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The INF^2 project develops advanced methods for high-dimensional data analysis in machine learning. It focuses on creating efficient algorithms that require minimal data while maintaining interpretability, particularly for applications in medical fields.
Extracting information from data is the key challenge of our time, and in many applications (e.g., genome-wide association studies, data compression, and virtual assistants such as ChatGPT) both the data and the machine learning model used to extract information are increasingly high-dimensional.
As traditional statistical theory is ill-equipped to face this explosion in the dimensionality of the problem, machine learning is now predominantly experimental.
However, empirical approaches come with…
INSTITUTE OF SCIENCE AND TECHNOLOGY AUSTRIA
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