7 citations · 30 across the 15 of their papers we have counts for
4 papers · 1 filter
Frustratingly Easy Uncertainty Estimation for Distribution Shift
Tiago Salvador, Vikram Voleti, Alexander Iannantuono +1
Distribution shift is an important concern in deep image classification, produced either by corruption of the source images, or a complete change, with the solution involving domai…
How to train your neural ODE: the world of Jacobian and kinetic regularization
Chris Finlay, Jörn-Henrik Jacobsen, Levon Nurbekyan +1
Training neural ODEs on large datasets has not been tractable due to the necessity of allowing the adaptive numerical ODE solver to refine its step size to very small values. In pr…
Scaleable input gradient regularization for adversarial robustness
Chris Finlay, Adam M Oberman
In this work we revisit gradient regularization for adversarial robustness with some new ingredients. First, we derive new per-image theoretical robustness bounds based on local gr…
Calibrated Top-1 Uncertainty estimates for classification by score based models
Adam M. Oberman, Chris Finlay, Alexander Iannantuono +1
While the accuracy of modern deep learning models has significantly improved in recent years, the ability of these models to generate uncertainty estimates has not progressed to th…