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

Aus SDQ-Institutsseminar
Keine Bearbeitungszusammenfassung
Keine Bearbeitungszusammenfassung
Zeile 4: Zeile 4:
|vortragstyp=Proposal
|vortragstyp=Proposal
|betreuer=Bela Böhnke
|betreuer=Bela Böhnke
|termin=Institutsseminar/2022-01-07
|termin=Institutsseminar/2022-01-14
|kurzfassung=In our data-driven world, where tons of data are collected, dependency estimation is important to get more insights into our data. 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. For comparison, a list of different criteria is used and the focus of this study is on data efficiency and uncertainty of the dependency estimation algorithms. The comparison includes a theoretical analysis and a variety of different experiments with an implementation of the dependency estimation algorithm that performed well in the theoretical analysis.
|kurzfassung=In our data-driven world, where tons of data are collected, dependency estimation is important to get more insights into our data. 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. For comparison, a list of different criteria is used and the focus of this study is on data efficiency and uncertainty of the dependency estimation algorithms. The comparison includes a theoretical analysis and a variety of different experiments with an implementation of the dependency estimation algorithm that performed well in the theoretical analysis.
}}
}}

Version vom 5. Januar 2022, 15:01 Uhr

Vortragende(r) Maximilian Georg
Vortragstyp Proposal
Betreuer(in) Bela Böhnke
Termin Fr 14. Januar 2022
Vortragsmodus
Kurzfassung In our data-driven world, where tons of data are collected, dependency estimation is important to get more insights into our data. 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. For comparison, a list of different criteria is used and the focus of this study is on data efficiency and uncertainty of the dependency estimation algorithms. The comparison includes a theoretical analysis and a variety of different experiments with an implementation of the dependency estimation algorithm that performed well in the theoretical analysis.