54 citations · 170 across the 11 of their papers we have counts for
14 papers
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,…
Solving Sokoban with forward-backward reinforcement learning
Yaron Shoham, Gal Elidan
Despite seminal advances in reinforcement learning in recent years, many domains where the rewards are sparse, e.g. given only at task completion, remain quite challenging. In such…
Explaining in Style: Training a GAN to explain a classifier in StyleSpace
Oran Lang, Yossi Gandelsman, Michal Yarom +8
Image classification models can depend on multiple different semantic attributes of the image. An explanation of the decision of the classifier needs to both discover and visualize…
ML-based Flood Forecasting: Advances in Scale, Accuracy and Reach
Sella Nevo, Gal Elidan, Avinatan Hassidim +4
Floods are among the most common and deadly natural disasters in the world, and flood warning systems have been shown to be effective in reducing harm. Yet the majority of the worl…
HydroNets: Leveraging River Structure for Hydrologic Modeling
Zach Moshe, Asher Metzger, Gal Elidan +3
Accurate and scalable hydrologic models are essential building blocks of several important applications, from water resource management to timely flood warnings. However, as the cl…
DNF-Net: A Neural Architecture for Tabular Data
Ami Abutbul, Gal Elidan, Liran Katzir +1
A challenging open question in deep learning is how to handle tabular data. Unlike domains such as image and natural language processing, where deep architectures prevail, there is…