29 citations · 61 across the 5 of their papers we have counts for
5 papers
Revisiting Sanity Checks for Saliency Maps
Gal Yona, Daniel Greenfeld
Saliency methods are a popular approach for model debugging and explainability. However, in the absence of ground-truth data for what the correct maps should be, evaluating and com…
On Calibration and Out-of-domain Generalization
Yoav Wald, Amir Feder, Daniel Greenfeld +1
Out-of-domain (OOD) generalization is a significant challenge for machine learning models. Many techniques have been proposed to overcome this challenge, often focused on learning…
Robust Learning with the Hilbert-Schmidt Independence Criterion
Daniel Greenfeld, Uri Shalit
We investigate the use of a non-parametric independence measure, the Hilbert-Schmidt Independence Criterion (HSIC), as a loss-function for learning robust regression and classifica…
Learning to Optimize Multigrid PDE Solvers
Daniel Greenfeld, Meirav Galun, Ron Kimmel +2
Constructing fast numerical solvers for partial differential equations (PDEs) is crucial for many scientific disciplines. A leading technique for solving large-scale PDEs is using…
Improved Training for Self-Training by Confidence Assessments
Gal Hyams, Daniel Greenfeld, Dor Bank
It is well known that for some tasks, labeled data sets may be hard to gather. Therefore, we wished to tackle here the problem of having insufficient training data. We examined lea…