2.2k citations · 5.9k across the 41 of their papers we have counts for
27 papers · 1 filter
A framework for benchmarking class-out-of-distribution detection and its application to ImageNet
Ido Galil, Mohammed Dabbah, Ran El-Yaniv
When deployed for risk-sensitive tasks, deep neural networks must be able to detect instances with labels from outside the distribution for which they were trained. In this paper w…
What Can We Learn From The Selective Prediction And Uncertainty Estimation Performance Of 523 Imagenet Classifiers
Ido Galil, Mohammed Dabbah, Ran El-Yaniv
When deployed for risk-sensitive tasks, deep neural networks must include an uncertainty estimation mechanism. Here we examine the relationship between deep architectures and their…
Which models are innately best at uncertainty estimation?
Ido Galil, Mohammed Dabbah, Ran El-Yaniv
Due to the comprehensive nature of this paper, it has been updated and split into two separate papers: "A Framework For Benchmarking Class-out-of-distribution Detection And Its App…
Disrupting Deep Uncertainty Estimation Without Harming Accuracy
Ido Galil, Ran El-Yaniv
Deep neural networks (DNNs) have proven to be powerful predictors and are widely used for various tasks. Credible uncertainty estimation of their predictions, however, is crucial f…
Train on Small, Play the Large: Scaling Up Board Games with AlphaZero and GNN
Shai Ben-Assayag, Ran El-Yaniv
Playing board games is considered a major challenge for both humans and AI researchers. Because some complicated board games are quite hard to learn, humans usually begin with play…
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…