248 citations · 710 across the 48 of their papers we have counts for
3 papers · 1 filter
Understanding the Limitations of Conditional Generative Models
Ethan Fetaya, Jörn-Henrik Jacobsen, Will Grathwohl +1
Class-conditional generative models hold promise to overcome the shortcomings of their discriminative counterparts. They are a natural choice to solve discriminative tasks in a rob…
Evaluating and Calibrating Uncertainty Prediction in Regression Tasks
Dan Levi, Liran Gispan, Niv Giladi +1
Predicting not only the target but also an accurate measure of uncertainty is important for many machine learning applications and in particular safety-critical ones. In this work…
On the Universality of Invariant Networks
Haggai Maron, Ethan Fetaya, Nimrod Segol +1
Constraining linear layers in neural networks to respect symmetry transformations from a group is a common design principle for invariant networks that has found many applicati…