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20172021
most citedLearning normalizing flows from Entropy-Kantorovich potentials

4 citations · 6 across the 7 of their papers we have counts for

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Showing 2020Show all

6 papers · 1 filter

cs.LG2020

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…

q-bio.PE2020

Climate & BCG: Effects on COVID-19 Death Growth Rates

Chris Finlay, Bruce A. Bassett

Multiple studies have suggested the spread of COVID-19 is affected by factors such as climate, BCG vaccinations, pollution and blood type. We perform a joint study of these factors…

cs.LG2020★ 1 cited

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…

cs.LG2020★ 4 cited

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…

astro-ph.IM2020

Deep Learning improves identification of Radio Frequency Interference

Alireza Vafaei Sadr, Bruce A. Bassett, Nadeem Oozeer +2

Flagging of Radio Frequency Interference (RFI) is an increasingly important challenge in radio astronomy. We present R-Net, a deep convolutional ResNet architecture that significan…

stat.ML2020

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…