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Marawan Gamal Abdel

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Articles

Computer Science > Machine Learning

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By Marawan Gamal Abdel, Pascal Jr Tikeng Notsawo, Derek Tam
| arXiv
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
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[2510.00382] Efficient Probabilistic Tensor Networks

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By Marawan Gamal Abdel
| arXiv
arXiv:2510.00382 (cs) View PDF HTML (experimental) Subjects: Machine Learning (cs.LG) Cite as: arXiv:2510.00382 [cs.LG] (or arXiv:2510.00382v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2510.00382 Submission history From: Marawan Gamal Abdel Hameed [ view email] [v1] Wed, 1 Oct 2025 00:52:51 UTC (212 KB) Bibliographic Tools Bibliographic Explorer Toggle Bibliographic Explorer () Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is...
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ROSA: Random Subspace Adaptation for Efficient Fine-Tuning

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By Marawan Gamal Abdel
| arXiv
[Submitted on 10 Jul 2024] Title:ROSA: Random Subspace Adaptation for Efficient Fine-Tuning View a PDF of the paper titled ROSA: Random Subspace Adaptation for Efficient Fine-Tuning, by Marawan Gamal Abdel Hameed and 3 other authors View PDF HTML (experimental) Abstract:Model training requires significantly more memory, compared with inference. Parameter efficient fine-tuning (PEFT) methods provide a means of adapting large models to downstream tasks using less memory.
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