---
OA_place: repository
OA_type: green
_id: '18976'
abstract:
- lang: eng
text: We analyze asynchronous-type algorithms for distributed SGD in the heterogeneous
setting, where each worker has its own computation and communication speeds, as
well as data distribution. In these algorithms, workers compute possibly stale
and stochastic gradients associated with their local data at some iteration back
in history and then return those gradients to the server without synchronizing
with other workers. We present a unified convergence theory for non-convex smooth
functions in the heterogeneous regime. The proposed analysis provides convergence
for pure asynchronous SGD and its various modifications. Moreover, our theory
explains what affects the convergence rate and what can be done to improve the
performance of asynchronous algorithms. In particular, we introduce a novel asynchronous
method based on worker shuffling. As a by-product of our analysis, we also demonstrate
convergence guarantees for gradient-type algorithms such as SGD with random reshuffling
and shuffle-once mini-batch SGD. The derived rates match the best-known results
for those algorithms, highlighting the tightness of our approach. Finally, our
numerical evaluations support theoretical findings and show the good practical
performance of our method.
acknowledgement: "The authors thank all anonymous reviewers for their valuable comments
and suggestions on how to improve the manuscript. This work was done when Rustem
Islamov was a Master’s student at Institut Polytechnique de Paris (IP Paris) and
an intern at Institute of Science and Technology Austria (ISTA). The research of
Rustem Islamov was supported by ISTA internship\r\nprogram. Mher Safaryan has received
funding from the European Union’s Horizon 2020 research and innovation program under
the Marie Skłodowska-Curie grant agreement No 101034413."
alternative_title:
- PMLR
article_processing_charge: No
arxiv: 1
author:
- first_name: Rustem
full_name: Islamov, Rustem
last_name: Islamov
- first_name: Mher
full_name: Safaryan, Mher
id: dd546b39-0804-11ed-9c55-ef075c39778d
last_name: Safaryan
- 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: 'Islamov R, Safaryan M, Alistarh D-A. AsGrad: A sharp unified analysis of asynchronous-SGD
algorithms. In: Proceedings of The 27th International Conference on Artificial
Intelligence and Statistics. Vol 238. ML Research Press; 2024:649-657.'
apa: 'Islamov, R., Safaryan, M., & Alistarh, D.-A. (2024). AsGrad: A sharp unified
analysis of asynchronous-SGD algorithms. In Proceedings of The 27th International
Conference on Artificial Intelligence and Statistics (Vol. 238, pp. 649–657).
Valencia, Spain: ML Research Press.'
chicago: 'Islamov, Rustem, Mher Safaryan, and Dan-Adrian Alistarh. “AsGrad: A Sharp
Unified Analysis of Asynchronous-SGD Algorithms.” In Proceedings of The 27th
International Conference on Artificial Intelligence and Statistics, 238:649–57.
ML Research Press, 2024.'
ieee: 'R. Islamov, M. Safaryan, and D.-A. Alistarh, “AsGrad: A sharp unified analysis
of asynchronous-SGD algorithms,” in Proceedings of The 27th International Conference
on Artificial Intelligence and Statistics, Valencia, Spain, 2024, vol. 238,
pp. 649–657.'
ista: 'Islamov R, Safaryan M, Alistarh D-A. 2024. AsGrad: A sharp unified analysis
of asynchronous-SGD algorithms. Proceedings of The 27th International Conference
on Artificial Intelligence and Statistics. AISTATS: Conference on Artificial Intelligence
and Statistics, PMLR, vol. 238, 649–657.'
mla: 'Islamov, Rustem, et al. “AsGrad: A Sharp Unified Analysis of Asynchronous-SGD
Algorithms.” Proceedings of The 27th International Conference on Artificial
Intelligence and Statistics, vol. 238, ML Research Press, 2024, pp. 649–57.'
short: R. Islamov, M. Safaryan, D.-A. Alistarh, in:, Proceedings of The 27th International
Conference on Artificial Intelligence and Statistics, ML Research Press, 2024,
pp. 649–657.
conference:
end_date: 2024-05-04
location: Valencia, Spain
name: 'AISTATS: Conference on Artificial Intelligence and Statistics'
start_date: 2024-05-02
corr_author: '1'
date_created: 2025-01-30T08:15:49Z
date_published: 2024-05-15T00:00:00Z
date_updated: 2025-04-14T07:54:52Z
day: '15'
department:
- _id: DaAl
ec_funded: 1
external_id:
arxiv:
- '2310.20452'
intvolume: ' 238'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2310.20452
month: '05'
oa: 1
oa_version: Preprint
page: 649-657
project:
- _id: fc2ed2f7-9c52-11eb-aca3-c01059dda49c
call_identifier: H2020
grant_number: '101034413'
name: 'IST-BRIDGE: International postdoctoral program'
publication: Proceedings of The 27th International Conference on Artificial Intelligence
and Statistics
publication_identifier:
eissn:
- 2640-3498
publication_status: published
publisher: ML Research Press
quality_controlled: '1'
scopus_import: '1'
status: public
title: 'AsGrad: A sharp unified analysis of asynchronous-SGD algorithms'
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 238
year: '2024'
...
---
OA_place: repository
OA_type: green
_id: '18977'
abstract:
- lang: eng
text: "Recent advances in large language model (LLM) pretraining have led to high-quality
LLMs with impressive abilities. By compressing such LLMs via quantization to 3-4
bits per parameter, they can fit into memory-limited devices such as laptops and
mobile phones, enabling personalized use. Quantizing models to 3-4 bits per parameter
can lead to moderate to high accuracy losses, especially for smaller models (1-10B
parameters), which are suitable for edge deployment. To address this accuracy
issue, we introduce the Sparse-Quantized Representation (SpQR), a new compressed
format and quantization technique that enables for the first time \\emph{near-lossless}
compression of LLMs across model scales while reaching similar compression levels
to previous methods. SpQR works by identifying and isolating \\emph{outlier weights},
which cause particularly large quantization errors, and storing them in higher
precision while compressing all other weights to 3-4 bits, and achieves relative
accuracy losses of less than \r\n in perplexity for highly-accurate LLaMA and
Falcon LLMs. This makes it possible to run a 33B parameter LLM on a single 24
GB consumer GPU without performance degradation at 15% speedup, thus making powerful
LLMs available to consumers without any downsides. SpQR comes with efficient algorithms
for both encoding weights into its format, as well as decoding them efficiently
at runtime. Specifically, we provide an efficient GPU inference algorithm for
SpQR, which yields faster inference than 16-bit baselines at similar accuracy
while enabling memory compression gains of more than 4x."
acknowledgement: "Denis Kuznedelev acknowledges the support from the Russian Ministry
of Science and Higher\r\nEducation, grant No. 075-10-2021-068. Ruslan Svirschevski
and Vage Egiazarian and Denis\r\nKuznedelev were supported by the grant for research
centers in the field of AI provided by the\r\nAnalytical Center for the Government
of the Russian Federation (ACRF) in accordance with the\r\nagreement on the provision
of subsidies (identifier of the agreement 000000D730321P5Q0002) and the agreement
with HSE University No. 70-2021-00139."
article_processing_charge: No
arxiv: 1
author:
- first_name: Tim
full_name: Dettmers, Tim
last_name: Dettmers
- first_name: Ruslan A.
full_name: Svirschevski, Ruslan A.
last_name: Svirschevski
- first_name: Vage
full_name: Egiazarian, Vage
last_name: Egiazarian
- first_name: Denis
full_name: Kuznedelev, Denis
last_name: Kuznedelev
- first_name: Elias
full_name: Frantar, Elias
id: 09a8f98d-ec99-11ea-ae11-c063a7b7fe5f
last_name: Frantar
- first_name: Saleh
full_name: Ashkboos, Saleh
last_name: Ashkboos
- first_name: Alexander
full_name: Borzunov, Alexander
last_name: Borzunov
- first_name: Torsten
full_name: Hoefler, Torsten
last_name: Hoefler
- first_name: Dan-Adrian
full_name: Alistarh, Dan-Adrian
id: 4A899BFC-F248-11E8-B48F-1D18A9856A87
last_name: Alistarh
orcid: 0000-0003-3650-940X
citation:
ama: 'Dettmers T, Svirschevski RA, Egiazarian V, et al. SpQR: A sparse-quantized
representation for near-lossless LLM weight compression. In: 12th International
Conference on Learning Representations. OpenReview; 2024.'
apa: 'Dettmers, T., Svirschevski, R. A., Egiazarian, V., Kuznedelev, D., Frantar,
E., Ashkboos, S., … Alistarh, D.-A. (2024). SpQR: A sparse-quantized representation
for near-lossless LLM weight compression. In 12th International Conference
on Learning Representations. Vienna, Austria: OpenReview.'
chicago: 'Dettmers, Tim, Ruslan A. Svirschevski, Vage Egiazarian, Denis Kuznedelev,
Elias Frantar, Saleh Ashkboos, Alexander Borzunov, Torsten Hoefler, and Dan-Adrian
Alistarh. “SpQR: A Sparse-Quantized Representation for near-Lossless LLM Weight
Compression.” In 12th International Conference on Learning Representations.
OpenReview, 2024.'
ieee: 'T. Dettmers et al., “SpQR: A sparse-quantized representation for near-lossless
LLM weight compression,” in 12th International Conference on Learning Representations,
Vienna, Austria, 2024.'
ista: 'Dettmers T, Svirschevski RA, Egiazarian V, Kuznedelev D, Frantar E, Ashkboos
S, Borzunov A, Hoefler T, Alistarh D-A. 2024. SpQR: A sparse-quantized representation
for near-lossless LLM weight compression. 12th International Conference on Learning
Representations. ICLR: International Conference on Learning Representations.'
mla: 'Dettmers, Tim, et al. “SpQR: A Sparse-Quantized Representation for near-Lossless
LLM Weight Compression.” 12th International Conference on Learning Representations,
OpenReview, 2024.'
short: T. Dettmers, R.A. Svirschevski, V. Egiazarian, D. Kuznedelev, E. Frantar,
S. Ashkboos, A. Borzunov, T. Hoefler, D.-A. Alistarh, in:, 12th International
Conference on Learning Representations, OpenReview, 2024.
conference:
end_date: 2024-05-11
location: Vienna, Austria
name: 'ICLR: International Conference on Learning Representations'
start_date: 2024-05-07
date_created: 2025-01-30T08:26:59Z
date_published: 2024-05-15T00:00:00Z
date_updated: 2025-01-30T08:27:47Z
day: '15'
department:
- _id: DaAl
external_id:
arxiv:
- '2306.03078'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2306.03078
month: '05'
oa: 1
oa_version: Preprint
publication: 12th International Conference on Learning Representations
publication_status: published
publisher: OpenReview
quality_controlled: '1'
scopus_import: '1'
status: public
title: 'SpQR: A sparse-quantized representation for near-lossless LLM weight compression'
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2024'
...
---
OA_place: repository
_id: '18981'
abstract:
- lang: eng
text: We establish several results combining discrete Morse theory and microlocal
sheaf theory in the setting of finite posets and simplicial complexes. Our primary
tool is a computationally tractable description of the bounded derived category
of sheaves on a poset with the Alexandrov topology. We prove that each bounded
complex of sheaves on a finite poset admits a unique (up to isomorphism of complexes)
minimal injective resolution, and we provide algorithms for computing minimal
injective resolution of an injective complex, as well as several useful functors
between derived categories of sheaves. For the constant sheaf on a simplicial
complex, we give asymptotically tight bounds on the complexity of computing the
minimal injective resolution using those algorithms. Our main result is a novel
definition of the discrete microsupport of a bounded complex of sheaves on a finite
poset. We detail several foundational properties of the discrete microsupport,
as well as a microlocal generalization of the discrete homological Morse theorem
and Morse inequalities.
acknowledgement: "This project has received funding from the European Research Council
(ERC) under the European\r\nUnion’s Horizon 2020 research and innovation programme,
grant no. 788183, from the Wittgenstein Prize,\r\nAustrian Science Fund (FWF), grant
no. Z 342-N31, and from the DFG Collaborative Research Center TRR\r\n109, ‘Discretization
in Geometry and Dynamics’, Austrian Science Fund (FWF), grant no. I 02979-N35."
article_processing_charge: No
arxiv: 1
author:
- first_name: Adam
full_name: Brown, Adam
last_name: Brown
- first_name: Ondrej
full_name: Draganov, Ondrej
id: 2B23F01E-F248-11E8-B48F-1D18A9856A87
last_name: Draganov
orcid: 0000-0003-0464-3823
citation:
ama: Brown A, Draganov O. Discrete microlocal Morse theory. arXiv. doi:10.48550/arXiv.2209.14993
apa: Brown, A., & Draganov, O. (n.d.). Discrete microlocal Morse theory. arXiv.
https://doi.org/10.48550/arXiv.2209.14993
chicago: Brown, Adam, and Ondrej Draganov. “Discrete Microlocal Morse Theory.” ArXiv,
n.d. https://doi.org/10.48550/arXiv.2209.14993.
ieee: A. Brown and O. Draganov, “Discrete microlocal Morse theory,” arXiv.
.
ista: Brown A, Draganov O. Discrete microlocal Morse theory. arXiv, 10.48550/arXiv.2209.14993.
mla: Brown, Adam, and Ondrej Draganov. “Discrete Microlocal Morse Theory.” ArXiv,
doi:10.48550/arXiv.2209.14993.
short: A. Brown, O. Draganov, ArXiv (n.d.).
corr_author: '1'
date_created: 2025-01-31T17:03:04Z
date_published: 2024-06-09T00:00:00Z
date_updated: 2026-04-07T11:47:29Z
day: '09'
department:
- _id: HeEd
doi: 10.48550/arXiv.2209.14993
ec_funded: 1
external_id:
arxiv:
- '2209.14993'
fulldoi: https://doi.org/10.48550/arXiv.2209.14993
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2209.14993
month: '06'
oa: 1
oa_version: Preprint
project:
- _id: 266A2E9E-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '788183'
name: Alpha Shape Theory Extended
- _id: 268116B8-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: Z00342
name: Mathematics, Computer Science
- _id: 2561EBF4-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: I02979-N35
name: Persistence and stability of geometric complexes
publication: arXiv
publication_status: draft
related_material:
record:
- id: '20323'
relation: later_version
status: public
- id: '18979'
relation: dissertation_contains
status: public
status: public
title: Discrete microlocal Morse theory
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: preprint
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2024'
...
---
OA_place: repository
OA_type: green
_id: '18996'
abstract:
- lang: eng
text: 'We consider the linear causal representation learning setting where we observe
a linear mixing of d unknown latent factors, which follow a linear structural
causal model. Recent work has shown that it is possible to recover the latent
factors as well as the underlying structural causal model over them, up to permutation
and scaling, provided that we have at least d environments, each of which corresponds
to perfect interventions on a single latent node (factor). After this powerful
result, a key open problem faced by the community has been to relax these conditions:
allow for coarser than perfect single-node interventions, and allow for fewer
than d of them, since the number of latent factors d could be very large. In this
work, we consider precisely such a setting, where we allow a smaller than d number
of environments, and also allow for very coarse interventions that can very coarsely
\textit{change the entire causal graph over the latent factors}. On the flip side,
we relax what we wish to extract to simply the \textit{list of nodes that have
shifted between one or more environments}. We provide a surprising identifiability
result that it is indeed possible, under some very mild standard assumptions,
to identify the set of shifted nodes. Our identifiability proof moreover is a
constructive one: we explicitly provide necessary and sufficient conditions for
a node to be a shifted node, and show that we can check these conditions given
observed data. Our algorithm lends itself very naturally to the sample setting
where instead of just interventional distributions, we are provided datasets of
samples from each of these distributions. We corroborate our results on both synthetic
experiments as well as an interesting psychometric dataset. The code can be found
at https://github.com/TianyuCodings/iLCS.'
alternative_title:
- Advances in Neural Information Processing Systems
article_processing_charge: No
arxiv: 1
author:
- first_name: Tianyu
full_name: Chen, Tianyu
last_name: Chen
- first_name: Kevin
full_name: Bello, Kevin
last_name: Bello
- first_name: Francesco
full_name: Locatello, Francesco
id: 26cfd52f-2483-11ee-8040-88983bcc06d4
last_name: Locatello
orcid: 0000-0002-4850-0683
- first_name: Bryon
full_name: Aragam, Bryon
last_name: Aragam
- first_name: Pradeep Kumar
full_name: Ravikumar, Pradeep Kumar
last_name: Ravikumar
citation:
ama: 'Chen T, Bello K, Locatello F, Aragam B, Ravikumar PK. Identifying general
mechanism shifts in linear causal representations. In: 38th Conference on Neural
Information Processing Systems. Vol 37. Neural Information Processing Systems
Foundation; 2024.'
apa: 'Chen, T., Bello, K., Locatello, F., Aragam, B., & Ravikumar, P. K. (2024).
Identifying general mechanism shifts in linear causal representations. In 38th
Conference on Neural Information Processing Systems (Vol. 37). Vancouver,
Canada: Neural Information Processing Systems Foundation.'
chicago: Chen, Tianyu, Kevin Bello, Francesco Locatello, Bryon Aragam, and Pradeep
Kumar Ravikumar. “Identifying General Mechanism Shifts in Linear Causal Representations.”
In 38th Conference on Neural Information Processing Systems, Vol. 37. Neural
Information Processing Systems Foundation, 2024.
ieee: T. Chen, K. Bello, F. Locatello, B. Aragam, and P. K. Ravikumar, “Identifying
general mechanism shifts in linear causal representations,” in 38th Conference
on Neural Information Processing Systems, Vancouver, Canada, 2024, vol. 37.
ista: 'Chen T, Bello K, Locatello F, Aragam B, Ravikumar PK. 2024. Identifying general
mechanism shifts in linear causal representations. 38th Conference on Neural Information
Processing Systems. NeurIPS: Neural Information Processing Systems, Advances in
Neural Information Processing Systems, vol. 37.'
mla: Chen, Tianyu, et al. “Identifying General Mechanism Shifts in Linear Causal
Representations.” 38th Conference on Neural Information Processing Systems,
vol. 37, Neural Information Processing Systems Foundation, 2024.
short: T. Chen, K. Bello, F. Locatello, B. Aragam, P.K. Ravikumar, in:, 38th Conference
on Neural Information Processing Systems, Neural Information Processing Systems
Foundation, 2024.
conference:
end_date: 2024-12-16
location: Vancouver, Canada
name: 'NeurIPS: Neural Information Processing Systems'
start_date: 2024-12-16
date_created: 2025-02-04T13:09:34Z
date_published: 2024-09-25T00:00:00Z
date_updated: 2025-07-07T13:23:49Z
day: '25'
ddc:
- '000'
department:
- _id: FrLo
external_id:
arxiv:
- '2410.24059'
file:
- access_level: open_access
checksum: 75c3091e70bd2916cd94afbf40a0c425
content_type: application/pdf
creator: dernst
date_created: 2025-02-04T13:09:08Z
date_updated: 2025-02-04T13:09:08Z
file_id: '18997'
file_name: 2024_NeurIPS_Chen.pdf
file_size: 5659119
relation: main_file
success: 1
file_date_updated: 2025-02-04T13:09:08Z
has_accepted_license: '1'
intvolume: ' 37'
language:
- iso: eng
month: '09'
oa: 1
oa_version: Published Version
publication: 38th Conference on Neural Information Processing Systems
publication_identifier:
eissn:
- 1049-5258
publication_status: published
publisher: Neural Information Processing Systems Foundation
quality_controlled: '1'
scopus_import: '1'
status: public
title: Identifying general mechanism shifts in linear causal representations
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: 37
year: '2024'
...
---
OA_place: publisher
OA_type: gold
_id: '18998'
abstract:
- lang: eng
text: Word embeddings represent language vocabularies as clouds of d-dimensional
points. We investigate how information is conveyed by the general shape of these
clouds, instead of representing the semantic meaning of each token. Specifically,
we use the notion of persistent homology from topological data analysis (TDA)
to measure the distances between language pairs from the shape of their unlabeled
embeddings. These distances quantify the degree of non-isometry of the embeddings.
To distinguish whether these differences are random training errors or capture
real information about the languages, we use the computed distance matrices to
construct language phylogenetic trees over 81 Indo-European languages. Careful
evaluation shows that our reconstructed trees exhibit strong and statistically-significant
similarities to the reference.
article_processing_charge: No
arxiv: 1
author:
- first_name: Ondrej
full_name: Draganov, Ondrej
id: 2B23F01E-F248-11E8-B48F-1D18A9856A87
last_name: Draganov
orcid: 0000-0003-0464-3823
- first_name: Steven
full_name: Skiena, Steven
last_name: Skiena
citation:
ama: 'Draganov O, Skiena S. The shape of word embeddings: Quantifying non-isometry
with topological data analysis. In: Findings of the Association for Computational
Linguistics: EMNLP 2024. Association for Computational Linguistics; 2024:12080-12099.
doi:10.18653/v1/2024.findings-emnlp.705'
apa: 'Draganov, O., & Skiena, S. (2024). The shape of word embeddings: Quantifying
non-isometry with topological data analysis. In Findings of the Association
for Computational Linguistics: EMNLP 2024 (pp. 12080–12099). Miami, FL, United
States: Association for Computational Linguistics. https://doi.org/10.18653/v1/2024.findings-emnlp.705'
chicago: 'Draganov, Ondrej, and Steven Skiena. “The Shape of Word Embeddings: Quantifying
Non-Isometry with Topological Data Analysis.” In Findings of the Association
for Computational Linguistics: EMNLP 2024, 12080–99. Association for Computational
Linguistics, 2024. https://doi.org/10.18653/v1/2024.findings-emnlp.705.'
ieee: 'O. Draganov and S. Skiena, “The shape of word embeddings: Quantifying non-isometry
with topological data analysis,” in Findings of the Association for Computational
Linguistics: EMNLP 2024, Miami, FL, United States, 2024, pp. 12080–12099.'
ista: 'Draganov O, Skiena S. 2024. The shape of word embeddings: Quantifying non-isometry
with topological data analysis. Findings of the Association for Computational
Linguistics: EMNLP 2024. EMNLP: Conference on Empirical Methods in Natural Language
Processing, 12080–12099.'
mla: 'Draganov, Ondrej, and Steven Skiena. “The Shape of Word Embeddings: Quantifying
Non-Isometry with Topological Data Analysis.” Findings of the Association for
Computational Linguistics: EMNLP 2024, Association for Computational Linguistics,
2024, pp. 12080–99, doi:10.18653/v1/2024.findings-emnlp.705.'
short: 'O. Draganov, S. Skiena, in:, Findings of the Association for Computational
Linguistics: EMNLP 2024, Association for Computational Linguistics, 2024, pp.
12080–12099.'
conference:
end_date: 2024-11-16
location: Miami, FL, United States
name: 'EMNLP: Conference on Empirical Methods in Natural Language Processing'
start_date: 2024-11-12
corr_author: '1'
date_created: 2025-02-04T16:19:28Z
date_published: 2024-11-01T00:00:00Z
date_updated: 2025-02-10T08:21:37Z
day: '01'
ddc:
- '500'
department:
- _id: GradSch
- _id: HeEd
doi: 10.18653/v1/2024.findings-emnlp.705
external_id:
arxiv:
- '2404.00500'
file:
- access_level: open_access
checksum: f4416a5962194f0181ab0dc7f9ef93c0
content_type: application/pdf
creator: dernst
date_created: 2025-02-10T08:20:34Z
date_updated: 2025-02-10T08:20:34Z
file_id: '19016'
file_name: 2024_EMNLP_Draganov.pdf
file_size: 1312638
relation: main_file
success: 1
file_date_updated: 2025-02-10T08:20:34Z
fulldoi: https://doi.org/10.18653/v1/2024.findings-emnlp.705
has_accepted_license: '1'
language:
- iso: eng
month: '11'
oa: 1
oa_version: Published Version
page: 12080-12099
publication: 'Findings of the Association for Computational Linguistics: EMNLP 2024'
publication_status: published
publisher: Association for Computational Linguistics
quality_controlled: '1'
scopus_import: '1'
status: public
title: 'The shape of word embeddings: Quantifying non-isometry with topological data
analysis'
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: '2024'
...
---
OA_place: repository
OA_type: green
_id: '18999'
abstract:
- lang: eng
text: Exploring the shape of point configurations has been a key driver in the evolution
of TDA (short for topological data analysis) since its infancy. This survey illustrates
the recent efforts to broaden these ideas to model spatial interactions among
multiple configurations, each distinguished by a color. It describes advances
in this area and prepares the ground for further exploration by mentioning unresolved
questions and promising research avenues while focusing on the overlap with discrete
geometry.
article_number: '2406.04102'
article_processing_charge: No
arxiv: 1
author:
- first_name: Sebastiano
full_name: Cultrera di Montesano, Sebastiano
id: 34D2A09C-F248-11E8-B48F-1D18A9856A87
last_name: Cultrera di Montesano
orcid: 0000-0001-6249-0832
- first_name: Ondrej
full_name: Draganov, Ondrej
id: 2B23F01E-F248-11E8-B48F-1D18A9856A87
last_name: Draganov
orcid: 0000-0003-0464-3823
- first_name: Herbert
full_name: Edelsbrunner, Herbert
id: 3FB178DA-F248-11E8-B48F-1D18A9856A87
last_name: Edelsbrunner
orcid: 0000-0002-9823-6833
- first_name: Morteza
full_name: Saghafian, Morteza
id: f86f7148-b140-11ec-9577-95435b8df824
last_name: Saghafian
citation:
ama: Cultrera di Montesano S, Draganov O, Edelsbrunner H, Saghafian M. Chromatic
topological data analysis. arXiv. doi:10.48550/ARXIV.2406.04102
apa: Cultrera di Montesano, S., Draganov, O., Edelsbrunner, H., & Saghafian,
M. (n.d.). Chromatic topological data analysis. arXiv. https://doi.org/10.48550/ARXIV.2406.04102
chicago: Cultrera di Montesano, Sebastiano, Ondrej Draganov, Herbert Edelsbrunner,
and Morteza Saghafian. “Chromatic Topological Data Analysis.” ArXiv, n.d.
https://doi.org/10.48550/ARXIV.2406.04102.
ieee: S. Cultrera di Montesano, O. Draganov, H. Edelsbrunner, and M. Saghafian,
“Chromatic topological data analysis,” arXiv. .
ista: Cultrera di Montesano S, Draganov O, Edelsbrunner H, Saghafian M. Chromatic
topological data analysis. arXiv, 2406.04102.
mla: Cultrera di Montesano, Sebastiano, et al. “Chromatic Topological Data Analysis.”
ArXiv, 2406.04102, doi:10.48550/ARXIV.2406.04102.
short: S. Cultrera di Montesano, O. Draganov, H. Edelsbrunner, M. Saghafian, ArXiv
(n.d.).
corr_author: '1'
date_created: 2025-02-04T16:21:21Z
date_published: 2024-06-06T00:00:00Z
date_updated: 2025-02-10T08:14:27Z
day: '06'
ddc:
- '510'
department:
- _id: GradSch
- _id: HeEd
doi: 10.48550/ARXIV.2406.04102
external_id:
arxiv:
- '2406.04102'
fulldoi: https://doi.org/10.48550/ARXIV.2406.04102
has_accepted_license: '1'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2406.04102
month: '06'
oa: 1
oa_version: Preprint
publication: arXiv
publication_status: submitted
status: public
title: Chromatic topological data analysis
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: preprint
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2024'
...
---
OA_place: publisher
OA_type: gold
_id: '19005'
abstract:
- lang: eng
text: "Causal representation learning promises to extend causal models to hidden
causal\r\nvariables from raw entangled measurements. However, most progress has
focused\r\non proving identifiability results in different settings, and we are
not aware of any\r\nsuccessful real-world application. At the same time, the field
of dynamical systems\r\nbenefited from deep learning and scaled to countless applications
but does not allow\r\nparameter identification. In this paper, we draw a clear
connection between the two\r\nand their key assumptions, allowing us to apply
identifiable methods developed\r\nin causal representation learning to dynamical
systems. At the same time, we can\r\nleverage scalable differentiable solvers
developed for differential equations to build\r\nmodels that are both identifiable
and practical. Overall, we learn explicitly controllable models that isolate the
trajectory-specific parameters for further downstream\r\ntasks such as out-of-distribution
classification or treatment effect estimation. We\r\nexperiment with a wind simulator
with partially known factors of variation. We\r\nalso apply the resulting model
to real-world climate data and successfully answer\r\ndownstream causal questions
in line with existing literature on climate change.\r\nCode is available at https://github.com/CausalLearningAI/crl-dynamical-systems."
acknowledgement: "We thank Niklas Boers for recommending the SpeedyWeather simulator
and Valentino Maiorca\r\nfor guidance on Fourier transformation for SST data. We
are also grateful to Shimeng Huang and Riccardo Cadei for their feedback on the
treatment effect estimation experiment and to Jiale Chen and Adeel Pervez for their
assistance with the solver implementation. Finally, we appreciate the anonymous
reviewers for their insightful suggestions, which helped improve the manuscript. "
alternative_title:
- Advances in Neural Information Processing Systems
article_processing_charge: No
arxiv: 1
author:
- first_name: Dingling
full_name: Yao, Dingling
id: d3e02e50-48a8-11ee-8f62-c108061797fa
last_name: Yao
- 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: Francesco
full_name: Locatello, Francesco
id: 26cfd52f-2483-11ee-8040-88983bcc06d4
last_name: Locatello
orcid: 0000-0002-4850-0683
citation:
ama: 'Yao D, Muller CJ, Locatello F. Marrying causal representation learning with
dynamical systems for science. In: 38th Conference on Neural Information Processing
Systems. Vol 37. Neural Information Processing Systems Foundation; 2024.'
apa: 'Yao, D., Muller, C. J., & Locatello, F. (2024). Marrying causal representation
learning with dynamical systems for science. In 38th Conference on Neural Information
Processing Systems (Vol. 37). Vancouver, Canada: Neural Information Processing
Systems Foundation.'
chicago: Yao, Dingling, Caroline J Muller, and Francesco Locatello. “Marrying Causal
Representation Learning with Dynamical Systems for Science.” In 38th Conference
on Neural Information Processing Systems, Vol. 37. Neural Information Processing
Systems Foundation, 2024.
ieee: D. Yao, C. J. Muller, and F. Locatello, “Marrying causal representation learning
with dynamical systems for science,” in 38th Conference on Neural Information
Processing Systems, Vancouver, Canada, 2024, vol. 37.
ista: 'Yao D, Muller CJ, Locatello F. 2024. Marrying causal representation learning
with dynamical systems for science. 38th Conference on Neural Information Processing
Systems. NeurIPS: Neural Information Processing Systems, Advances in Neural Information
Processing Systems, vol. 37.'
mla: Yao, Dingling, et al. “Marrying Causal Representation Learning with Dynamical
Systems for Science.” 38th Conference on Neural Information Processing Systems,
vol. 37, Neural Information Processing Systems Foundation, 2024.
short: D. Yao, C.J. Muller, F. Locatello, in:, 38th Conference on Neural Information
Processing Systems, Neural Information Processing Systems Foundation, 2024.
conference:
end_date: 2024-12-16
location: Vancouver, Canada
name: 'NeurIPS: Neural Information Processing Systems'
start_date: 2024-12-16
corr_author: '1'
date_created: 2025-02-05T07:49:00Z
date_published: 2024-12-01T00:00:00Z
date_updated: 2025-07-10T11:51:32Z
day: '01'
ddc:
- '000'
- '550'
department:
- _id: CaMu
- _id: FrLo
external_id:
arxiv:
- '2405.13888'
file:
- access_level: open_access
checksum: fe8832367e7143876f178244385d859e
content_type: application/pdf
creator: dernst
date_created: 2025-02-05T07:44:58Z
date_updated: 2025-02-05T07:44:58Z
file_id: '19006'
file_name: 2024_NeurIPS_Yao.pdf
file_size: 2595855
relation: main_file
success: 1
file_date_updated: 2025-02-05T07:44:58Z
has_accepted_license: '1'
intvolume: ' 37'
language:
- iso: eng
month: '12'
oa: 1
oa_version: Published Version
publication: 38th Conference on Neural Information Processing Systems
publication_status: published
publisher: Neural Information Processing Systems Foundation
quality_controlled: '1'
related_material:
link:
- relation: software
url: https://github.com/CausalLearningAI/crl-dynamical-systems
scopus_import: '1'
status: public
title: Marrying causal representation learning with dynamical systems for science
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: 37
year: '2024'
...
---
OA_place: publisher
OA_type: hybrid
_id: '19007'
abstract:
- lang: eng
text: "Learning modular object-centric representations is crucial for systematic
generalization. Existing methods show promising object-binding capabilities empirically,\r\nbut
theoretical identifiability guarantees remain relatively underdeveloped. Understanding
when object-centric representations can theoretically be identified is\r\ncrucial
for scaling slot-based methods to high-dimensional images with correctness\r\nguarantees.
To that end, we propose a probabilistic slot-attention algorithm that\r\nimposes
an aggregate mixture prior over object-centric slot representations, thereby\r\nproviding
slot identifiability guarantees without supervision, up to an equivalence\r\nrelation.
We provide empirical verification of our theoretical identifiability result\r\nusing
both simple 2-dimensional data and high-resolution imaging datasets.\r\n"
acknowledgement: A. Kori is supported by UKRI (grant number EP/S023356/1), as part
of the UKRI Centre for Doctoral Training in Safe and Trusted AI. B. Glocker and
F.D.S. Ribeiro acknowledge the support of the UKRI AI programme, and the Engineering
and Physical Sciences Research Council, for CHAI - EPSRC Causality in Healthcare
AI Hub (grant number EP/Y028856/1).
alternative_title:
- Advances in Neural Information Processing Systems
article_processing_charge: No
arxiv: 1
author:
- first_name: Avinash
full_name: Kori, Avinash
last_name: Kori
- first_name: Francesco
full_name: Locatello, Francesco
id: 26cfd52f-2483-11ee-8040-88983bcc06d4
last_name: Locatello
orcid: 0000-0002-4850-0683
- first_name: Ainkaran
full_name: Santhirasekaram, Ainkaran
last_name: Santhirasekaram
- first_name: Francesca
full_name: Toni, Francesca
last_name: Toni
- first_name: Ben
full_name: Glocker, Ben
last_name: Glocker
- first_name: Fabio
full_name: De Sousa Ribeiro, Fabio
last_name: De Sousa Ribeiro
citation:
ama: 'Kori A, Locatello F, Santhirasekaram A, Toni F, Glocker B, De Sousa Ribeiro
F. Identifiable object-centric representation learning via probabilistic slot
attention. In: 38th Conference on Neural Information Processing Systems.
Vol 37. Neural Information Processing Systems Foundation; 2024.'
apa: 'Kori, A., Locatello, F., Santhirasekaram, A., Toni, F., Glocker, B., &
De Sousa Ribeiro, F. (2024). Identifiable object-centric representation learning
via probabilistic slot attention. In 38th Conference on Neural Information
Processing Systems (Vol. 37). Vancouver, Canada: Neural Information Processing
Systems Foundation.'
chicago: Kori, Avinash, Francesco Locatello, Ainkaran Santhirasekaram, Francesca
Toni, Ben Glocker, and Fabio De Sousa Ribeiro. “Identifiable Object-Centric Representation
Learning via Probabilistic Slot Attention.” In 38th Conference on Neural Information
Processing Systems, Vol. 37. Neural Information Processing Systems Foundation,
2024.
ieee: A. Kori, F. Locatello, A. Santhirasekaram, F. Toni, B. Glocker, and F. De
Sousa Ribeiro, “Identifiable object-centric representation learning via probabilistic
slot attention,” in 38th Conference on Neural Information Processing Systems,
Vancouver, Canada, 2024, vol. 37.
ista: 'Kori A, Locatello F, Santhirasekaram A, Toni F, Glocker B, De Sousa Ribeiro
F. 2024. Identifiable object-centric representation learning via probabilistic
slot attention. 38th Conference on Neural Information Processing Systems. NeurIPS:
Neural Information Processing Systems, Advances in Neural Information Processing
Systems, vol. 37.'
mla: Kori, Avinash, et al. “Identifiable Object-Centric Representation Learning
via Probabilistic Slot Attention.” 38th Conference on Neural Information Processing
Systems, vol. 37, Neural Information Processing Systems Foundation, 2024.
short: A. Kori, F. Locatello, A. Santhirasekaram, F. Toni, B. Glocker, F. De Sousa
Ribeiro, in:, 38th Conference on Neural Information Processing Systems, Neural
Information Processing Systems Foundation, 2024.
conference:
end_date: 2024-12-16
location: Vancouver, Canada
name: 'NeurIPS: Neural Information Processing Systems'
start_date: 2024-12-16
date_created: 2025-02-05T08:36:22Z
date_published: 2024-12-01T00:00:00Z
date_updated: 2025-05-14T11:29:10Z
day: '01'
ddc:
- '000'
department:
- _id: FrLo
external_id:
arxiv:
- '2406.07141'
file:
- access_level: open_access
checksum: d27b3c7102adc28e798fe41001f0b919
content_type: application/pdf
creator: dernst
date_created: 2025-02-05T08:34:25Z
date_updated: 2025-02-05T08:34:25Z
file_id: '19008'
file_name: 2024_NeurIPS_Kori.pdf
file_size: 6943800
relation: main_file
success: 1
file_date_updated: 2025-02-05T08:34:25Z
has_accepted_license: '1'
intvolume: ' 37'
language:
- iso: eng
month: '12'
oa: 1
oa_version: Published Version
publication: 38th Conference on Neural Information Processing Systems
publication_status: published
publisher: Neural Information Processing Systems Foundation
quality_controlled: '1'
scopus_import: '1'
status: public
title: Identifiable object-centric representation learning via probabilistic slot
attention
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 37
year: '2024'
...
---
OA_place: publisher
OA_type: hybrid
_id: '19028'
abstract:
- lang: eng
text: The stochastic nature of modern Monte Carlo (MC) rendering methods inevitably
produces noise in rendered images for a practical number of samples per pixel.
The problem of denoising these images has been widely studied, with most recent
methods relying on data-driven, pretrained neural networks. In contrast, in this
paper we propose a statistical approach to the denoising problem, treating each
pixel as a random variable and reasoning about its distribution. Considering a
pixel of the noisy rendered image, we formulate fast pair-wise statistical tests—based
on online estimators—to decide which of the nearby pixels to exclude from the
denoising filter. We show that for symmetric pixel weights and normally distributed
samples, the classical Welch t-test is optimal in terms of mean squared error.
We then show how to extend this result to handle non-normal distributions, using
more recent confidence-interval formulations in combination with the Box-Cox transformation.
Our results show that our statistical denoising approach matches the performance
of state-of-the-art neural image denoising without having to resort to any computation-intensive
pretraining. Furthermore, our approach easily generalizes to other quantities
besides pixel intensity, which we demonstrate by showing additional applications
to Russian roulette path termination and multiple importance sampling.
acknowledgement: 'We would like to thank Lukas Lipp for fruitful discussions, Károly
Zsolnai-Fehér and Jaroslav Křivánek for valuable contributions to early versions
of this work, and Bernhard Kerbl for help with our CUDA implementation. Moreover,
we would like to thank the creators of the scenes we have used: Wig42 for “Wooden
Staircase” (Fig. 1), “Grey and White Room” (Fig. S6), and “Modern Living Room” (Fig.
S8); nacimus for “Bathroom” (Fig. 3, S5); NovaZeeke for “Japanese Classroom” (Fig.
4, 6); Beeple for “Zero-Day” (Fig. 8); Jay-Artist for “White Room” (Fig. S7); Mareck
for “Contemporary Bathroom” (Fig. 2); Christian Freude for “Glass Caustics” (Fig.
S10); and Benedikt Bitterli for “Veach Ajar” (Fig. 7, S2), “Veach MIS” (Fig. S4),
and “Fur Ball” (Fig. S11). This work has received funding from the Vienna Science
and Technology Fund (WWTF) project ICT22-028 (“Toward Optimal Path Guiding for Photorealistic
Rendering”) and the Austrian Science Fund (FWF) project F 77 (SFB “Advanced Computational
Design”).'
article_number: '68'
article_processing_charge: Yes (in subscription journal)
author:
- first_name: Hiroyuki
full_name: Sakai, Hiroyuki
last_name: Sakai
- first_name: Christian
full_name: Freude, Christian
last_name: Freude
- first_name: Thomas
full_name: Auzinger, Thomas
id: 4718F954-F248-11E8-B48F-1D18A9856A87
last_name: Auzinger
orcid: 0000-0002-1546-3265
- first_name: David
full_name: Hahn, David
id: 357A6A66-F248-11E8-B48F-1D18A9856A87
last_name: Hahn
- first_name: Michael
full_name: Wimmer, Michael
last_name: Wimmer
citation:
ama: 'Sakai H, Freude C, Auzinger T, Hahn D, Wimmer M. A statistical approach to
Monte Carlo denoising. In: Proceedings - SIGGRAPH Asia 2024 Conference Papers.
Association for Computing Machinery; 2024. doi:10.1145/3680528.3687591'
apa: 'Sakai, H., Freude, C., Auzinger, T., Hahn, D., & Wimmer, M. (2024). A
statistical approach to Monte Carlo denoising. In Proceedings - SIGGRAPH Asia
2024 Conference Papers. Tokyo, Japan: Association for Computing Machinery.
https://doi.org/10.1145/3680528.3687591'
chicago: Sakai, Hiroyuki, Christian Freude, Thomas Auzinger, David Hahn, and Michael
Wimmer. “A Statistical Approach to Monte Carlo Denoising.” In Proceedings -
SIGGRAPH Asia 2024 Conference Papers. Association for Computing Machinery,
2024. https://doi.org/10.1145/3680528.3687591.
ieee: H. Sakai, C. Freude, T. Auzinger, D. Hahn, and M. Wimmer, “A statistical approach
to Monte Carlo denoising,” in Proceedings - SIGGRAPH Asia 2024 Conference Papers,
Tokyo, Japan, 2024.
ista: 'Sakai H, Freude C, Auzinger T, Hahn D, Wimmer M. 2024. A statistical approach
to Monte Carlo denoising. Proceedings - SIGGRAPH Asia 2024 Conference Papers.
SA: SIGGRAPH Asia, 68.'
mla: Sakai, Hiroyuki, et al. “A Statistical Approach to Monte Carlo Denoising.”
Proceedings - SIGGRAPH Asia 2024 Conference Papers, 68, Association for
Computing Machinery, 2024, doi:10.1145/3680528.3687591.
short: H. Sakai, C. Freude, T. Auzinger, D. Hahn, M. Wimmer, in:, Proceedings -
SIGGRAPH Asia 2024 Conference Papers, Association for Computing Machinery, 2024.
conference:
end_date: 2024-12-06
location: Tokyo, Japan
name: 'SA: SIGGRAPH Asia'
start_date: 2024-12-03
date_created: 2025-02-16T23:02:34Z
date_published: 2024-12-03T00:00:00Z
date_updated: 2025-12-02T13:58:56Z
day: '03'
ddc:
- '000'
doi: 10.1145/3680528.3687591
external_id:
isi:
- '001441591200068'
file:
- access_level: open_access
checksum: 89f63b9237224362ec33430af9152700
content_type: application/pdf
creator: dernst
date_created: 2025-04-15T12:53:24Z
date_updated: 2025-04-15T12:53:24Z
file_id: '19563'
file_name: 2024_SIGGRAPH_Sakai.pdf
file_size: 14791980
relation: main_file
success: 1
file_date_updated: 2025-04-15T12:53:24Z
fulldoi: https://doi.org/10.1145/3680528.3687591
has_accepted_license: '1'
isi: 1
language:
- iso: eng
month: '12'
oa: 1
oa_version: Published Version
publication: Proceedings - SIGGRAPH Asia 2024 Conference Papers
publication_identifier:
isbn:
- '9798400711312'
publication_status: published
publisher: Association for Computing Machinery
quality_controlled: '1'
scopus_import: '1'
status: public
title: A statistical approach to Monte Carlo denoising
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: '2024'
...
---
OA_place: repository
OA_type: green
_id: '19307'
abstract:
- lang: eng
text: "This repository contains the data, scripts, SAM codes and files required
to reproduce the results of the manuscript \"The Unreasonable Efficiency of Total
Rain Evaporation Removal in Triggering Convective Self-Aggregation\" submitted
to the Geophysical Research Letters (GRL).\r\n\r\nBrief description of project:
This project aims to examine the impact of rain evaporation removal or reduction
in the planetary boundary layer (PBL) on convective self aggregation (CSA). Non-rotating
radiative-convective equilibrium (RCE) simulations were conducted with the System
for Atmospheric Modeling (SAM) cloud resolving model. Rain evaporation in the
lowest 1 km was progressively reduced and the effect on CSA was investigated.
The physical processes underlying this type of aggregation (referred to in the
manuscript as no-evaporation CSA, or NE-CSA) were analyzed and described. \r\nThe
default SAM code base (version 6.10.8) can be downloaded from here: http://rossby.msrc.sunysb.edu/~marat/SAM.html"
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: Caroline J
full_name: Muller, Caroline J
id: f978ccb0-3f7f-11eb-b193-b0e2bd13182b
last_name: Muller
orcid: 0000-0001-5836-5350
citation:
ama: Hwong Y-L, Muller CJ. Data - The unreasonable efficiency of total rain evaporation
removal in triggering convective self-aggregation. 2024. doi:10.5281/ZENODO.10687169
apa: Hwong, Y.-L., & Muller, C. J. (2024). Data - The unreasonable efficiency
of total rain evaporation removal in triggering convective self-aggregation. Zenodo.
https://doi.org/10.5281/ZENODO.10687169
chicago: Hwong, Yi-Ling, and Caroline J Muller. “Data - The Unreasonable Efficiency
of Total Rain Evaporation Removal in Triggering Convective Self-Aggregation.”
Zenodo, 2024. https://doi.org/10.5281/ZENODO.10687169.
ieee: Y.-L. Hwong and C. J. Muller, “Data - The unreasonable efficiency of total
rain evaporation removal in triggering convective self-aggregation.” Zenodo, 2024.
ista: Hwong Y-L, Muller CJ. 2024. Data - The unreasonable efficiency of total rain
evaporation removal in triggering convective self-aggregation, Zenodo, 10.5281/ZENODO.10687169.
mla: Hwong, Yi-Ling, and Caroline J. Muller. Data - The Unreasonable Efficiency
of Total Rain Evaporation Removal in Triggering Convective Self-Aggregation.
Zenodo, 2024, doi:10.5281/ZENODO.10687169.
short: Y.-L. Hwong, C.J. Muller, (2024).
corr_author: '1'
date_created: 2025-03-07T08:39:40Z
date_published: 2024-02-21T00:00:00Z
date_updated: 2025-09-04T13:16:39Z
day: '21'
ddc:
- '550'
department:
- _id: CaMu
doi: 10.5281/ZENODO.10687169
fulldoi: https://doi.org/10.5281/ZENODO.10687169
has_accepted_license: '1'
main_file_link:
- open_access: '1'
url: https://doi.org/10.5281/zenodo.8369509
month: '02'
oa: 1
oa_version: Published Version
publisher: Zenodo
related_material:
record:
- id: '15186'
relation: used_in_publication
status: public
status: public
title: Data - The unreasonable efficiency of total rain evaporation removal in triggering
convective self-aggregation
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: '2024'
...
---
OA_place: publisher
OA_type: diamond
_id: '19408'
abstract:
- lang: eng
text: 'Continual learning is a subfield of machine learning, which aims to allow
machine learning models to continuously learn on new data, by accumulating knowledge
without forgetting what was learned in the past. In this work, we take a step
back, and ask: "Why should one care about continual learning in the first place?".
We set the stage by examining recent continual learning papers published at four
major machine learning conferences, and show that memory-constrained settings
dominate the field. Then, we discuss five open problems in machine learning, and
even though they might seem unrelated to continual learning at first sight, we
show that continual learning will inevitably be part of their solution. These
problems are model editing, personalization and specialization, on-device learning,
faster (re-)training and reinforcement learning. Finally, by comparing the desiderata
from these unsolved problems and the current assumptions in continual learning,
we highlight and discuss four future directions for continual learning research.
We hope that this work offers an interesting perspective on the future of continual
learning, while displaying its potential value and the paths we have to pursue
in order to make it successful. This work is the result of the many discussions
the authors had at the Dagstuhl seminar on Deep Continual Learning, in March 2023.'
alternative_title:
- TMLR
article_processing_charge: No
article_type: original
arxiv: 1
author:
- first_name: Eli
full_name: Verwimp, Eli
last_name: Verwimp
- first_name: Rahaf
full_name: Aljundi, Rahaf
last_name: Aljundi
- first_name: Shai
full_name: Ben-David, Shai
last_name: Ben-David
- first_name: Matthias
full_name: Bethge, Matthias
last_name: Bethge
- first_name: Andrea
full_name: Cossu, Andrea
last_name: Cossu
- first_name: Alexander
full_name: Gepperth, Alexander
last_name: Gepperth
- first_name: Tyler L.
full_name: Hayes, Tyler L.
last_name: Hayes
- first_name: Eyke
full_name: Hüllermeier, Eyke
last_name: Hüllermeier
- first_name: Christopher
full_name: Kanan, Christopher
last_name: Kanan
- first_name: Dhireesha
full_name: Kudithipudi, Dhireesha
last_name: Kudithipudi
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
- first_name: Martin
full_name: Mundt, Martin
last_name: Mundt
- first_name: Razvan
full_name: Pascanu, Razvan
last_name: Pascanu
- first_name: Adrian
full_name: Popescu, Adrian
last_name: Popescu
- first_name: Andreas S.
full_name: Tolias, Andreas S.
last_name: Tolias
- first_name: Joost
full_name: Van De Weijer, Joost
last_name: Van De Weijer
- first_name: Bing
full_name: Liu, Bing
last_name: Liu
- first_name: Vincenzo
full_name: Lomonaco, Vincenzo
last_name: Lomonaco
- first_name: Tinne
full_name: Tuytelaars, Tinne
last_name: Tuytelaars
- first_name: Gido M.
full_name: Van De Ven, Gido M.
last_name: Van De Ven
citation:
ama: 'Verwimp E, Aljundi R, Ben-David S, et al. Continual learning: Applications
and the road forward. Transactions on Machine Learning Research. 2024;2024.'
apa: 'Verwimp, E., Aljundi, R., Ben-David, S., Bethge, M., Cossu, A., Gepperth,
A., … Van De Ven, G. M. (2024). Continual learning: Applications and the road
forward. Transactions on Machine Learning Research. Transactions on Machine
Learning Research.'
chicago: 'Verwimp, Eli, Rahaf Aljundi, Shai Ben-David, Matthias Bethge, Andrea Cossu,
Alexander Gepperth, Tyler L. Hayes, et al. “Continual Learning: Applications and
the Road Forward.” Transactions on Machine Learning Research. Transactions
on Machine Learning Research, 2024.'
ieee: 'E. Verwimp et al., “Continual learning: Applications and the road
forward,” Transactions on Machine Learning Research, vol. 2024. Transactions
on Machine Learning Research, 2024.'
ista: 'Verwimp E, Aljundi R, Ben-David S, Bethge M, Cossu A, Gepperth A, Hayes TL,
Hüllermeier E, Kanan C, Kudithipudi D, Lampert C, Mundt M, Pascanu R, Popescu
A, Tolias AS, Van De Weijer J, Liu B, Lomonaco V, Tuytelaars T, Van De Ven GM.
2024. Continual learning: Applications and the road forward. Transactions on Machine
Learning Research. 2024.'
mla: 'Verwimp, Eli, et al. “Continual Learning: Applications and the Road Forward.”
Transactions on Machine Learning Research, vol. 2024, Transactions on Machine
Learning Research, 2024.'
short: E. Verwimp, R. Aljundi, S. Ben-David, M. Bethge, A. Cossu, A. Gepperth, T.L.
Hayes, E. Hüllermeier, C. Kanan, D. Kudithipudi, C. Lampert, M. Mundt, R. Pascanu,
A. Popescu, A.S. Tolias, J. Van De Weijer, B. Liu, V. Lomonaco, T. Tuytelaars,
G.M. Van De Ven, Transactions on Machine Learning Research 2024 (2024).
date_created: 2025-03-16T23:01:25Z
date_published: 2024-04-12T00:00:00Z
date_updated: 2025-03-20T09:21:02Z
day: '12'
ddc:
- '000'
department:
- _id: ChLa
external_id:
arxiv:
- '2311.11908'
file:
- access_level: open_access
checksum: 0714e12f7423cd098976ed9974561155
content_type: application/pdf
creator: dernst
date_created: 2025-03-20T09:02:18Z
date_updated: 2025-03-20T09:02:18Z
file_id: '19426'
file_name: 2024_TMLR_Verwimp.pdf
file_size: 1367966
relation: main_file
success: 1
file_date_updated: 2025-03-20T09:02:18Z
has_accepted_license: '1'
intvolume: ' 2024'
language:
- iso: eng
month: '04'
oa: 1
oa_version: Published Version
publication: Transactions on Machine Learning Research
publication_identifier:
eissn:
- 2835-8856
publication_status: published
publisher: Transactions on Machine Learning Research
quality_controlled: '1'
scopus_import: '1'
status: public
title: 'Continual learning: Applications and the road forward'
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: 2024
year: '2024'
...
---
OA_place: repository
OA_type: green
_id: '19486'
abstract:
- lang: eng
text: Consider the family of elliptic curves En:y2=x3+n2, where n varies over positive
cubefree integers. There is a rational 3-isogeny ϕ from En to E^n:y2=x3−27n2 and
a dual isogeny ϕ^:E^n→En. We show that for almost all n, the rank of Selϕ(En)
is 0, and the rank of Selϕ^(E^n) is determined by the number of prime factors
of n that are congruent to 2mod3 and the congruence class of nmod9.
acknowledgement: The author would like to thank Peter Koymans and Carlo Pagano for
helpful discussions.
article_processing_charge: No
article_type: original
arxiv: 1
author:
- first_name: Yik Tung
full_name: Chan, Yik Tung
id: c4c0afc8-9262-11ed-9231-d8b0bc743af1
last_name: Chan
orcid: 0000-0001-8467-4106
citation:
ama: Chan S. The 3-isogeny selmer groups of the elliptic curves y2=x3+n2. International
Mathematics Research Notices. 2024;2024(9):7571-7593. doi:10.1093/imrn/rnad266
apa: Chan, S. (2024). The 3-isogeny selmer groups of the elliptic curves y2=x3+n2.
International Mathematics Research Notices. Oxford University Press. https://doi.org/10.1093/imrn/rnad266
chicago: Chan, Stephanie. “The 3-Isogeny Selmer Groups of the Elliptic Curves Y2=x3+n2.”
International Mathematics Research Notices. Oxford University Press, 2024.
https://doi.org/10.1093/imrn/rnad266.
ieee: S. Chan, “The 3-isogeny selmer groups of the elliptic curves y2=x3+n2,” International
Mathematics Research Notices, vol. 2024, no. 9. Oxford University Press, pp.
7571–7593, 2024.
ista: Chan S. 2024. The 3-isogeny selmer groups of the elliptic curves y2=x3+n2.
International Mathematics Research Notices. 2024(9), 7571–7593.
mla: Chan, Stephanie. “The 3-Isogeny Selmer Groups of the Elliptic Curves Y2=x3+n2.”
International Mathematics Research Notices, vol. 2024, no. 9, Oxford University
Press, 2024, pp. 7571–93, doi:10.1093/imrn/rnad266.
short: S. Chan, International Mathematics Research Notices 2024 (2024) 7571–7593.
date_created: 2025-04-05T10:50:33Z
date_published: 2024-05-01T00:00:00Z
date_updated: 2025-07-10T11:51:44Z
day: '01'
doi: 10.1093/imrn/rnad266
extern: '1'
external_id:
arxiv:
- '2211.06062'
fulldoi: https://doi.org/10.1093/imrn/rnad266
intvolume: ' 2024'
issue: '9'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2211.06062
month: '05'
oa: 1
oa_version: Preprint
page: 7571-7593
publication: International Mathematics Research Notices
publication_identifier:
eissn:
- 1687-0247
issn:
- 1073-7928
publication_status: published
publisher: Oxford University Press
quality_controlled: '1'
scopus_import: '1'
status: public
title: The 3-isogeny selmer groups of the elliptic curves y2=x3+n2
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 2024
year: '2024'
...
---
OA_place: repository
OA_type: green
_id: '19510'
abstract:
- lang: eng
text: "We propose a new variant of the Adam optimizer [Kingma and Ba, 2014] called\r\nMICROADAM
that specifically minimizes memory overheads, while maintaining\r\ntheoretical
convergence guarantees. We achieve this by compressing the gradient\r\ninformation
before it is fed into the optimizer state, thereby reducing its memory\r\nfootprint
significantly. We control the resulting compression error via a novel\r\ninstance
of the classical error feedback mechanism from distributed optimization [Seide
et al., 2014, Alistarh et al., 2018, Karimireddy et al., 2019] in which\r\nthe
error correction information is itself compressed to allow for practical memory\r\ngains.
We prove that the resulting approach maintains theoretical convergence\r\nguarantees
competitive to those of AMSGrad, while providing good practical performance. Specifically,
we show that MICROADAM can be implemented efficiently\r\non GPUs: on both million-scale
(BERT) and billion-scale (LLaMA) models, MICROADAM provides practical convergence
competitive to that of the uncompressed\r\nAdam baseline, with lower memory usage
and similar running time. Our code is\r\navailable at https://github.com/IST-DASLab/MicroAdam."
acknowledged_ssus:
- _id: CampIT
acknowledgement: The authors thank Razvan Pascanu, Mahdi Nikdan and Soroush Tabesh
for their valuable feedback, the IT department from Institute of Science and Technology
Austria for the hardware support and Weights and Biases for the infrastructure to
track all our experiments. Mher Safaryan has received funding from the European
Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie
grant agreement No 101034413.
alternative_title:
- Advances in Neural Information Processing Systems
article_processing_charge: No
arxiv: 1
author:
- first_name: Ionut-Vlad
full_name: Modoranu, Ionut-Vlad
id: 449f7a18-f128-11eb-9611-9b430c0c6333
last_name: Modoranu
- first_name: Mher
full_name: Safaryan, Mher
id: dd546b39-0804-11ed-9c55-ef075c39778d
last_name: Safaryan
- first_name: Grigory
full_name: Malinovsky, Grigory
last_name: Malinovsky
- first_name: Eldar
full_name: Kurtic, Eldar
id: 47beb3a5-07b5-11eb-9b87-b108ec578218
last_name: Kurtic
- first_name: Thomas
full_name: Robert, Thomas
id: de632733-1457-11f0-ae22-b5914b8c1c41
last_name: Robert
- first_name: Peter
full_name: Richtárik, Peter
last_name: Richtárik
- 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: 'Modoranu I-V, Safaryan M, Malinovsky G, et al. MICROADAM: Accurate adaptive
optimization with low space overhead and provable convergence. In: 38th Conference
on Neural Information Processing Systems. Vol 37. Neural Information Processing
Systems Foundation; 2024.'
apa: 'Modoranu, I.-V., Safaryan, M., Malinovsky, G., Kurtic, E., Robert, T., Richtárik,
P., & Alistarh, D.-A. (2024). MICROADAM: Accurate adaptive optimization with
low space overhead and provable convergence. In 38th Conference on Neural Information
Processing Systems (Vol. 37). Neural Information Processing Systems Foundation.'
chicago: 'Modoranu, Ionut-Vlad, Mher Safaryan, Grigory Malinovsky, Eldar Kurtic,
Thomas Robert, Peter Richtárik, and Dan-Adrian Alistarh. “MICROADAM: Accurate
Adaptive Optimization with Low Space Overhead and Provable Convergence.” In 38th
Conference on Neural Information Processing Systems, Vol. 37. Neural Information
Processing Systems Foundation, 2024.'
ieee: 'I.-V. Modoranu et al., “MICROADAM: Accurate adaptive optimization
with low space overhead and provable convergence,” in 38th Conference on Neural
Information Processing Systems, 2024, vol. 37.'
ista: 'Modoranu I-V, Safaryan M, Malinovsky G, Kurtic E, Robert T, Richtárik P,
Alistarh D-A. 2024. MICROADAM: Accurate adaptive optimization with low space overhead
and provable convergence. 38th Conference on Neural Information Processing Systems.
, Advances in Neural Information Processing Systems, vol. 37.'
mla: 'Modoranu, Ionut-Vlad, et al. “MICROADAM: Accurate Adaptive Optimization with
Low Space Overhead and Provable Convergence.” 38th Conference on Neural Information
Processing Systems, vol. 37, Neural Information Processing Systems Foundation,
2024.'
short: I.-V. Modoranu, M. Safaryan, G. Malinovsky, E. Kurtic, T. Robert, P. Richtárik,
D.-A. Alistarh, in:, 38th Conference on Neural Information Processing Systems,
Neural Information Processing Systems Foundation, 2024.
corr_author: '1'
date_created: 2025-04-06T22:01:32Z
date_published: 2024-12-20T00:00:00Z
date_updated: 2025-05-14T11:32:52Z
day: '20'
department:
- _id: DaAl
ec_funded: 1
external_id:
arxiv:
- '2405.15593'
intvolume: ' 37'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2405.15593
month: '12'
oa: 1
oa_version: Preprint
project:
- _id: fc2ed2f7-9c52-11eb-aca3-c01059dda49c
call_identifier: H2020
grant_number: '101034413'
name: 'IST-BRIDGE: International postdoctoral program'
publication: 38th Conference on Neural Information Processing Systems
publication_identifier:
issn:
- 1049-5258
publication_status: published
publisher: Neural Information Processing Systems Foundation
quality_controlled: '1'
related_material:
link:
- relation: software
url: https://github.com/IST-DASLab/MicroAdam
scopus_import: '1'
status: public
title: 'MICROADAM: Accurate adaptive optimization with low space overhead and provable
convergence'
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 37
year: '2024'
...
---
OA_place: repository
OA_type: green
_id: '19511'
abstract:
- lang: eng
text: We introduce QuaRot, a new Quantization scheme based on Rotations, which is
able to quantize LLMs end-to-end, including all weights, activations, and KV cache
in 4 bits. QuaRot rotates LLMs in a way that removes outliers from the hidden
state without changing the output, making quantization easier. This computational
invariance is applied to the hidden state (residual) of the LLM, as well as to
the activations of the feed-forward components, aspects of the attention mechanism,
and to the KV cache. The result is a quantized model where all matrix multiplications
are performed in 4 bits, without any channels identified for retention in higher
precision. Our 4-bit quantized LLAMA2-70B model has losses of at most 0.47 WikiText-2
perplexity and retains 99% of the zero-shot performance. We also show that QuaRot
can provide lossless 6 and 8 bit LLAMA-2 models without any calibration data using
round-to-nearest quantization. Code is available at github.com/spcl/QuaRot.
alternative_title:
- Advances in Neural Information Processing Systems
article_processing_charge: No
arxiv: 1
author:
- first_name: Saleh
full_name: Ashkboos, Saleh
last_name: Ashkboos
- first_name: Amirkeivan
full_name: Mohtashami, Amirkeivan
last_name: Mohtashami
- first_name: Maximilian L.
full_name: Croci, Maximilian L.
last_name: Croci
- first_name: Bo
full_name: Li, Bo
last_name: Li
- first_name: Pashmina
full_name: Cameron, Pashmina
last_name: Cameron
- first_name: Martin
full_name: Jaggi, Martin
last_name: Jaggi
- first_name: Dan-Adrian
full_name: Alistarh, Dan-Adrian
id: 4A899BFC-F248-11E8-B48F-1D18A9856A87
last_name: Alistarh
orcid: 0000-0003-3650-940X
- first_name: Torsten
full_name: Hoefler, Torsten
last_name: Hoefler
- first_name: James
full_name: Hensman, James
last_name: Hensman
citation:
ama: 'Ashkboos S, Mohtashami A, Croci ML, et al. QuaRot: Outlier-free 4-bit inference
in rotated LLMs. In: 38th Conference on Neural Information Processing Systems.
Vol 37. Neural Information Processing Systems Foundation; 2024.'
apa: 'Ashkboos, S., Mohtashami, A., Croci, M. L., Li, B., Cameron, P., Jaggi, M.,
… Hensman, J. (2024). QuaRot: Outlier-free 4-bit inference in rotated LLMs. In
38th Conference on Neural Information Processing Systems (Vol. 37). Vancouver,
Canada: Neural Information Processing Systems Foundation.'
chicago: 'Ashkboos, Saleh, Amirkeivan Mohtashami, Maximilian L. Croci, Bo Li, Pashmina
Cameron, Martin Jaggi, Dan-Adrian Alistarh, Torsten Hoefler, and James Hensman.
“QuaRot: Outlier-Free 4-Bit Inference in Rotated LLMs.” In 38th Conference
on Neural Information Processing Systems, Vol. 37. Neural Information Processing
Systems Foundation, 2024.'
ieee: 'S. Ashkboos et al., “QuaRot: Outlier-free 4-bit inference in rotated
LLMs,” in 38th Conference on Neural Information Processing Systems, Vancouver,
Canada, 2024, vol. 37.'
ista: 'Ashkboos S, Mohtashami A, Croci ML, Li B, Cameron P, Jaggi M, Alistarh D-A,
Hoefler T, Hensman J. 2024. QuaRot: Outlier-free 4-bit inference in rotated LLMs.
38th Conference on Neural Information Processing Systems. NeurIPS: Neural Information
Processing Systems, Advances in Neural Information Processing Systems, vol. 37.'
mla: 'Ashkboos, Saleh, et al. “QuaRot: Outlier-Free 4-Bit Inference in Rotated LLMs.”
38th Conference on Neural Information Processing Systems, vol. 37, Neural
Information Processing Systems Foundation, 2024.'
short: S. Ashkboos, A. Mohtashami, M.L. Croci, B. Li, P. Cameron, M. Jaggi, D.-A.
Alistarh, T. Hoefler, J. Hensman, in:, 38th Conference on Neural Information Processing
Systems, Neural Information Processing Systems Foundation, 2024.
conference:
end_date: 2024-12-15
location: Vancouver, Canada
name: 'NeurIPS: Neural Information Processing Systems'
start_date: 2024-12-09
date_created: 2025-04-06T22:01:32Z
date_published: 2024-12-20T00:00:00Z
date_updated: 2025-05-14T11:33:12Z
day: '20'
department:
- _id: DaAl
external_id:
arxiv:
- '2404.00456'
intvolume: ' 37'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2404.00456
month: '12'
oa: 1
oa_version: Preprint
publication: 38th Conference on Neural Information Processing Systems
publication_identifier:
issn:
- 1049-5258
publication_status: published
publisher: Neural Information Processing Systems Foundation
quality_controlled: '1'
related_material:
link:
- relation: software
url: https://github.com/spcl/QuaRot
scopus_import: '1'
status: public
title: 'QuaRot: Outlier-free 4-bit inference in rotated LLMs'
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 37
year: '2024'
...
---
OA_place: repository
OA_type: green
_id: '19512'
abstract:
- lang: eng
text: "Differential privacy with gradual expiration models the setting where data
items\r\narrive in a stream and at a given time t the privacy loss guaranteed
for a data item\r\nseen at time (t − d) is εg(d), where g is a monotonically non-decreasing
function.\r\nWe study the fundamental continual (binary) counting problem where
each data\r\nitem consists of a bit, and the algorithm needs to output at each
time step the sum of\r\nall the bits streamed so far. For a stream of length T
and privacy without expiration\r\ncontinual counting is possible with maximum
(over all time steps) additive error\r\nO(log2\r\n(T)/ε) and the best known lower
bound is Ω(log(T)/ε); closing this gap\r\nis a challenging open problem.\r\nWe
show that the situation is very different for privacy with gradual expiration
by\r\ngiving upper and lower bounds for a large set of expiration functions g.
Specifically,\r\nour algorithm achieves an additive error of O(log(T)/ε) for a
large set of privacy\r\nexpiration functions. We also give a lower bound that
shows that if C is the additive\r\nerror of any ε-DP algorithm for this problem,
then the product of C and the privacy\r\nexpiration function after 2C steps must
be Ω(log(T)/ε). Our algorithm matches\r\nthis lower bound as its additive error
is O(log(T)/ε), even when g(2C) = O(1).\r\nOur empirical evaluation shows that
we achieve a slowly growing privacy loss\r\nwith significantly smaller empirical
privacy loss for large values of d than a natural\r\nbaseline algorithm."
acknowledgement: 'Monika Henzinger: This project has received funding from the European
Research Council (ERC) under the European Union’s Horizon 2020 research and innovation
programme (Grant agreement No. 101019564) and the Austrian Science Fund (FWF) grant
DOI 10.55776/Z422, grant DOI 10.55776/I5982, and grant DOI 10.55776/P33775 with
additional funding from the netidee SCIENCE Stiftung, 2020–2024. Joel Daniel Andersson
and Rasmus Pagh are affiliated with Basic Algorithms Research Copenhagen (BARC),
supported by the VILLUM Foundation grant 16582, and are also supported by Providentia,
a Data Science Distinguished Investigator grant from Novo Nordisk Fonden. Teresa
Anna Steiner is supported by a research grant (VIL51463) from VILLUM FONDEN. This
work was done while Teresa Anna Steiner was a Postdoc at the Technical University
of Denmark. Jalaj Upadhyay’s research was funded by the Rutgers Decanal Grant no.
302918 and an unrestricted gift from Google.'
alternative_title:
- Advances in Neural Information Processing Systems
article_processing_charge: No
arxiv: 1
author:
- first_name: Joel Daniel
full_name: Andersson, Joel Daniel
last_name: Andersson
- first_name: Monika H
full_name: Henzinger, Monika H
id: 540c9bbd-f2de-11ec-812d-d04a5be85630
last_name: Henzinger
orcid: 0000-0002-5008-6530
- first_name: Rasmus
full_name: Pagh, Rasmus
last_name: Pagh
- first_name: Teresa Anna
full_name: Steiner, Teresa Anna
last_name: Steiner
- first_name: Jalaj
full_name: Upadhyay, Jalaj
last_name: Upadhyay
citation:
ama: 'Andersson JD, Henzinger M, Pagh R, Steiner TA, Upadhyay J. Continual counting
with gradual privacy expiration. In: 38th Conference on Neural Information
Processing Systems. Vol 37. Neural Information Processing Systems Foundation;
2024.'
apa: 'Andersson, J. D., Henzinger, M., Pagh, R., Steiner, T. A., & Upadhyay,
J. (2024). Continual counting with gradual privacy expiration. In 38th Conference
on Neural Information Processing Systems (Vol. 37). Vancouver, Canada: Neural
Information Processing Systems Foundation.'
chicago: Andersson, Joel Daniel, Monika Henzinger, Rasmus Pagh, Teresa Anna Steiner,
and Jalaj Upadhyay. “Continual Counting with Gradual Privacy Expiration.” In 38th
Conference on Neural Information Processing Systems, Vol. 37. Neural Information
Processing Systems Foundation, 2024.
ieee: J. D. Andersson, M. Henzinger, R. Pagh, T. A. Steiner, and J. Upadhyay, “Continual
counting with gradual privacy expiration,” in 38th Conference on Neural Information
Processing Systems, Vancouver, Canada, 2024, vol. 37.
ista: 'Andersson JD, Henzinger M, Pagh R, Steiner TA, Upadhyay J. 2024. Continual
counting with gradual privacy expiration. 38th Conference on Neural Information
Processing Systems. NeurIPS: Neural Information Processing Systems, Advances in
Neural Information Processing Systems, vol. 37.'
mla: Andersson, Joel Daniel, et al. “Continual Counting with Gradual Privacy Expiration.”
38th Conference on Neural Information Processing Systems, vol. 37, Neural
Information Processing Systems Foundation, 2024.
short: J.D. Andersson, M. Henzinger, R. Pagh, T.A. Steiner, J. Upadhyay, in:, 38th
Conference on Neural Information Processing Systems, Neural Information Processing
Systems Foundation, 2024.
conference:
end_date: 2024-12-15
location: Vancouver, Canada
name: 'NeurIPS: Neural Information Processing Systems'
start_date: 2024-12-09
corr_author: '1'
date_created: 2025-04-06T22:01:32Z
date_published: 2024-12-20T00:00:00Z
date_updated: 2025-05-14T11:33:22Z
day: '20'
department:
- _id: MoHe
ec_funded: 1
external_id:
arxiv:
- '2406.03802'
intvolume: ' 37'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2406.03802
month: '12'
oa: 1
oa_version: Preprint
project:
- _id: bd9ca328-d553-11ed-ba76-dc4f890cfe62
call_identifier: H2020
grant_number: '101019564'
name: The design and evaluation of modern fully dynamic data structures
- _id: 34def286-11ca-11ed-8bc3-da5948e1613c
grant_number: Z00422
name: Efficient algorithms
- _id: bda196b2-d553-11ed-ba76-8e8ee6c21103
grant_number: I05982
name: Static and Dynamic Hierarchical Graph Decompositions
- _id: bd9e3a2e-d553-11ed-ba76-8aa684ce17fe
grant_number: P33775
name: Fast Algorithms for a Reactive Network Layer
publication: 38th Conference on Neural Information Processing Systems
publication_identifier:
issn:
- 1049-5258
publication_status: published
publisher: Neural Information Processing Systems Foundation
quality_controlled: '1'
scopus_import: '1'
status: public
title: Continual counting with gradual privacy expiration
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 37
year: '2024'
...
---
OA_place: repository
OA_type: green
_id: '19515'
abstract:
- lang: eng
text: "Neural models learn data representations that lie on low-dimensional manifolds,\r\nyet
modeling the relation between these representational spaces is an ongoing challenge.
By integrating spectral geometry principles into neural modeling, we show\r\nthat
this problem can be better addressed in the functional domain, mitigating complexity,
while enhancing interpretability and performances on downstream tasks.\r\nTo this
end, we introduce a multi-purpose framework to the representation learning\r\ncommunity,
which allows to: (i) compare different spaces in an interpretable way\r\nand measure
their intrinsic similarity; (ii) find correspondences between them, both\r\nin
unsupervised and weakly supervised settings, and (iii) to effectively transfer\r\nrepresentations
between distinct spaces. We validate our framework on various\r\napplications,
ranging from stitching to retrieval tasks, and on multiple modalities,\r\ndemonstrating
that Latent Functional Maps can serve as a swiss-army knife for\r\nrepresentation
alignment"
acknowledgement: MF is supported by the MSCA IST-Bridge fellowship which has received
funding from the European Union’s Horizon 2020 research and innovation program under
the Marie Skłodowska-Curie grant agreement No 101034413. ER and VM are supported
by the PNRR MUR project PE0000013-FAIR. MP is supported by the Sapienza grant "Predicting
and Explaining Clinical Trial Outcomes", prot. RG12218166FA3F13.
alternative_title:
- Advances in Neural Information Processing Systems
article_processing_charge: No
arxiv: 1
author:
- first_name: Marco
full_name: Fumero, Marco
id: 1c1593eb-393f-11ef-bb8e-ab4f1e979650
last_name: Fumero
- first_name: Marco
full_name: Pegoraro, Marco
last_name: Pegoraro
- first_name: Valentino
full_name: Maiorca, Valentino
last_name: Maiorca
- first_name: Francesco
full_name: Locatello, Francesco
id: 26cfd52f-2483-11ee-8040-88983bcc06d4
last_name: Locatello
orcid: 0000-0002-4850-0683
- first_name: Emanuele
full_name: Rodolà, Emanuele
last_name: Rodolà
citation:
ama: 'Fumero M, Pegoraro M, Maiorca V, Locatello F, Rodolà E. Latent functional
maps: A spectral framework for representation alignment. In: 38th Conference
on Neural Information Processing Systems. Vol 37. Neural Information Processing
Systems Foundation; 2024.'
apa: 'Fumero, M., Pegoraro, M., Maiorca, V., Locatello, F., & Rodolà, E. (2024).
Latent functional maps: A spectral framework for representation alignment. In
38th Conference on Neural Information Processing Systems (Vol. 37). Vancouver,
Canada: Neural Information Processing Systems Foundation.'
chicago: 'Fumero, Marco, Marco Pegoraro, Valentino Maiorca, Francesco Locatello,
and Emanuele Rodolà. “Latent Functional Maps: A Spectral Framework for Representation
Alignment.” In 38th Conference on Neural Information Processing Systems,
Vol. 37. Neural Information Processing Systems Foundation, 2024.'
ieee: 'M. Fumero, M. Pegoraro, V. Maiorca, F. Locatello, and E. Rodolà, “Latent
functional maps: A spectral framework for representation alignment,” in 38th
Conference on Neural Information Processing Systems, Vancouver, Canada, 2024,
vol. 37.'
ista: 'Fumero M, Pegoraro M, Maiorca V, Locatello F, Rodolà E. 2024. Latent functional
maps: A spectral framework for representation alignment. 38th Conference on Neural
Information Processing Systems. NeurIPS: Neural Information Processing Systems,
Advances in Neural Information Processing Systems, vol. 37.'
mla: 'Fumero, Marco, et al. “Latent Functional Maps: A Spectral Framework for Representation
Alignment.” 38th Conference on Neural Information Processing Systems, vol.
37, Neural Information Processing Systems Foundation, 2024.'
short: M. Fumero, M. Pegoraro, V. Maiorca, F. Locatello, E. Rodolà, in:, 38th Conference
on Neural Information Processing Systems, Neural Information Processing Systems
Foundation, 2024.
conference:
end_date: 2024-12-15
location: Vancouver, Canada
name: 'NeurIPS: Neural Information Processing Systems'
start_date: 2024-12-09
corr_author: '1'
date_created: 2025-04-06T22:01:32Z
date_published: 2024-12-20T00:00:00Z
date_updated: 2025-05-14T11:36:51Z
day: '20'
department:
- _id: FrLo
ec_funded: 1
external_id:
arxiv:
- '2406.14183'
intvolume: ' 37'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2406.14183
month: '12'
oa: 1
oa_version: Preprint
project:
- _id: fc2ed2f7-9c52-11eb-aca3-c01059dda49c
call_identifier: H2020
grant_number: '101034413'
name: 'IST-BRIDGE: International postdoctoral program'
publication: 38th Conference on Neural Information Processing Systems
publication_identifier:
issn:
- 1049-5258
publication_status: published
publisher: Neural Information Processing Systems Foundation
quality_controlled: '1'
scopus_import: '1'
status: public
title: 'Latent functional maps: A spectral framework for representation alignment'
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 37
year: '2024'
...
---
OA_place: repository
OA_type: green
_id: '19517'
abstract:
- lang: eng
text: "In this paper, we present a novel data-free method for merging neural networks
in weight space. Differently from most existing works, our method optimizes for
the permutations of network neurons globally across all layers. This allows us
to enforce cycle consistency of the permutations when merging n ≥ 3 models, allowing
circular compositions of permutations to be computed without accumulating error
along the path. We qualitatively and quantitatively motivate the need for such
a constraint, showing its benefits when merging sets of models in scenarios spanning
varying architectures and datasets. We finally show that, when coupled\r\nwith
activation renormalization, our approach yields the best results in the task."
acknowledgement: "This work is supported by the ERC grant no.802554 (SPECGEO), PRIN
2020 project\r\nno.2020TA3K9N (LEGO.AI), and PNRR MUR project PE0000013-FAIR. Marco
Fumero is supported by the MSCA IST-Bridge fellowship which has received funding
from the European Union’s Horizon 2020 research and innovation program under the
Marie Skłodowska-Curie grant agreement No 101034413. We thank Simone Scardapane
for the helpful feedback on the paper."
alternative_title:
- Advances in Neural Information Processing Systems
article_processing_charge: No
arxiv: 1
author:
- first_name: Donato
full_name: Crisostomi, Donato
last_name: Crisostomi
- first_name: Marco
full_name: Fumero, Marco
id: 1c1593eb-393f-11ef-bb8e-ab4f1e979650
last_name: Fumero
- first_name: Daniele
full_name: Baieri, Daniele
last_name: Baieri
- first_name: Florian
full_name: Bernard, Florian
last_name: Bernard
- first_name: Emanuele
full_name: Rodolà, Emanuele
last_name: Rodolà
citation:
ama: 'Crisostomi D, Fumero M, Baieri D, Bernard F, Rodolà E. C2M3: Cycle-consistent
multi-model merging. In: 38th Conference on Neural Information Processing Systems.
Vol 37. Neural Information Processing Systems Foundation; 2024.'
apa: 'Crisostomi, D., Fumero, M., Baieri, D., Bernard, F., & Rodolà, E. (2024).
C2M3: Cycle-consistent multi-model merging. In 38th Conference on Neural Information
Processing Systems (Vol. 37). Vancouver, Canada: Neural Information Processing
Systems Foundation.'
chicago: 'Crisostomi, Donato, Marco Fumero, Daniele Baieri, Florian Bernard, and
Emanuele Rodolà. “C2M3: Cycle-Consistent Multi-Model Merging.” In 38th Conference
on Neural Information Processing Systems, Vol. 37. Neural Information Processing
Systems Foundation, 2024.'
ieee: 'D. Crisostomi, M. Fumero, D. Baieri, F. Bernard, and E. Rodolà, “C2M3: Cycle-consistent
multi-model merging,” in 38th Conference on Neural Information Processing Systems,
Vancouver, Canada, 2024, vol. 37.'
ista: 'Crisostomi D, Fumero M, Baieri D, Bernard F, Rodolà E. 2024. C2M3: Cycle-consistent
multi-model merging. 38th Conference on Neural Information Processing Systems.
NeurIPS: Neural Information Processing Systems, Advances in Neural Information
Processing Systems, vol. 37.'
mla: 'Crisostomi, Donato, et al. “C2M3: Cycle-Consistent Multi-Model Merging.” 38th
Conference on Neural Information Processing Systems, vol. 37, Neural Information
Processing Systems Foundation, 2024.'
short: D. Crisostomi, M. Fumero, D. Baieri, F. Bernard, E. Rodolà, in:, 38th Conference
on Neural Information Processing Systems, Neural Information Processing Systems
Foundation, 2024.
conference:
end_date: 2024-12-15
location: Vancouver, Canada
name: 'NeurIPS: Neural Information Processing Systems'
start_date: 2024-12-09
corr_author: '1'
date_created: 2025-04-06T22:01:32Z
date_published: 2024-12-20T00:00:00Z
date_updated: 2025-05-14T11:36:59Z
day: '20'
department:
- _id: FrLo
ec_funded: 1
external_id:
arxiv:
- '2405.17897'
intvolume: ' 37'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2405.17897
month: '12'
oa: 1
oa_version: Preprint
project:
- _id: fc2ed2f7-9c52-11eb-aca3-c01059dda49c
call_identifier: H2020
grant_number: '101034413'
name: 'IST-BRIDGE: International postdoctoral program'
publication: 38th Conference on Neural Information Processing Systems
publication_identifier:
issn:
- 1049-5258
publication_status: published
publisher: Neural Information Processing Systems Foundation
quality_controlled: '1'
scopus_import: '1'
status: public
title: 'C2M3: Cycle-consistent multi-model merging'
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 37
year: '2024'
...
---
OA_place: repository
OA_type: green
_id: '19518'
abstract:
- lang: eng
text: "The rising footprint of machine learning has led to a focus on imposing model\r\nsparsity
as a means of reducing computational and memory costs. For deep neural\r\nnetworks
(DNNs), the state-of-the-art accuracy-vs-sparsity is achieved by heuristics\r\ninspired
by the classical Optimal Brain Surgeon (OBS) framework [LeCun et al.,\r\n1989,
Hassibi and Stork, 1992, Hassibi et al., 1993], which leverages loss curvature\r\ninformation
to make better pruning decisions. Yet, these results still lack a solid\r\ntheoretical
understanding, and it is unclear whether they can be improved by\r\nleveraging
connections to the wealth of work on sparse recovery algorithms. In this\r\npaper,
we draw new connections between these two areas and present new sparse\r\nrecovery
algorithms inspired by the OBS framework that comes with theoretical\r\nguarantees
under reasonable assumptions and have strong practical performance.\r\nSpecifically,
our work starts from the observation that we can leverage curvature\r\ninformation
in OBS-like fashion upon the projection step of classic iterative sparse\r\nrecovery
algorithms such as IHT. We show for the first time that this leads both\r\nto
improved convergence bounds under standard assumptions. Furthermore, we\r\npresent
extensions of this approach to the practical task of obtaining accurate sparse\r\nDNNs,
and validate it experimentally at scale for Transformer-based models on\r\nvision
and language tasks."
acknowledged_ssus:
- _id: CampIT
acknowledgement: The authors thank the anonymous NeurIPS reviewers for their useful
comments and feedback, the IT department from the Institute of Science and Technology
Austria for the hardware support, and Weights and Biases for the infrastructure
to track all our experiments. Mher Safaryan has received funding from the European
Union’s Horizon 2020 research and innovation program under the Maria Skłodowska-Curie
grant agreement No 101034413.
alternative_title:
- Advances in Neural Information Processing Systems
article_processing_charge: No
arxiv: 1
author:
- first_name: Diyuan
full_name: Wu, Diyuan
id: 1a5914c2-896a-11ed-bdf8-fb80621a0635
last_name: Wu
- first_name: Ionut-Vlad
full_name: Modoranu, Ionut-Vlad
id: 449f7a18-f128-11eb-9611-9b430c0c6333
last_name: Modoranu
- first_name: Mher
full_name: Safaryan, Mher
id: dd546b39-0804-11ed-9c55-ef075c39778d
last_name: Safaryan
- first_name: Denis
full_name: Kuznedelev, Denis
last_name: Kuznedelev
- 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: 'Wu D, Modoranu I-V, Safaryan M, Kuznedelev D, Alistarh D-A. The iterative
optimal brain surgeon: Faster sparse recovery by leveraging second-order information.
In: 38th Conference on Neural Information Processing Systems. Vol 37. Neural
Information Processing Systems Foundation; 2024.'
apa: 'Wu, D., Modoranu, I.-V., Safaryan, M., Kuznedelev, D., & Alistarh, D.-A.
(2024). The iterative optimal brain surgeon: Faster sparse recovery by leveraging
second-order information. In 38th Conference on Neural Information Processing
Systems (Vol. 37). Vancouver, Canada: Neural Information Processing Systems
Foundation.'
chicago: 'Wu, Diyuan, Ionut-Vlad Modoranu, Mher Safaryan, Denis Kuznedelev, and
Dan-Adrian Alistarh. “The Iterative Optimal Brain Surgeon: Faster Sparse Recovery
by Leveraging Second-Order Information.” In 38th Conference on Neural Information
Processing Systems, Vol. 37. Neural Information Processing Systems Foundation,
2024.'
ieee: 'D. Wu, I.-V. Modoranu, M. Safaryan, D. Kuznedelev, and D.-A. Alistarh, “The
iterative optimal brain surgeon: Faster sparse recovery by leveraging second-order
information,” in 38th Conference on Neural Information Processing Systems,
Vancouver, Canada, 2024, vol. 37.'
ista: 'Wu D, Modoranu I-V, Safaryan M, Kuznedelev D, Alistarh D-A. 2024. The iterative
optimal brain surgeon: Faster sparse recovery by leveraging second-order information.
38th Conference on Neural Information Processing Systems. NeurIPS: Neural Information
Processing Systems, Advances in Neural Information Processing Systems, vol. 37.'
mla: 'Wu, Diyuan, et al. “The Iterative Optimal Brain Surgeon: Faster Sparse Recovery
by Leveraging Second-Order Information.” 38th Conference on Neural Information
Processing Systems, vol. 37, Neural Information Processing Systems Foundation,
2024.'
short: D. Wu, I.-V. Modoranu, M. Safaryan, D. Kuznedelev, D.-A. Alistarh, in:, 38th
Conference on Neural Information Processing Systems, Neural Information Processing
Systems Foundation, 2024.
conference:
end_date: 2024-12-15
location: Vancouver, Canada
name: 'NeurIPS: Neural Information Processing Systems'
start_date: 2024-12-09
corr_author: '1'
date_created: 2025-04-06T22:01:32Z
date_published: 2024-12-20T00:00:00Z
date_updated: 2025-05-14T11:37:10Z
day: '20'
department:
- _id: DaAl
- _id: MaMo
ec_funded: 1
external_id:
arxiv:
- '2408.17163'
intvolume: ' 37'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2408.17163
month: '12'
oa: 1
oa_version: Preprint
project:
- _id: fc2ed2f7-9c52-11eb-aca3-c01059dda49c
call_identifier: H2020
grant_number: '101034413'
name: 'IST-BRIDGE: International postdoctoral program'
publication: 38th Conference on Neural Information Processing Systems
publication_identifier:
issn:
- 1049-5258
publication_status: published
publisher: Neural Information Processing Systems Foundation
quality_controlled: '1'
scopus_import: '1'
status: public
title: 'The iterative optimal brain surgeon: Faster sparse recovery by leveraging
second-order information'
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 37
year: '2024'
...
---
OA_place: publisher
OA_type: gold
_id: '19519'
abstract:
- lang: eng
text: There has been significant interest in "extreme" compression of large language
models (LLMs), i.e. to 1-2 bits per parameter, which allows such models to be
executed efficiently on resource-constrained devices. Existing work focused on
improved one-shot quantization techniques and weight representations; yet, purely
post-training approaches are reaching diminishing returns in terms of the accuracy-vs-bit-width
trade-off. State-of-the-art quantization methods such as QuIP# and AQLM include
fine-tuning (part of) the compressed parameters over a limited amount of calibration
data; however, such fine-tuning techniques over compressed weights often make
exclusive use of straight-through estimators (STE), whose performance is not well-understood
in this setting. In this work, we question the use of STE for extreme LLM compression,
showing that it can be sub-optimal, and perform a systematic study of quantization-aware
fine-tuning strategies for LLMs.We propose PV-Tuning - a representation-agnostic
framework that generalizes and improves upon existing fine-tuning strategies,
and provides convergence guarantees in restricted cases.On the practical side,
when used for 1-2 bit vector quantization, PV-Tuning outperforms prior techniques
for highly-performant models such as Llama and Mistral. Using PV-Tuning, we achieve
the first Pareto-optimal quantization for Llama-2 family models at 2 bits per
parameter.
acknowledgement: "Authors would like to thank Vage Egiazarian, Andrei Panferov and
Ruslan Svirschevski for their\r\nhelp and advice on AQLM codebase and running large-scale
experiments. We also thank Philip\r\nZmushko and Artem Fedorov for helpful discussions
during the early stages of our research. The research of Kai Yi, Konstantin Burlachenko,
and Peter Richtárik reported in this publication was supported by funding from King
Abdullah University of Science and Technology (KAUST) – Center of Excellence for
Generative AI, under award number 5940. We would also like to thank our NeurIPS
reviewers for their helpful suggestions, we specifically highlight p3Lv’s suggestions
to consider smaller codebook sizes and evaluate PV-Tuning with QuIP#, both of which
produced interesting findings. Finally, we thank the open-source contributors from
llama.cpp9 and the LocalLlama10 community for discussions and inspirations on practical
use cases of quantized language models, and in particular, Yalda Shabanzadeh and
Arthur Aardvark for their help with improving the codebase."
alternative_title:
- Advances in Neural Information Processing Systems
article_processing_charge: No
arxiv: 1
author:
- first_name: Vladimir
full_name: Malinovskii, Vladimir
last_name: Malinovskii
- first_name: Denis
full_name: Mazur, Denis
last_name: Mazur
- first_name: Ivan
full_name: Ilin, Ivan
last_name: Ilin
- first_name: Denis
full_name: Kuznedelev, Denis
last_name: Kuznedelev
- first_name: Konstantin
full_name: Burlachenko, Konstantin
last_name: Burlachenko
- first_name: Kai
full_name: Yi, Kai
last_name: Yi
- first_name: Dan-Adrian
full_name: Alistarh, Dan-Adrian
id: 4A899BFC-F248-11E8-B48F-1D18A9856A87
last_name: Alistarh
orcid: 0000-0003-3650-940X
- first_name: Peter
full_name: Richtarik, Peter
last_name: Richtarik
citation:
ama: 'Malinovskii V, Mazur D, Ilin I, et al. PV-tuning: Beyond straight-through
estimation for extreme LLM compression. In: 38th Conference on Neural Information
Processing Systems. Vol 37. Neural Information Processing Systems Foundation;
2024.'
apa: 'Malinovskii, V., Mazur, D., Ilin, I., Kuznedelev, D., Burlachenko, K., Yi,
K., … Richtarik, P. (2024). PV-tuning: Beyond straight-through estimation for
extreme LLM compression. In 38th Conference on Neural Information Processing
Systems (Vol. 37). Vancouver, Canada: Neural Information Processing Systems
Foundation.'
chicago: 'Malinovskii, Vladimir, Denis Mazur, Ivan Ilin, Denis Kuznedelev, Konstantin
Burlachenko, Kai Yi, Dan-Adrian Alistarh, and Peter Richtarik. “PV-Tuning: Beyond
Straight-through Estimation for Extreme LLM Compression.” In 38th Conference
on Neural Information Processing Systems, Vol. 37. Neural Information Processing
Systems Foundation, 2024.'
ieee: 'V. Malinovskii et al., “PV-tuning: Beyond straight-through estimation
for extreme LLM compression,” in 38th Conference on Neural Information Processing
Systems, Vancouver, Canada, 2024, vol. 37.'
ista: 'Malinovskii V, Mazur D, Ilin I, Kuznedelev D, Burlachenko K, Yi K, Alistarh
D-A, Richtarik P. 2024. PV-tuning: Beyond straight-through estimation for extreme
LLM compression. 38th Conference on Neural Information Processing Systems. NeurIPS:
Neural Information Processing Systems, Advances in Neural Information Processing
Systems, vol. 37.'
mla: 'Malinovskii, Vladimir, et al. “PV-Tuning: Beyond Straight-through Estimation
for Extreme LLM Compression.” 38th Conference on Neural Information Processing
Systems, vol. 37, Neural Information Processing Systems Foundation, 2024.'
short: V. Malinovskii, D. Mazur, I. Ilin, D. Kuznedelev, K. Burlachenko, K. Yi,
D.-A. Alistarh, P. Richtarik, in:, 38th Conference on Neural Information Processing
Systems, Neural Information Processing Systems Foundation, 2024.
conference:
end_date: 2024-12-15
location: Vancouver, Canada
name: 'NeurIPS: Neural Information Processing Systems'
start_date: 2024-12-10
date_created: 2025-04-06T22:01:32Z
date_published: 2024-12-20T00:00:00Z
date_updated: 2025-05-14T10:49:20Z
day: '20'
ddc:
- '000'
department:
- _id: DaAl
external_id:
arxiv:
- '2405.14852'
file:
- access_level: open_access
checksum: 54d36f947887e26d0e568b512167001a
content_type: application/pdf
creator: dernst
date_created: 2025-04-07T09:17:10Z
date_updated: 2025-04-07T09:17:10Z
file_id: '19521'
file_name: 2024_NeurIPS_Malinovskii.pdf
file_size: 939712
relation: main_file
success: 1
file_date_updated: 2025-04-07T09:17:10Z
has_accepted_license: '1'
intvolume: ' 37'
language:
- iso: eng
month: '12'
oa: 1
oa_version: Published Version
publication: 38th Conference on Neural Information Processing Systems
publication_identifier:
isbn:
- '9798331314385'
issn:
- 1049-5258
publication_status: published
publisher: Neural Information Processing Systems Foundation
quality_controlled: '1'
scopus_import: '1'
status: public
title: 'PV-tuning: Beyond straight-through estimation for extreme LLM compression'
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 37
year: '2024'
...
---
OA_place: repository
OA_type: green
_id: '19520'
abstract:
- lang: eng
text: Vertebrates exhibit a wide range of motor behaviors, ranging from swimming
to complex limb-based movements. Here we take advantage of frog metamorphosis,
which captures a swim-to-limb-based movement transformation during the development
of a single organism, to explore changes in the underlying spinal circuits. We
find that the tadpole spinal cord contains small and largely homogeneous populations
of motor neurons (MNs) and V1 interneurons (V1s) at early escape swimming stages.
These neuronal populations only modestly increase in number and subtype heterogeneity
with the emergence of free swimming. In contrast, during frog metamorphosis and
the emergence of limb movement, there is a dramatic expansion of MN and V1 interneuron
number and transcriptional heterogeneity, culminating in cohorts of neurons that
exhibit striking molecular similarity to mammalian motor circuits. CRISPR/Cas9-mediated
gene disruption of the limb MN and V1 determinants FoxP1 and Engrailed-1, respectively,
results in severe but selective deficits in tail and limb function. Our work thus
demonstrates that neural diversity scales exponentially with increasing behavioral
complexity and illustrates striking evolutionary conservation in the molecular
organization and function of motor circuits across species.
acknowledged_ssus:
- _id: Bio
acknowledgement: "We would like to thank the members of the Sweeney Lab (especially
Stavros Papadopoulos and\r\nSophie Gobeil) for their contributions to this project
and, in addition to the lab, Graziana Gatto\r\nand Mario de Bono, for discussion,
and support. We are also grateful to Tom Jessell and Chris\r\nKintner for their
scientific insight and mentorship during the conception of this project. This\r\nproject
would also not have been possible with the technical support of the Matthias Nowak,\r\nVerena
Mayer and the Aquatics as well as the Imaging and Optics Facility support teams\r\n(ISTA).
In addition, we thank our funding sources for providing the resources to do these\r\nexperiments:
FTI Strategy Lower Austria Dissertation Grant Number FT121-D-046 (D.V.);\r\nHorizon
Europe ERC Starting Grant Number 101041551 (L.B.S., F.A.T. and D.V); Special\r\nResearch
Program (SFB) of the Austrian Science Fund (FWF) Project number F7814-B (L.B.S);\r\nNINDS
5R35NS116858 (J.S.D); CZI grant DAF2020-225401 (DOI): 10.37921/120055ratwvi\r\n(R.H.);
NIH grant number R01NS123116 (J.B.B); American Lebanese Syrian Associated\r\nCharities
(ALSAC) (J.B.B.); German Academic Exchange Service (DAAD) IFI Grant Number\r\n57515251-91853472
(Z.H.); and Project A.L.S. (S.B-M.). "
article_processing_charge: No
author:
- first_name: David
full_name: Vijatovic, David
id: cf391e77-ec3c-11ea-a124-d69323410b58
last_name: Vijatovic
- first_name: 'Florina Alexandra '
full_name: 'Toma, Florina Alexandra '
id: 2f73f876-f128-11eb-9611-b96b5a30cb0e
last_name: Toma
- first_name: Zoe P
full_name: Harrington, Zoe P
id: a8144562-32c9-11ee-b5ce-d9800628bda2
last_name: Harrington
orcid: 0009-0008-0158-4032
- first_name: Christoph M
full_name: Sommer, Christoph M
id: 4DF26D8C-F248-11E8-B48F-1D18A9856A87
last_name: Sommer
orcid: 0000-0003-1216-9105
- first_name: Robert
full_name: Hauschild, Robert
id: 4E01D6B4-F248-11E8-B48F-1D18A9856A87
last_name: Hauschild
orcid: 0000-0001-9843-3522
- first_name: Alexandra J.
full_name: Trevisan, Alexandra J.
last_name: Trevisan
- first_name: Phillip
full_name: Chapman, Phillip
last_name: Chapman
- first_name: Mara
full_name: Julseth, Mara
id: 1cf464b2-dc7d-11ea-9b2f-f9b1aa9417d1
last_name: Julseth
- first_name: Susan
full_name: Brenner-Morton, Susan
last_name: Brenner-Morton
- first_name: Mariano I.
full_name: Gabitto, Mariano I.
last_name: Gabitto
- first_name: Jeremy S.
full_name: Dasen, Jeremy S.
last_name: Dasen
- first_name: Jay B.
full_name: Bikoff, Jay B.
last_name: Bikoff
- first_name: Lora Beatrice Jaeger
full_name: Sweeney, Lora Beatrice Jaeger
id: 56BE8254-C4F0-11E9-8E45-0B23E6697425
last_name: Sweeney
orcid: 0000-0001-9242-5601
citation:
ama: Vijatovic D, Toma FA, Harrington ZP, et al. Spinal neuron diversity scales
exponentially with swim-to-limb transformation during frog metamorphosis. bioRxiv.
doi:10.1101/2024.09.20.614050
apa: Vijatovic, D., Toma, F. A., Harrington, Z. P., Sommer, C. M., Hauschild, R.,
Trevisan, A. J., … Sweeney, L. B. (n.d.). Spinal neuron diversity scales exponentially
with swim-to-limb transformation during frog metamorphosis. bioRxiv. https://doi.org/10.1101/2024.09.20.614050
chicago: Vijatovic, David, Florina Alexandra Toma, Zoe P Harrington, Christoph
M Sommer, Robert Hauschild, Alexandra J. Trevisan, Phillip Chapman, et al. “Spinal
Neuron Diversity Scales Exponentially with Swim-to-Limb Transformation during
Frog Metamorphosis.” BioRxiv, n.d. https://doi.org/10.1101/2024.09.20.614050.
ieee: D. Vijatovic et al., “Spinal neuron diversity scales exponentially
with swim-to-limb transformation during frog metamorphosis,” bioRxiv. .
ista: Vijatovic D, Toma FA, Harrington ZP, Sommer CM, Hauschild R, Trevisan AJ,
Chapman P, Julseth M, Brenner-Morton S, Gabitto MI, Dasen JS, Bikoff JB, Sweeney
LB. Spinal neuron diversity scales exponentially with swim-to-limb transformation
during frog metamorphosis. bioRxiv, 10.1101/2024.09.20.614050.
mla: Vijatovic, David, et al. “Spinal Neuron Diversity Scales Exponentially with
Swim-to-Limb Transformation during Frog Metamorphosis.” BioRxiv, doi:10.1101/2024.09.20.614050.
short: D. Vijatovic, F.A. Toma, Z.P. Harrington, C.M. Sommer, R. Hauschild, A.J.
Trevisan, P. Chapman, M. Julseth, S. Brenner-Morton, M.I. Gabitto, J.S. Dasen,
J.B. Bikoff, L.B. Sweeney, BioRxiv (n.d.).
corr_author: '1'
date_created: 2025-04-07T08:48:28Z
date_published: 2024-09-27T00:00:00Z
date_updated: 2025-05-14T11:40:13Z
day: '27'
department:
- _id: LoSw
- _id: TiVo
- _id: Bio
- _id: NiBa
doi: 10.1101/2024.09.20.614050
fulldoi: https://doi.org/10.1101/2024.09.20.614050
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.1101/2024.09.20.614050
month: '09'
oa: 1
oa_version: Preprint
project:
- _id: bd73af52-d553-11ed-ba76-912049f0ac7a
grant_number: FTI21-D-046
name: Development of V1 interneuron diversity during swim-to-walk transition of
Xenopus metamorphosis
- _id: ebb66355-77a9-11ec-83b8-b8ac210a4dae
grant_number: '101041551'
name: Development and Evolution of Tetrapod Motor Circuits
- _id: c08e9ad1-5a5b-11eb-8a69-9d1cf3b07473
grant_number: CZI01
name: Tools for automation and feedback microscopy
publication: bioRxiv
publication_status: submitted
status: public
title: Spinal neuron diversity scales exponentially with swim-to-limb transformation
during frog metamorphosis
type: preprint
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2024'
...