28 citations · 52 across the 11 of their papers we have counts for
4 papers · 1 filter
Single Layer Predictive Normalized Maximum Likelihood for Out-of-Distribution Detection
Koby Bibas, Meir Feder, Tal Hassner
Detecting out-of-distribution (OOD) samples is vital for developing machine learning based models for critical safety systems. Common approaches for OOD detection assume access to…
Utilizing Adversarial Targeted Attacks to Boost Adversarial Robustness
Uriya Pesso, Koby Bibas, Meir Feder
Adversarial attacks have been shown to be highly effective at degrading the performance of deep neural networks (DNNs). The most prominent defense is adversarial training, a method…
Distribution Free Uncertainty for the Minimum Norm Solution of Over-parameterized Linear Regression
Koby Bibas, Meir Feder
A fundamental principle of learning theory is that there is a trade-off between the complexity of a prediction rule and its ability to generalize. Modern machine learning models do…
Learning Rotation Invariant Features for Cryogenic Electron Microscopy Image Reconstruction
Koby Bibas, Gili Weiss-Dicker, Dana Cohen +2
Cryo-Electron Microscopy (Cryo-EM) is a Nobel prize-winning technology for determining the 3D structure of particles at near-atomic resolution. A fundamental step in the recovering…