Cross-Spectral Factor Analysis

By Neil Gallagher, Kyle R. Ulrich, Austin Talbot, Kafui Dzirasa, Lawrence Carin, David Edwin Carlson. 2017

26 citations recorded by the source corpus.

How researchers see this paper

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

1. Cynthia Rudin
2. David Edwin Carlson
3. Yitong Li
4. Lawrence Carin
5. Michael Murias
6. Geraldine Dawson
7. Samantha Major
8. Kafui Dzirasa

Citations in the corpus

• Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead (2018, background): “f examples of work on interpretability that would not have been covered by recent review articles, and yet are valuable contributions to interpretability in their respective domains. Gallagher et al. [56] analyze brain-wide electrical spatiotemporal dynamics to understand depression vulnerability an”
• Please Stop Explaining Black Box Models for High Stakes Decisions (2018, background): “For instance, Gallagher et al. [2017] analyze brain-wide electrical spatiotemporal dynamics to understand depression vulnerability and find interpretable patterns in a low dimensional space.”
• Targeting EEG/LFP Synchrony with Neural Nets (2017, methodology): “The phase/amplitude synchrony of LFP signals has been modeled [30, 10] with the cross-spectral mixture (CSM) kernel.”