8598
last_name: Mallik
orcid: 0000-0001-9864-7475
- first_name: Mateusz
full_name: Rychlicki, Mateusz
last_name: Rychlicki
- first_name: Anne-Kathrin
full_name: Schmuck, Anne-Kathrin
last_name: Schmuck
- first_name: Sadegh
full_name: Soudjani, Sadegh
last_name: Soudjani
citation:
ama: Majumdar R, Mallik K, Rychlicki M, Schmuck A-K, Soudjani S. A flexible toolchain
for symbolic rabin games under fair and stochastic uncertainties. 2023. doi:10.5281/ZENODO.7877790
apa: Majumdar, R., Mallik, K., Rychlicki, M., Schmuck, A.-K., & Soudjani, S.
(2023). A flexible toolchain for symbolic rabin games under fair and stochastic
uncertainties. Zenodo. https://doi.org/10.5281/ZENODO.7877790
chicago: Majumdar, Rupak, Kaushik Mallik, Mateusz Rychlicki, Anne-Kathrin Schmuck,
and Sadegh Soudjani. “A Flexible Toolchain for Symbolic Rabin Games under Fair
and Stochastic Uncertainties.” Zenodo, 2023. https://doi.org/10.5281/ZENODO.7877790.
ieee: R. Majumdar, K. Mallik, M. Rychlicki, A.-K. Schmuck, and S. Soudjani, “A flexible
toolchain for symbolic rabin games under fair and stochastic uncertainties.” Zenodo,
2023.
ista: Majumdar R, Mallik K, Rychlicki M, Schmuck A-K, Soudjani S. 2023. A flexible
toolchain for symbolic rabin games under fair and stochastic uncertainties, Zenodo,
10.5281/ZENODO.7877790.
mla: Majumdar, Rupak, et al. A Flexible Toolchain for Symbolic Rabin Games under
Fair and Stochastic Uncertainties. Zenodo, 2023, doi:10.5281/ZENODO.7877790.
short: R. Majumdar, K. Mallik, M. Rychlicki, A.-K. Schmuck, S. Soudjani, (2023).
date_created: 2024-02-14T15:13:00Z
date_published: 2023-04-28T00:00:00Z
date_updated: 2024-02-27T07:39:51Z
day: '28'
ddc:
- '000'
department:
- _id: ToHe
doi: 10.5281/ZENODO.7877790
has_accepted_license: '1'
main_file_link:
- open_access: '1'
url: https://doi.org/10.5281/zenodo.7877790
month: '04'
oa: 1
oa_version: Published Version
publisher: Zenodo
related_material:
record:
- id: '14758'
relation: used_in_publication
status: public
status: public
title: A flexible toolchain for symbolic rabin games under fair and stochastic uncertainties
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: research_data_reference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2023'
...
---
_id: '14995'
abstract:
- lang: eng
text: "Lincheck is a new practical and user-friendly framework for testing concurrent
data structures on the Java Virtual Machine (JVM). It provides a simple and declarative
way to write concurrent tests. Instead of describing how to perform the test,
users specify what to test by declaring all the operations to examine; the framework
automatically handles the rest. As a result, tests written with Lincheck are concise
and easy to understand. \r\nThe artifact presents a collection of Lincheck tests
that discover new bugs in popular libraries and implementations from the concurrency
literature -- they are listed in Table 1, Section 3. To evaluate the performance
of Lincheck analysis, the collection of tests also includes those which check
correct data structures and, thus, always succeed. Similarly to Table 2, Section
3, the experiments demonstrate the reasonable time to perform a test. Finally,
Lincheck provides user-friendly output with an easy-to-follow trace to reproduce
a detected error, significantly simplifying further investigation."
article_processing_charge: No
author:
- first_name: Nikita
full_name: Koval, Nikita
id: 2F4DB10C-F248-11E8-B48F-1D18A9856A87
last_name: Koval
- first_name: Alexander
full_name: Fedorov, Alexander
id: 2e711909-896a-11ed-bdf8-eb0f5a2984c6
last_name: Fedorov
- first_name: Maria
full_name: Sokolova, Maria
last_name: Sokolova
- first_name: Dmitry
full_name: Tsitelov, Dmitry
last_name: Tsitelov
- 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: 'Koval N, Fedorov A, Sokolova M, Tsitelov D, Alistarh D-A. Lincheck: A practical
framework for testing concurrent data structures on JVM. 2023. doi:10.5281/ZENODO.7877757'
apa: 'Koval, N., Fedorov, A., Sokolova, M., Tsitelov, D., & Alistarh, D.-A.
(2023). Lincheck: A practical framework for testing concurrent data structures
on JVM. Zenodo. https://doi.org/10.5281/ZENODO.7877757'
chicago: 'Koval, Nikita, Alexander Fedorov, Maria Sokolova, Dmitry Tsitelov, and
Dan-Adrian Alistarh. “Lincheck: A Practical Framework for Testing Concurrent Data
Structures on JVM.” Zenodo, 2023. https://doi.org/10.5281/ZENODO.7877757.'
ieee: 'N. Koval, A. Fedorov, M. Sokolova, D. Tsitelov, and D.-A. Alistarh, “Lincheck:
A practical framework for testing concurrent data structures on JVM.” Zenodo,
2023.'
ista: 'Koval N, Fedorov A, Sokolova M, Tsitelov D, Alistarh D-A. 2023. Lincheck:
A practical framework for testing concurrent data structures on JVM, Zenodo, 10.5281/ZENODO.7877757.'
mla: 'Koval, Nikita, et al. Lincheck: A Practical Framework for Testing Concurrent
Data Structures on JVM. Zenodo, 2023, doi:10.5281/ZENODO.7877757.'
short: N. Koval, A. Fedorov, M. Sokolova, D. Tsitelov, D.-A. Alistarh, (2023).
date_created: 2024-02-14T15:14:13Z
date_published: 2023-04-28T00:00:00Z
date_updated: 2024-02-27T07:46:52Z
day: '28'
ddc:
- '000'
department:
- _id: DaAl
doi: 10.5281/ZENODO.7877757
main_file_link:
- open_access: '1'
url: https://doi.org/10.5281/zenodo.7877757
month: '04'
oa: 1
oa_version: Published Version
publisher: Zenodo
related_material:
record:
- id: '14260'
relation: used_in_publication
status: public
status: public
title: 'Lincheck: A practical framework for testing concurrent data structures on
JVM'
type: research_data_reference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2023'
...
---
_id: '15023'
abstract:
- lang: eng
text: Reinforcement learning has shown promising results in learning neural network
policies for complicated control tasks. However, the lack of formal guarantees
about the behavior of such policies remains an impediment to their deployment.
We propose a novel method for learning a composition of neural network policies
in stochastic environments, along with a formal certificate which guarantees that
a specification over the policy's behavior is satisfied with the desired probability.
Unlike prior work on verifiable RL, our approach leverages the compositional nature
of logical specifications provided in SpectRL, to learn over graphs of probabilistic
reach-avoid specifications. The formal guarantees are provided by learning neural
network policies together with reach-avoid supermartingales (RASM) for the graph’s
sub-tasks and then composing them into a global policy. We also derive a tighter
lower bound compared to previous work on the probability of reach-avoidance implied
by a RASM, which is required to find a compositional policy with an acceptable
probabilistic threshold for complex tasks with multiple edge policies. We implement
a prototype of our approach and evaluate it on a Stochastic Nine Rooms environment.
acknowledgement: "This work was supported in part by the ERC-2020-AdG 101020093 (VAMOS)
and the ERC-2020-\r\nCoG 863818 (FoRM-SMArt)."
article_processing_charge: No
arxiv: 1
author:
- first_name: Dorde
full_name: Zikelic, Dorde
id: 294AA7A6-F248-11E8-B48F-1D18A9856A87
last_name: Zikelic
orcid: 0000-0002-4681-1699
- first_name: Mathias
full_name: Lechner, Mathias
id: 3DC22916-F248-11E8-B48F-1D18A9856A87
last_name: Lechner
- first_name: Abhinav
full_name: Verma, Abhinav
id: a235593c-d7fa-11eb-a0c5-b22ca3c66ee6
last_name: Verma
- first_name: Krishnendu
full_name: Chatterjee, Krishnendu
id: 2E5DCA20-F248-11E8-B48F-1D18A9856A87
last_name: Chatterjee
orcid: 0000-0002-4561-241X
- first_name: Thomas A
full_name: Henzinger, Thomas A
id: 40876CD8-F248-11E8-B48F-1D18A9856A87
last_name: Henzinger
orcid: 0000-0002-2985-7724
citation:
ama: 'Zikelic D, Lechner M, Verma A, Chatterjee K, Henzinger TA. Compositional policy
learning in stochastic control systems with formal guarantees. In: 37th Conference
on Neural Information Processing Systems. ; 2023.'
apa: Zikelic, D., Lechner, M., Verma, A., Chatterjee, K., & Henzinger, T. A.
(2023). Compositional policy learning in stochastic control systems with formal
guarantees. In 37th Conference on Neural Information Processing Systems.
New Orleans, LO, United States.
chicago: Zikelic, Dorde, Mathias Lechner, Abhinav Verma, Krishnendu Chatterjee,
and Thomas A Henzinger. “Compositional Policy Learning in Stochastic Control Systems
with Formal Guarantees.” In 37th Conference on Neural Information Processing
Systems, 2023.
ieee: D. Zikelic, M. Lechner, A. Verma, K. Chatterjee, and T. A. Henzinger, “Compositional
policy learning in stochastic control systems with formal guarantees,” in 37th
Conference on Neural Information Processing Systems, New Orleans, LO, United
States, 2023.
ista: 'Zikelic D, Lechner M, Verma A, Chatterjee K, Henzinger TA. 2023. Compositional
policy learning in stochastic control systems with formal guarantees. 37th Conference
on Neural Information Processing Systems. NeurIPS: Neural Information Processing
Systems.'
mla: Zikelic, Dorde, et al. “Compositional Policy Learning in Stochastic Control
Systems with Formal Guarantees.” 37th Conference on Neural Information Processing
Systems, 2023.
short: D. Zikelic, M. Lechner, A. Verma, K. Chatterjee, T.A. Henzinger, in:, 37th
Conference on Neural Information Processing Systems, 2023.
conference:
end_date: 2023-12-16
location: New Orleans, LO, United States
name: 'NeurIPS: Neural Information Processing Systems'
start_date: 2023-12-10
date_created: 2024-02-25T09:23:24Z
date_published: 2023-12-15T00:00:00Z
date_updated: 2025-07-14T09:10:04Z
day: '15'
department:
- _id: ToHe
- _id: KrCh
ec_funded: 1
external_id:
arxiv:
- '2312.01456'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2312.01456
month: '12'
oa: 1
oa_version: Preprint
project:
- _id: 0599E47C-7A3F-11EA-A408-12923DDC885E
call_identifier: H2020
grant_number: '863818'
name: 'Formal Methods for Stochastic Models: Algorithms and Applications'
- _id: 62781420-2b32-11ec-9570-8d9b63373d4d
call_identifier: H2020
grant_number: '101020093'
name: Vigilant Algorithmic Monitoring of Software
publication: 37th Conference on Neural Information Processing Systems
publication_status: epub_ahead
quality_controlled: '1'
status: public
title: Compositional policy learning in stochastic control systems with formal guarantees
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2023'
...
---
_id: '15027'
abstract:
- lang: eng
text: 'This data repository underpins the paper, published in PNAS (doi pending)
and bioarxiv (doi: https://doi.org/10.1101/2023.07.05.547777).'
article_processing_charge: No
author:
- first_name: Samo
full_name: Curk, Samo
id: 031eff0d-d481-11ee-8508-cd12a7a86e5b
last_name: Curk
orcid: 0000-0001-6160-9766
citation:
ama: Curk S. aggregation_data. 2023.
apa: Curk, S. (2023). aggregation_data. Figshare.
chicago: Curk, Samo. “Aggregation_data.” Figshare, 2023.
ieee: S. Curk, “aggregation_data.” Figshare, 2023.
ista: Curk S. 2023. aggregation_data, Figshare.
mla: Curk, Samo. Aggregation_data. Figshare, 2023.
short: S. Curk, (2023).
date_created: 2024-02-26T08:37:57Z
date_published: 2023-12-13T00:00:00Z
date_updated: 2024-02-26T08:45:55Z
day: '13'
ddc:
- '570'
department:
- _id: AnSa
has_accepted_license: '1'
license: https://creativecommons.org/publicdomain/zero/1.0/
main_file_link:
- open_access: '1'
url: https://figshare.com/s/85798bba4ebc68d822ed
month: '12'
oa: 1
oa_version: Published Version
publisher: Figshare
related_material:
record:
- id: '15001'
relation: used_in_publication
status: public
status: public
title: aggregation_data
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)
type: research_data_reference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2023'
...
---
_id: '15035'
abstract:
- lang: eng
text: "This artifact aims to reproduce experiments from the paper Monitoring Hyperproperties
With Prefix Transducers accepted at RV'23, and give further pointers to implementation
of prefix transducers.\r\nIt has two parts: a pre-compiled docker image and sources
that one can use to compile (locally or in docker) the software and run the experiments."
article_processing_charge: No
author:
- first_name: Marek
full_name: Chalupa, Marek
id: 87e34708-d6c6-11ec-9f5b-9391e7be2463
last_name: Chalupa
- first_name: Thomas A
full_name: Henzinger, Thomas A
id: 40876CD8-F248-11E8-B48F-1D18A9856A87
last_name: Henzinger
orcid: 0000-0002-2985-7724
citation:
ama: Chalupa M, Henzinger TA. Monitoring hyperproperties with prefix transducers.
2023. doi:10.5281/ZENODO.8191723
apa: Chalupa, M., & Henzinger, T. A. (2023). Monitoring hyperproperties with
prefix transducers. Zenodo. https://doi.org/10.5281/ZENODO.8191723
chicago: Chalupa, Marek, and Thomas A Henzinger. “Monitoring Hyperproperties with
Prefix Transducers.” Zenodo, 2023. https://doi.org/10.5281/ZENODO.8191723.
ieee: M. Chalupa and T. A. Henzinger, “Monitoring hyperproperties with prefix transducers.”
Zenodo, 2023.
ista: Chalupa M, Henzinger TA. 2023. Monitoring hyperproperties with prefix transducers,
Zenodo, 10.5281/ZENODO.8191723.
mla: Chalupa, Marek, and Thomas A. Henzinger. Monitoring Hyperproperties with
Prefix Transducers. Zenodo, 2023, doi:10.5281/ZENODO.8191723.
short: M. Chalupa, T.A. Henzinger, (2023).
date_created: 2024-02-28T07:34:34Z
date_published: 2023-07-28T00:00:00Z
date_updated: 2024-02-28T12:33:09Z
day: '28'
ddc:
- '000'
department:
- _id: ToHe
doi: 10.5281/ZENODO.8191723
ec_funded: 1
has_accepted_license: '1'
main_file_link:
- open_access: '1'
url: https://doi.org/10.5281/zenodo.8191722
month: '07'
oa: 1
oa_version: Published Version
project:
- _id: 62781420-2b32-11ec-9570-8d9b63373d4d
call_identifier: H2020
grant_number: '101020093'
name: Vigilant Algorithmic Monitoring of Software
publisher: Zenodo
related_material:
record:
- id: '14076'
relation: used_in_publication
status: public
status: public
title: Monitoring hyperproperties with prefix transducers
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: research_data_reference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2023'
...
---
_id: '15039'
abstract:
- lang: eng
text: 'A crucial property for achieving secure, trustworthy and interpretable deep
learning systems is their robustness: small changes to a system''s inputs should
not result in large changes to its outputs. Mathematically, this means one strives
for networks with a small Lipschitz constant. Several recent works have focused
on how to construct such Lipschitz networks, typically by imposing constraints
on the weight matrices. In this work, we study an orthogonal aspect, namely the
role of the activation function. We show that commonly used activation functions,
such as MaxMin, as well as all piece-wise linear ones with two segments unnecessarily
restrict the class of representable functions, even in the simplest one-dimensional
setting. We furthermore introduce the new N-activation function that is provably
more expressive than currently popular activation functions. We provide code at
this https URL.'
article_number: '2311.06103'
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. 1-Lipschitz neural networks are more expressive with N-activations.
arXiv. doi:10.48550/ARXIV.2311.06103
apa: Prach, B., & Lampert, C. (n.d.). 1-Lipschitz neural networks are more expressive
with N-activations. arXiv. https://doi.org/10.48550/ARXIV.2311.06103
chicago: Prach, Bernd, and Christoph Lampert. “1-Lipschitz Neural Networks Are More
Expressive with N-Activations.” ArXiv, n.d. https://doi.org/10.48550/ARXIV.2311.06103.
ieee: B. Prach and C. Lampert, “1-Lipschitz neural networks are more expressive
with N-activations,” arXiv. .
ista: Prach B, Lampert C. 1-Lipschitz neural networks are more expressive with N-activations.
arXiv, 2311.06103.
mla: Prach, Bernd, and Christoph Lampert. “1-Lipschitz Neural Networks Are More
Expressive with N-Activations.” ArXiv, 2311.06103, doi:10.48550/ARXIV.2311.06103.
short: B. Prach, C. Lampert, ArXiv (n.d.).
date_created: 2024-02-28T17:59:32Z
date_published: 2023-11-10T00:00:00Z
date_updated: 2024-03-04T07:02:39Z
day: '10'
department:
- _id: GradSch
- _id: ChLa
doi: 10.48550/ARXIV.2311.06103
external_id:
arxiv:
- '2311.06103'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2311.06103
month: '11'
oa: 1
oa_version: Preprint
publication: arXiv
publication_status: submitted
status: public
title: 1-Lipschitz neural networks are more expressive with N-activations
type: preprint
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2023'
...
---
_id: '13053'
abstract:
- lang: eng
text: 'Deep neural networks (DNNs) often have to be compressed, via pruning and/or
quantization, before they can be deployed in practical settings. In this work
we propose a new compression-aware minimizer dubbed CrAM that modifies the optimization
step in a principled way, in order to produce models whose local loss behavior
is stable under compression operations such as pruning. Thus, dense models trained
via CrAM should be compressible post-training, in a single step, without significant
accuracy loss. Experimental results on standard benchmarks, such as residual networks
for ImageNet classification and BERT models for language modelling, show that
CrAM produces dense models that can be more accurate than the standard SGD/Adam-based
baselines, but which are stable under weight pruning: specifically, we can prune
models in one-shot to 70-80% sparsity with almost no accuracy loss, and to 90%
with reasonable (∼1%) accuracy loss, which is competitive with gradual compression
methods. Additionally, CrAM can produce sparse models which perform well for transfer
learning, and it also works for semi-structured 2:4 pruning patterns supported
by GPU hardware. The code for reproducing the results is available at this https
URL .'
acknowledged_ssus:
- _id: ScienComp
acknowledgement: "AP, EK, DA received funding from the European Research Council (ERC)
under the European\r\nUnion’s Horizon 2020 research and innovation programme (grant
agreement No 805223 ScaleML). AV acknowledges the support of the French Agence Nationale
de la Recherche (ANR), under grant ANR-21-CE48-0016 (project COMCOPT). We further
acknowledge the support from the Scientific Service Units (SSU) of ISTA through
resources provided by Scientific Computing (SciComp)-"
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: Adrian
full_name: Vladu, Adrian
last_name: Vladu
- first_name: Eldar
full_name: Kurtic, Eldar
id: 47beb3a5-07b5-11eb-9b87-b108ec578218
last_name: Kurtic
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
- 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: 'Peste E-A, Vladu A, Kurtic E, Lampert C, Alistarh D-A. CrAM: A Compression-Aware
Minimizer. In: 11th International Conference on Learning Representations .'
apa: 'Peste, E.-A., Vladu, A., Kurtic, E., Lampert, C., & Alistarh, D.-A. (n.d.).
CrAM: A Compression-Aware Minimizer. In 11th International Conference on Learning
Representations . Kigali, Rwanda .'
chicago: 'Peste, Elena-Alexandra, Adrian Vladu, Eldar Kurtic, Christoph Lampert,
and Dan-Adrian Alistarh. “CrAM: A Compression-Aware Minimizer.” In 11th International
Conference on Learning Representations , n.d.'
ieee: 'E.-A. Peste, A. Vladu, E. Kurtic, C. Lampert, and D.-A. Alistarh, “CrAM:
A Compression-Aware Minimizer,” in 11th International Conference on Learning
Representations , Kigali, Rwanda .'
ista: 'Peste E-A, Vladu A, Kurtic E, Lampert C, Alistarh D-A. CrAM: A Compression-Aware
Minimizer. 11th International Conference on Learning Representations . ICLR: International
Conference on Learning Representations.'
mla: 'Peste, Elena-Alexandra, et al. “CrAM: A Compression-Aware Minimizer.” 11th
International Conference on Learning Representations .'
short: E.-A. Peste, A. Vladu, E. Kurtic, C. Lampert, D.-A. Alistarh, in:, 11th International
Conference on Learning Representations , n.d.
conference:
end_date: 2023-05-05
location: 'Kigali, Rwanda '
name: 'ICLR: International Conference on Learning Representations'
start_date: 2023-05-01
date_created: 2023-05-23T11:36:18Z
date_published: 2023-05-01T00:00:00Z
date_updated: 2023-06-01T12:54:45Z
department:
- _id: GradSch
- _id: DaAl
- _id: ChLa
ec_funded: 1
external_id:
arxiv:
- '2207.14200'
language:
- iso: eng
main_file_link:
- open_access: '1'
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month: '05'
oa: 1
oa_version: Preprint
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: accepted
quality_controlled: '1'
related_material:
record:
- id: '13074'
relation: dissertation_contains
status: public
status: public
title: 'CrAM: A Compression-Aware Minimizer'
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2023'
...
---
_id: '13074'
abstract:
- lang: eng
text: "Deep learning has become an integral part of a large number of important
applications, and many of the recent breakthroughs have been enabled by the ability
to train very large models, capable to capture complex patterns and relationships
from the data. At the same time, the massive sizes of modern deep learning models
have made their deployment to smaller devices more challenging; this is particularly
important, as in many applications the users rely on accurate deep learning predictions,
but they only have access to devices with limited memory and compute power. One
solution to this problem is to prune neural networks, by setting as many of their
parameters as possible to zero, to obtain accurate sparse models with lower memory
footprint. Despite the great research progress in obtaining sparse models that
preserve accuracy, while satisfying memory and computational constraints, there
are still many challenges associated with efficiently training sparse models,
as well as understanding their generalization properties.\r\n\r\nThe focus of
this thesis is to investigate how the training process of sparse models can be
made more efficient, and to understand the differences between sparse and dense
models in terms of how well they can generalize to changes in the data distribution.
We first study a method for co-training sparse and dense models, at a lower cost
compared to regular training. With our method we can obtain very accurate sparse
networks, and dense models that can recover the baseline accuracy. Furthermore,
we are able to more easily analyze the differences, at prediction level, between
the sparse-dense model pairs. Next, we investigate the generalization properties
of sparse neural networks in more detail, by studying how well different sparse
models trained on a larger task can adapt to smaller, more specialized tasks,
in a transfer learning scenario. Our analysis across multiple pruning methods
and sparsity levels reveals that sparse models provide features that can transfer
similarly to or better than the dense baseline. However, the choice of the pruning
method plays an important role, and can influence the results when the features
are fixed (linear finetuning), or when they are allowed to adapt to the new task
(full finetuning). Using sparse models with fixed masks for finetuning on new
tasks has an important practical advantage, as it enables training neural networks
on smaller devices. However, one drawback of current pruning methods is that the
entire training cycle has to be repeated to obtain the initial sparse model, for
every sparsity target; in consequence, the entire training process is costly and
also multiple models need to be stored. In the last part of the thesis we propose
a method that can train accurate dense models that are compressible in a single
step, to multiple sparsity levels, without additional finetuning. Our method results
in sparse models that can be competitive with existing pruning methods, and which
can also successfully generalize to new tasks."
acknowledged_ssus:
- _id: ScienComp
alternative_title:
- ISTA Thesis
article_processing_charge: No
author:
- first_name: Elena-Alexandra
full_name: Peste, Elena-Alexandra
id: 32D78294-F248-11E8-B48F-1D18A9856A87
last_name: Peste
citation:
ama: Peste E-A. Efficiency and generalization of sparse neural networks. 2023. doi:10.15479/at:ista:13074
apa: Peste, E.-A. (2023). Efficiency and generalization of sparse neural networks.
Institute of Science and Technology Austria. https://doi.org/10.15479/at:ista:13074
chicago: Peste, Elena-Alexandra. “Efficiency and Generalization of Sparse Neural
Networks.” Institute of Science and Technology Austria, 2023. https://doi.org/10.15479/at:ista:13074.
ieee: E.-A. Peste, “Efficiency and generalization of sparse neural networks,” Institute
of Science and Technology Austria, 2023.
ista: Peste E-A. 2023. Efficiency and generalization of sparse neural networks.
Institute of Science and Technology Austria.
mla: Peste, Elena-Alexandra. Efficiency and Generalization of Sparse Neural Networks.
Institute of Science and Technology Austria, 2023, doi:10.15479/at:ista:13074.
short: E.-A. Peste, Efficiency and Generalization of Sparse Neural Networks, Institute
of Science and Technology Austria, 2023.
date_created: 2023-05-23T17:07:53Z
date_published: 2023-05-23T00:00:00Z
date_updated: 2023-08-04T10:33:27Z
day: '23'
ddc:
- '000'
degree_awarded: PhD
department:
- _id: GradSch
- _id: DaAl
- _id: ChLa
doi: 10.15479/at:ista:13074
ec_funded: 1
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month: '05'
oa: 1
oa_version: Published Version
page: '147'
project:
- _id: 2564DBCA-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '665385'
name: International IST Doctoral Program
- _id: 268A44D6-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '805223'
name: Elastic Coordination for Scalable Machine Learning
publication_identifier:
issn:
- 2663-337X
publication_status: published
publisher: Institute of Science and Technology Austria
related_material:
record:
- id: '11458'
relation: part_of_dissertation
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relation: part_of_dissertation
status: public
- id: '12299'
relation: part_of_dissertation
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
- first_name: Dan-Adrian
full_name: Alistarh, Dan-Adrian
id: 4A899BFC-F248-11E8-B48F-1D18A9856A87
last_name: Alistarh
orcid: 0000-0003-3650-940X
title: Efficiency and generalization of sparse neural networks
type: dissertation
user_id: 8b945eb4-e2f2-11eb-945a-df72226e66a9
year: '2023'
...
---
_id: '13081'
abstract:
- lang: eng
text: During development, tissues undergo changes in size and shape to form functional
organs. Distinct cellular processes such as cell division and cell rearrangements
underlie tissue morphogenesis. Yet how the distinct processes are controlled and
coordinated, and how they contribute to morphogenesis is poorly understood. In
our study, we addressed these questions using the developing mouse neural tube.
This epithelial organ transforms from a flat epithelial sheet to an epithelial
tube while increasing in size and undergoing morpho-gen-mediated patterning. The
extent and mechanism of neural progenitor rearrangement within the developing
mouse neuroepithelium is unknown. To investigate this, we per-formed high resolution
lineage tracing analysis to quantify the extent of epithelial rear-rangement at
different stages of neural tube development. We quantitatively described the relationship
between apical cell size with cell cycle dependent interkinetic nuclear migra-tions
(IKNM) and performed high cellular resolution live imaging of the neuroepithelium
to study the dynamics of junctional remodeling. Furthermore, developed a vertex
model of the neuroepithelium to investigate the quantitative contribution of cell
proliferation, cell differentiation and mechanical properties to the epithelial
rearrangement dynamics and validated the model predictions through functional
experiments. Our analysis revealed that at early developmental stages, the apical
cell area kinetics driven by IKNM induce high lev-els of cell rearrangements in
a regime of high junctional tension and contractility. After E9.5, there is a
sharp decline in the extent of cell rearrangements, suggesting that the epi-thelium
transitions from a fluid-like to a solid-like state. We found that this transition
is regulated by the growth rate of the tissue, rather than by changes in cell-cell
adhesion and contractile forces. Overall, our study provides a quantitative description
of the relationship between tissue growth, cell cycle dynamics, epithelia rearrangements
and the emergent tissue material properties, and novel insights on how epithelial
cell dynamics influences tissue morphogenesis.
acknowledged_ssus:
- _id: Bio
- _id: LifeSc
alternative_title:
- ISTA Thesis
article_processing_charge: No
author:
- first_name: Laura
full_name: Bocanegra, Laura
id: 4896F754-F248-11E8-B48F-1D18A9856A87
last_name: Bocanegra
citation:
ama: Bocanegra L. Epithelial dynamics during mouse neural tube development. 2023.
doi:10.15479/at:ista:13081
apa: Bocanegra, L. (2023). Epithelial dynamics during mouse neural tube development.
Institute of Science and Technology Austria. https://doi.org/10.15479/at:ista:13081
chicago: Bocanegra, Laura. “Epithelial Dynamics during Mouse Neural Tube Development.”
Institute of Science and Technology Austria, 2023. https://doi.org/10.15479/at:ista:13081.
ieee: L. Bocanegra, “Epithelial dynamics during mouse neural tube development,”
Institute of Science and Technology Austria, 2023.
ista: Bocanegra L. 2023. Epithelial dynamics during mouse neural tube development.
Institute of Science and Technology Austria.
mla: Bocanegra, Laura. Epithelial Dynamics during Mouse Neural Tube Development.
Institute of Science and Technology Austria, 2023, doi:10.15479/at:ista:13081.
short: L. Bocanegra, Epithelial Dynamics during Mouse Neural Tube Development, Institute
of Science and Technology Austria, 2023.
date_created: 2023-05-23T19:10:42Z
date_published: 2023-05-23T00:00:00Z
date_updated: 2023-10-04T11:14:04Z
day: '23'
ddc:
- '570'
degree_awarded: PhD
department:
- _id: GradSch
- _id: AnKi
doi: 10.15479/at:ista:13081
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date_updated: 2023-05-25T06:32:12Z
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has_accepted_license: '1'
language:
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month: '05'
oa_version: Published Version
page: '93'
publication_identifier:
issn:
- 2663 - 337X
publication_status: published
publisher: Institute of Science and Technology Austria
related_material:
record:
- id: '9349'
relation: part_of_dissertation
status: public
- id: '12837'
relation: part_of_dissertation
status: public
status: public
supervisor:
- first_name: Anna
full_name: Kicheva, Anna
id: 3959A2A0-F248-11E8-B48F-1D18A9856A87
last_name: Kicheva
orcid: 0000-0003-4509-4998
title: Epithelial dynamics during mouse neural tube development
tmp:
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legal_code_url: https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode
name: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
(CC BY-NC-ND 4.0)
short: CC BY-NC-ND (4.0)
type: dissertation
user_id: 8b945eb4-e2f2-11eb-945a-df72226e66a9
year: '2023'
...