People

Max Ploner

Doctoral Researcher

Computer Science

Max joined SCIoI as a doctoral researcher in October 2022


HU Berlin

 

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Photo: SCIoI

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Max Ploner

Max Ploner

Photo: SCIoI

Max Ploner is a computer scientist with a focus on machine learning and the dynamics of artificial neural networks.

He joined SCIoI as a doctoral researcher in October 2022 and works under the supervision of Professor Alan Akbik at HU Berlin.

Max studied computer science at Humboldt University of Berlin and Technical University of Berlin, focusing on machine learning. His research interests are concentrated on topological changes during the training of artificial neural networks (ANN). In his master’s thesis, Max studied growth as a means for reducing the pre-training time of transformer networks.

At SCIoI, Max is a member of Project 45A, and researches how ANN growth can improve the continual learning capabilities of these models.


Projects

Max Ploner is member of:


6984777 Ploner 1 apa 50 date desc year 19984 https://www.scienceofintelligence.de/wp-content/plugins/zotpress/
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Ploner, M., Wiland, J., Pohl, S., & Akbik, A. (2025). LM-Pub-Quiz: A Comprehensive Framework for Zero-Shot Evaluation of Relational Knowledge in Language Models. In N. Dziri, S. (Xiang) Ren, & S. Diao (Eds.), Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics (pp. 29–39). Association for Computational Linguistics. https://doi.org/10.18653/v1/2025.naacl-demo.4
Garbas, L., Ploner, M., & Akbik, A. (2025). TransformerRanker: A Tool for Efficiently Finding the Best-Suited Language Models for Downstream Classification Tasks. In N. Dziri, S. (Xiang) Ren, & S. Diao (Eds.), Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics (pp. 295–302). Association for Computational Linguistics. https://doi.org/10.18653/v1/2025.naacl-demo.25
Pohl, S., Ploner, M., & Akbik, A. (2025). Towards a Principled Evaluation of Knowledge Editors. Proceedings of the First Workshop on Large Language Model Memorization (L2M2), 47–60. https://doi.org/10.18653/v1/2025.l2m2-1.4
Christoph, D., Ploner, M., Haller, P., & Akbik, A. (2025). From Data to Knowledge: Evaluating How Efficiently Language Models Learn Facts. Proceedings of the First Workshop on Large Language Model Memorization (L2M2), 29–46. https://doi.org/10.18653/v1/2025.l2m2-1.3
Garbaciauskas, L., Ploner, M., & Akbik, A. (2024). Choose Your Transformer: Improved Transferability Estimation of Transformer Models on Classification Tasks. In L.-W. Ku, A. Martins, & V. Srikumar (Eds.), Findings of the Association for Computational Linguistics: ACL 2024 (pp. 12752–12768). Association for Computational Linguistics. https://doi.org/10.18653/v1/2024.findings-acl.757
Ploner, M., & Akbik, A. (2024). Parameter-Efficient Fine-Tuning: Is There An Optimal Subset of Parameters to Tune? Findings of the Association for Computational Linguistics: EACL 2024, 1743–1759. https://doi.org/10.18653/v1/2024.findings-eacl.122
Wiland, J., Ploner, M., & Akbik, A. (2024). BEAR: A Unified Framework for Evaluating Relational Knowledge in Causal and Masked Language Models. Findings of the Association for Computational Linguistics: NAACL 2024, 2393–2411. https://doi.org/10.18653/v1/2024.findings-naacl.155

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