activity
20122022
most citedA Reparameterization-Invariant Flatness Measure for Deep Neural Networks

2 citations · 2 across the 4 of their papers we have counts for

collaborators

6 papers

cs.LG2022

Discriminating Against Unrealistic Interpolations in Generative Adversarial Networks

Henning Petzka, Ted Kronvall, Cristian Sminchisescu

Interpolations in the latent space of deep generative models is one of the standard tools to synthesize semantically meaningful mixtures of generated samples. As the generator func…

cs.LG20192 cited

A Reparameterization-Invariant Flatness Measure for Deep Neural Networks

Henning Petzka, Linara Adilova, Michael Kamp +1

The performance of deep neural networks is often attributed to their automated, task-related feature construction. It remains an open question, though, why this leads to solutions…

cs.LG2018

Non-attracting Regions of Local Minima in Deep and Wide Neural Networks

Henning Petzka, Cristian Sminchisescu

Understanding the loss surface of neural networks is essential for the design of models with predictable performance and their success in applications. Experimental results suggest…

math.OA2016

Comparison Properties of the Cuntz semigroup and applications to C*-algebras

Joan Bosa, Henning Petzka

We study comparison properties in the category Cu aiming to lift results to the C*-algebraic setting. We introduce a new comparison property and relate it to both the CFP and -c…

math.OA2012

The Blackadar-Handelman theorem for non-unital C*-algebras

Henning Petzka

A well-known theorem of Blackadar and Handelman states that every unital stably finite C*-algebra has a bounded quasitrace. Rather strong generalizations of stable finiteness to th…

math.OA2012

On certain multiplier projections

Henning Petzka

Let $\MCZK$, denote the multiplier algebra over $\CZK$, the algebra of continuous functions into the compact operators with spectrum the infinite product of two-spheres. We conside…