People

Max Ploner

Doctoral Researcher

Computer Science

HU Berlin

 

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Photo: SCIoI

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Max Ploner

Max Ploner

Photo: SCIoI

Max Ploner studied computer science at Humboldt University of Berlin and Technical University of Berlin focusing on machine learning. He now works as a PhD student under the supervision of Professor Alan Akbik at HU Berlin. His research interests are concentrated on   topological changes during the training of artificial neural networks (ANN). In his master thesis, Max studied growth as a means for reducing the pre-training time of transformer networks. At SCIoI, Max is working on Project 45A (“Modeling Neurogenesis for Continuous Learning”) and researches how ANN growth can improve the continual learning capabilities of these models.


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6984777 Ploner 1 apa 50 date desc year 19984 https://www.scienceofintelligence.de/wp-content/plugins/zotpress/
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Pohl, S., Ploner, M., & Akbik, A. (2025). Towards a Principled Evaluation of Knowledge Editors. In R. Jia, E. Wallace, Y. Huang, T. Pimentel, P. Maini, V. Dankers, J. Wei, & P. Lesci (Eds.), Proceedings of the First Workshop on Large Language Model Memorization (L2M2) (pp. 47–60). Association for Computational Linguistics. https://doi.org/10.18653/v1/2025.l2m2-1.4
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Ploner, M., & Akbik, A. (2024). Parameter-Efficient Fine-Tuning: Is There An Optimal Subset of Parameters to Tune? In Y. Graham & M. Purver (Eds.), Findings of the Association for Computational Linguistics: EACL 2024 (pp. 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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