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Masked Autoencoder Pretraining for Event Classification in Elite Soccer

Publikation: Beiträge in Sammelwerken › Aufsätze in Konferenzbänden › Forschung › begutachtet

Standard

Masked Autoencoder Pretraining for Event Classification in Elite Soccer. / Rudolph, Yannick; Brefeld, Ulf.

Machine Learning and Data Mining for Sports Analytics: 10th International Workshop, MLSA 2023, Revised Selected Papers. Hrsg. / Ulf Brefeld; Jesse Davis; Jan Van Haaren; Albrecht Zimmermann. Cham : Springer Nature Switzerland AG, 2024. S. 24-35 (Communications in Computer and Information Science; Band 2035).

Publikation: Beiträge in Sammelwerken › Aufsätze in Konferenzbänden › Forschung › begutachtet

Harvard

Rudolph, Y & Brefeld, U 2024, Masked Autoencoder Pretraining for Event Classification in Elite Soccer. in U Brefeld, J Davis, J Van Haaren & A Zimmermann (Hrsg.), Machine Learning and Data Mining for Sports Analytics: 10th International Workshop, MLSA 2023, Revised Selected Papers. Communications in Computer and Information Science, Bd. 2035, Springer Nature Switzerland AG, Cham, S. 24-35, 10th International Workshop on Machine Learning and Data Mining for Sports Analytics, MLSA 2023, Turin, Italien, 18.09.23. https://doi.org/10.1007/978-3-031-53833-9_3

APA

Rudolph, Y., & Brefeld, U. (2024). Masked Autoencoder Pretraining for Event Classification in Elite Soccer. in U. Brefeld, J. Davis, J. Van Haaren, & A. Zimmermann (Hrsg.), Machine Learning and Data Mining for Sports Analytics: 10th International Workshop, MLSA 2023, Revised Selected Papers (S. 24-35). (Communications in Computer and Information Science; Band 2035). Springer Nature Switzerland AG. https://doi.org/10.1007/978-3-031-53833-9_3

Vancouver

Rudolph Y, Brefeld U. Masked Autoencoder Pretraining for Event Classification in Elite Soccer. in Brefeld U, Davis J, Van Haaren J, Zimmermann A, Hrsg., Machine Learning and Data Mining for Sports Analytics: 10th International Workshop, MLSA 2023, Revised Selected Papers. Cham: Springer Nature Switzerland AG. 2024. S. 24-35. (Communications in Computer and Information Science). doi: 10.1007/978-3-031-53833-9_3

Bibtex

@inbook{0ca682cc4633469e8d5e809ba430275d,
title = "Masked Autoencoder Pretraining for Event Classification in Elite Soccer",
abstract = "We show that pretraining transformer models improves the performance on supervised classification of tracking data from elite soccer. Specifically, we propose a novel self-supervised masked autoencoder for multiagent trajectories. In contrast to related work, our approach is significantly simpler, has no necessity for handcrafted features and inherently allows for permutation invariance in downstream tasks.",
keywords = "Factorized transformer architecture, Masked autoencoder, Multiagent trajectories, Self-supervised learning, Soccer, Tracking data, Informatics, Business informatics",
author = "Yannick Rudolph and Ulf Brefeld",
note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.; 10th International Workshop on Machine Learning and Data Mining for Sports Analytics, MLSA 2023 ; Conference date: 18-09-2023 Through 18-09-2023",
year = "2024",
month = feb,
day = "26",
doi = "10.1007/978-3-031-53833-9_3",
language = "English",
isbn = "978-3-031-53832-2",
series = "Communications in Computer and Information Science",
publisher = "Springer Nature Switzerland AG",
pages = "24--35",
editor = "Ulf Brefeld and Jesse Davis and {Van Haaren}, Jan and Albrecht Zimmermann",
booktitle = "Machine Learning and Data Mining for Sports Analytics",
address = "Switzerland",

}

RIS

TY - CHAP

T1 - Masked Autoencoder Pretraining for Event Classification in Elite Soccer

AU - Rudolph, Yannick

AU - Brefeld, Ulf

N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

PY - 2024/2/26

Y1 - 2024/2/26

N2 - We show that pretraining transformer models improves the performance on supervised classification of tracking data from elite soccer. Specifically, we propose a novel self-supervised masked autoencoder for multiagent trajectories. In contrast to related work, our approach is significantly simpler, has no necessity for handcrafted features and inherently allows for permutation invariance in downstream tasks.

AB - We show that pretraining transformer models improves the performance on supervised classification of tracking data from elite soccer. Specifically, we propose a novel self-supervised masked autoencoder for multiagent trajectories. In contrast to related work, our approach is significantly simpler, has no necessity for handcrafted features and inherently allows for permutation invariance in downstream tasks.

KW - Factorized transformer architecture

KW - Masked autoencoder

KW - Multiagent trajectories

KW - Self-supervised learning

KW - Soccer

KW - Tracking data

KW - Informatics

KW - Business informatics

UR - http://www.scopus.com/inward/record.url?scp=85187650562&partnerID=8YFLogxK

UR - https://www.mendeley.com/catalogue/df720300-8c0a-37b1-a340-cb810b405c19/

U2 - 10.1007/978-3-031-53833-9_3

DO - 10.1007/978-3-031-53833-9_3

M3 - Article in conference proceedings

AN - SCOPUS:85187650562

SN - 978-3-031-53832-2

T3 - Communications in Computer and Information Science

SP - 24

EP - 35

BT - Machine Learning and Data Mining for Sports Analytics

A2 - Brefeld, Ulf

A2 - Davis, Jesse

A2 - Van Haaren, Jan

A2 - Zimmermann, Albrecht

PB - Springer Nature Switzerland AG

CY - Cham

T2 - 10th International Workshop on Machine Learning and Data Mining for Sports Analytics, MLSA 2023

Y2 - 18 September 2023 through 18 September 2023

ER -

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DOI