75 citations · 141 across the 19 of their papers we have counts for
4 papers · 1 filter
Applications of fractional calculus in learned optimization
Teodor Alexandru Szente, James Harrison, Mihai Zanfir +1
Fractional gradient descent has been studied extensively, with a focus on its ability to extend traditional gradient descent methods by incorporating fractional-order derivatives.…
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…
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…
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…