Table of Links
3. SkyCURTAINs Method and 3.1 CurtainsF4F
5. Conclusion, Acknowledgments, Data Availability, and References
APPENDIX A: CurtainsF4F TRAINING AND HYPERPARAMETER TUNING DETAILS
A1. CurtainsF4F features preprocessing
A2 Hyperparameter tuning
The hyperparameters for the base and top flow are listed in Table A1, where the hyperparameters for the base flows were found to give robust performance regardless of the patch, and so held constant. For the top flows, there could be significant variation in performance related to the hyperparameter selection depending on the patch, and so hyperparameter tuning was performed to find the values that performed well regardless of the patch. Both base and top flow were capped at a maximum number of 150, and 100 epochs respectively. While the base flow seemed to improve with higher number of epochs, the top flow converged much more quickly at ∼ 30 − 40 epochs of training.
Authors:
(1) Debajyoti Sengupta, Département de physique nucléaire et corpusculaire, University of Geneva, Switzerland ([email protected]);
(2) Stephen Mulligan, Département de physique nucléaire et corpusculaire, University of Geneva, Switzerland;
(3) David Shih, NHETC, Dept. of Physics and Astronomy, Rutgers, Piscataway, NJ 08854, USA;
(4) John Andrew Raine,, Département de physique nucléaire et corpusculaire, University of Geneva, Switzerland;
(5) Tobias Golling, Département de physique nucléaire et corpusculaire, University of Geneva, Switzerland.
This paper is
