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M. Emrullah

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Articles by M. Emrullah

Computer Science > Machine Learning

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By Ege Onur, M. Emrullah
| arXiv
Large language models are increasingly used as open-ended search operators in evolutionary optimization. We introduce Evolutionary Feature Engineering (EFE), a framework for using LLM-based evolution to discover preprocessing transformations for structured data. EFE represents transformations as Python programs with a standardized fit/transform interface, allowing them to be inserted directly into existing machine learning pipelines.
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[2503.11842] Test-Time Training Provably Improves Transformers as In-context Learners

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By Halil Alperen, M. Emrullah
| arXiv
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TimePFN: Effective Multivariate Time Series Forecasting with Synthetic Data

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By Ege Onur, M. Emrullah
| 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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High-dimensional Analysis of Knowledge Distillation: Weak-to-Strong Generalization and Scaling Laws

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By M. Emrullah, Ege Onur, Halil Alperen
| arXiv
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From Self-Attention to Markov Models: Unveiling the Dynamics of Generative Transformers

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By Ankit Singh, M. Emrullah
| arXiv
[Submitted on 21 Feb 2024] Title:From Self-Attention to Markov Models: Unveiling the Dynamics of Generative Transformers Download a PDF of the paper titled From Self-Attention to Markov Models: Unveiling the Dynamics of Generative Transformers, by M. Emrullah Ildiz and 3 other authors Download PDF HTML (experimental) Abstract:Modern language models rely on the transformer architecture and attention mechanism to perform language understanding and text generation.
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