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Maissam Barkeshli on Muck Rack

Maissam Barkeshli

As seen in: Nature, arXiv, Physical Review B
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Articles

Computer Science > Machine Learning

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By Maissam Barkeshli
| arXiv
[Submitted on 20 May 2026] Title:Quantifying Hyperparameter Transfer and the Importance of Embedding Layer Learning Rate View a PDF of the paper titled Quantifying Hyperparameter Transfer and the Importance of Embedding Layer Learning Rate, by Dayal Singh Kalra and Maissam Barkeshli View PDF HTML (experimental) Abstract:Hyperparameter transfer allows extrapolating optimal optimization hyperparameters from small to large scales, making it critical for training large language models (LLMs).
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Learning Pseudorandom Numbers with Transformers: Permuted Congruential Generators, Curricula, and Interpretability

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By Maissam Barkeshli
| 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.
Open in Who Shared View Duplicates (614) Issue with article?

[2406.09405] Why Warmup the Learning Rate? Underlying Mechanisms and Improvements

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By Maissam Barkeshli
| arXiv
[Submitted on 13 Jun 2024] Title:Why Warmup the Learning Rate? Underlying Mechanisms and Improvements View a PDF of the paper titled Why Warmup the Learning Rate? Underlying Mechanisms and Improvements, by Dayal Singh Kalra and Maissam Barkeshli View PDF HTML (experimental) Abstract:It is common in deep learning to warm up the learning rate $\eta$, often by a linear schedule between $\eta_{\text{init}} = 0$ and a predetermined target $\eta_{\text{trgt}}$.
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