4 citations · 6 across the 7 of their papers we have counts for
7 papers · 1 filter
Adversarial Boot Camp: label free certified robustness in one epoch
Ryan Campbell, Chris Finlay, Adam M Oberman
Machine learning models are vulnerable to adversarial attacks. One approach to addressing this vulnerability is certification, which focuses on models that are guaranteed to be rob…
Deterministic Gaussian Averaged Neural Networks
Ryan Campbell, Chris Finlay, Adam M Oberman
We present a deterministic method to compute the Gaussian average of neural networks used in regression and classification. Our method is based on an equivalence between training w…
Learning normalizing flows from Entropy-Kantorovich potentials
Chris Finlay, Augusto Gerolin, Adam M Oberman +1
We approach the problem of learning continuous normalizing flows from a dual perspective motivated by entropy-regularized optimal transport, in which continuous normalizing flows a…
Farkas layers: don't shift the data, fix the geometry
Aram-Alexandre Pooladian, Chris Finlay, Adam M Oberman
Successfully training deep neural networks often requires either batch normalization, appropriate weight initialization, both of which come with their own challenges. We propose an…
A principled approach for generating adversarial images under non-smooth dissimilarity metrics
Aram-Alexandre Pooladian, Chris Finlay, Tim Hoheisel +1
Deep neural networks perform well on real world data but are prone to adversarial perturbations: small changes in the input easily lead to misclassification. In this work, we propo…
The LogBarrier adversarial attack: making effective use of decision boundary information
Chris Finlay, Aram-Alexandre Pooladian, Adam M. Oberman
Adversarial attacks for image classification are small perturbations to images that are designed to cause misclassification by a model. Adversarial attacks formally correspond to a…