Review of dependency estimation with focus on data efficiency: Unterschied zwischen den Versionen
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|betreuer=Bela Böhnke | |betreuer=Bela Böhnke | ||
|termin=Institutsseminar/2022-01-14 Zusatztermin | |termin=Institutsseminar/2022-01-14 Zusatztermin | ||
|kurzfassung=In our data-driven world, where large amounts of data are collected in all kinds of environments, data analysis rises in importance. How different variables influence each other is a significant part of knowledge discovery and | |kurzfassung=In our data-driven world, where large amounts of data are collected in all kinds of environments, data analysis rises in importance. How different variables influence each other is a significant part of knowledge discovery and allows strategic decisions based on this knowledge. Therefore high-quality dependency estimation should be accessible to a variety of people. Many dependency estimation algorithms are difficult to use in a real-world setting. In addition, most of these dependency estimation algorithms need large data sets to return a good estimation. In practice, gathering this amount of data may be costly, especially when collected in experiments with high costs for materials or infrastructure needed. In this study, different state-of-the-art dependency estimation algorithms are compared by a list of different criteria. This study focuses on data efficiency and uncertainty of the dependency estimation algorithms. The comparison includes a theoretical analysis and different experiments with dependency estimation algorithms that performed well in the theoretical analysis. | ||
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Version vom 5. Januar 2022, 16:33 Uhr
Vortragende(r) | Maximilian Georg | |
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Vortragstyp | Proposal | |
Betreuer(in) | Bela Böhnke | |
Termin | [[Institutsseminar/2022-01-14 Zusatztermin|]] | |
Vortragsmodus | ||
Kurzfassung | In our data-driven world, where large amounts of data are collected in all kinds of environments, data analysis rises in importance. How different variables influence each other is a significant part of knowledge discovery and allows strategic decisions based on this knowledge. Therefore high-quality dependency estimation should be accessible to a variety of people. Many dependency estimation algorithms are difficult to use in a real-world setting. In addition, most of these dependency estimation algorithms need large data sets to return a good estimation. In practice, gathering this amount of data may be costly, especially when collected in experiments with high costs for materials or infrastructure needed. In this study, different state-of-the-art dependency estimation algorithms are compared by a list of different criteria. This study focuses on data efficiency and uncertainty of the dependency estimation algorithms. The comparison includes a theoretical analysis and different experiments with dependency estimation algorithms that performed well in the theoretical analysis. |