A submodular optimization approach to sentence set selection

By Yusuke Shinohara. 2014

34 citations recorded by the source corpus.

Subfields: Artificial Intelligence

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Rishabh K. Iyer
2. Ganesh Ramakrishnan
3. Kai Wei
4. Durga Sivasubramanian
5. J. Bilmes
6. Indian Institute of Technology Bombay Institution One
7. I. Two
8. Krishnateja Killamsetty

Citations in the corpus

• Submodularity in Data Subset Selection and Active Learning (2015, methodology): “A number of authors have studied these versions of data subset selection, and have observed that submodular functions nicely fit this problem (Shinohara, 2014; Zheng et al., 2014; Hoi et al., 2006; Shamaiah et al., 2010; Prasad et al., 2014; Krause et al., 2008; Das & Kempe, 2011; Tschiatschek et…”
• GLISTER: Generalization based Data Subset Selection for Efficient and Robust Learning (2020, methodology): “For example, the data selection approaches such as (Wei, Iyer, and Bilmes 2015; Mirzasoleiman, Bilmes, and Leskovec 2020; Shinohara 2014) and others focus on approximating either gradients or performance on the training sets, and hence would not be suitable for scenarios such as label noise and…”