Review of dependency estimation with focus on data efficiency: Unterschied zwischen den Versionen

Version vom 25. November 2021, 09:58 Uhr

Vortragende(r)	Maximilian Georg
Vortragstyp	Proposal
Betreuer(in)	Bela Böhnke
Termin	[[Institutsseminar/2022-01-07\|]]
Vortragsmodus
Kurzfassung	In our data-driven world where tons of data is collected, dependency estimation is an important tool to get more insight into our data. But many dependency estimation algorithms are hard to use in a real-world setting. In this study, I will do a comparison of different state-of-the-art dependency estimation algorithms. I will compare them regarding a list of different criteria and focus on data efficiency and uncertainty of the estimation. The comparison includes a theoretical analysis and a variety of different experiments with the algorithms.

@@ Zeile 5: / Zeile 5: @@
 |betreuer=Bela Böhnke
 |termin=Institutsseminar/2022-01-07
-|kurzfassung=In our data-driven world data analysis is very important. Therefore dependency estimation is an important tool to get more insight into our data. In this study, I will do a comparison of different state-of-the-art dependency estimation algorithms. I will compare them regarding a list of different criteria and focus on data efficiency and uncertainty of the estimation.
+|kurzfassung=In our data-driven world where tons of data is collected, dependency estimation is an important tool to get more insight into our data. But many dependency estimation algorithms are hard to use in a real-world setting. In this study, I will do a comparison of different state-of-the-art dependency estimation algorithms. I will compare them regarding a list of different criteria and focus on data efficiency and uncertainty of the estimation. The comparison includes a theoretical analysis and a variety of different experiments with the algorithms.
 }}