245 citations · 406 across the 13 of their papers we have counts for
14 papers
Leveraging LLM Inconsistency to Boost Pass@k Performance
Uri Dalal, Meirav Segal, Zvika Ben-Haim +2
Large language models (LLMs) achieve impressive abilities in numerous domains, but exhibit inconsistent performance in response to minor input changes. Rather than view this as a d…
Flood forecasting with machine learning models in an operational framework
Sella Nevo, Efrat Morin, Adi Gerzi Rosenthal +28
The operational flood forecasting system by Google was developed to provide accurate real-time flood warnings to agencies and the public, with a focus on riverine floods in large,…
Physics-Aware Downsampling with Deep Learning for Scalable Flood Modeling
Niv Giladi, Zvika Ben-Haim, Sella Nevo +2
Background: Floods are the most common natural disaster in the world, affecting the lives of hundreds of millions. Flood forecasting is therefore a vitally important endeavor, typi…
Inundation Modeling in Data Scarce Regions
Zvika Ben-Haim, Vladimir Anisimov, Aaron Yonas +4
Flood forecasts are crucial for effective individual and governmental protective action. The vast majority of flood-related casualties occur in developing countries, where providin…
Minimum Variance Estimation of a Sparse Vector within the Linear Gaussian Model: An RKHS Approach
Alexander Jung, Sebastian Schmutzhard, Franz Hlawatsch +2
We consider minimum variance estimation within the sparse linear Gaussian model (SLGM). A sparse vector is to be estimated from a linearly transformed version embedded in Gaussian…
A Lower Bound on the Estimator Variance for the Sparse Linear Model
Sebastian Schmutzhard, Alexander Jung, Franz Hlawatsch +2
We study the performance of estimators of a sparse nonrandom vector based on an observation which is linearly transformed and corrupted by additive white Gaussian noise. Using the…