2.1k citations · 5.2k across the 53 of their papers we have counts for
7 papers · 2 filters
The best defense is a good offense: Countering black box attacks by predicting slightly wrong labels
Yannic Kilcher, Thomas Hofmann
Black-Box attacks on machine learning models occur when an attacker, despite having no access to the inner workings of a model, can successfully craft an attack by means of model t…
Parametrizing filters of a CNN with a GAN
Yannic Kilcher, Gary Becigneul, Thomas Hofmann
It is commonly agreed that the use of relevant invariances as a good statistical bias is important in machine-learning. However, most approaches that explicitly incorporate invaria…
Flexible Prior Distributions for Deep Generative Models
Yannic Kilcher, Aurelien Lucchi, Thomas Hofmann
We consider the problem of training generative models with deep neural networks as generators, i.e. to map latent codes to data points. Whereas the dominant paradigm combines simpl…
Semantic Interpolation in Implicit Models
Yannic Kilcher, Aurelien Lucchi, Thomas Hofmann
In implicit models, one often interpolates between sampled points in latent space. As we show in this paper, care needs to be taken to match-up the distributional assumptions on co…
Accelerated Dual Learning by Homotopic Initialization
Hadi Daneshmand, Hamed Hassani, Thomas Hofmann
Gradient descent and coordinate descent are well understood in terms of their asymptotic behavior, but less so in a transient regime often used for approximations in machine learni…
An Online Learning Approach to Generative Adversarial Networks
Paulina Grnarova, Kfir Y. Levy, Aurelien Lucchi +2
We consider the problem of training generative models with a Generative Adversarial Network (GAN). Although GANs can accurately model complex distributions, they are known to be di…