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www.huber.embl.de | ||
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ddarmon.github.io
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iclr-blogposts.github.io
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| | | | | The transfer of matching-based training from Diffusion Models to Normalizing Flows allows to fit expressive continuous normalizing flows efficiently and therefore enables their usage for different kinds of density estimation tasks. One particularly interesting task is Simulation-Based Inference, where Flow Matching enabled several improvements. The post shall focus on the discussion of Flow Matching for Continuous Normalizing Flows. To highlight the relevance and the practicality of the method, their use and advantages for Simulation-Based Inference is elaborated. | |
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sebastianraschka.com
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| | | | | I'm an LLM Research Engineer with over a decade of experience in artificial intelligence. My work bridges academia and industry, with roles including senior staff at an AI company and a statistics professor. My expertise lies in LLM research and the development of high-performance AI systems, with a deep focus on practical, code-driven implementations. | |
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www.analyticsvidhya.com
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| | | Interpretable machine learning is key to understanding how machine learning models work. In this article learn about LIME and python implementation of it. | ||
