Human-Explainable Features for Job Candidate Screening Prediction

By Achmadnoer Sukma Wicaksana, Cynthia C. S. Liem. 2017

19 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Clinical Psychology, Computer Vision and Pattern Recognition

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. Song-Chun Zhu
2. Quanshi Zhang
3. Ying Nian Wu
4. Yu Yang
5. Cornelius J. Konig
6. Markus Langer
7. Erik Nijkamp
8. Mitch Hill

In Clinical Psychology

1. Cornelius J. Konig
2. Marise Ph. Born

In Computer Vision and Pattern Recognition

1. Song-Chun Zhu
2. Quanshi Zhang
3. Ying Nian Wu
4. Yu Yang
5. Alan L. Yuille
6. Erik Nijkamp
7. Luc Van Gool
8. Jianwen Xie

Citations in the corpus

• Unsupervised Learning of Neural Networks to Explain Neural Networks (2018, background): “Other studies retrieved mid-level representations with specific meanings from CNNs for various applications [15, 35, 32, 19].”
• Interpretable Convolutional Neural Networks (2017, background): “[11, 31, 29, 15] selected neural units with specific meanings from CNNs for various applications.”
• Psychology Meets Machine Learning: Interdisciplinary Perspectives on Algorithmic Job Candidate Screening (2018, methodology): “C. C. S. Liem ( ) · A. Demetriou Multimedia Computing Group, Delft University of Technology, Delft, The Netherlands e-mail: c.c.s.liem@tudelft.nl; a.m.demetriou@tudelft.nl M. Langer · C. J. König Universität des Saarlandes, Saarbrücken, Germany e-mail: markus.langer@uni-saarland.de; ckoenig@mx.uni-s”