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I am interested in building principally designed machine learning algorithms that strikes a balance between theory and application. On one hand, theory provides us mathematical tools to make theoretical guarantees such as robustness, convergence rates, transferability, etc. On the other hand, application provides us empirical examination of engineering properties and practical implications using real-life data. Both are equally important and supports each other, making machine learning interpretable and explainable. To give this relationship a fancy term between this balance, I call it the “hermeneutics of machine learning.”
I am interested in building principally designed machine learning algorithms that strikes a balance between theory and application. On one hand, theory provides us mathematical tools to make theoretical guarantees such as robustness, convergence rates, transferability, etc. On the other hand, application provides us empirical examination of engineering properties and practical implications using real-life data. Both are equally important and supports each other, making machine learning interpretable and explainable. To give this relationship a fancy term between this balance, I call it the “hermeneutics of machine learning.”
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Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)pp.5427-5438, (2023)
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