no code implementations • 1 Dec 2023 • Petru Tighineanu, Lukas Grossberger, Paul Baireuther, Kathrin Skubch, Stefan Falkner, Julia Vinogradska, Felix Berkenkamp
Meta-learning is a powerful approach that exploits historical data to quickly solve new tasks from the same distribution.
1 code implementation • NeurIPS 2021 • Amrutha Saseendran, Kathrin Skubch, Stefan Falkner, Margret Keuper
In this paper, we propose a simple and end-to-end trainable deterministic autoencoding framework, that efficiently shapes the latent space of the model during training and utilizes the capacity of expressive multi-modal latent distributions.
1 code implementation • 22 Nov 2021 • Petru Tighineanu, Kathrin Skubch, Paul Baireuther, Attila Reiss, Felix Berkenkamp, Julia Vinogradska
Bayesian optimization is a powerful paradigm to optimize black-box functions based on scarce and noisy data.
1 code implementation • ICCV 2021 • Amrutha Saseendran, Kathrin Skubch, Margret Keuper
Image generation has rapidly evolved in recent years.
no code implementations • 1 Jan 2021 • Felix Berkenkamp, Anna Eivazi, Lukas Grossberger, Kathrin Skubch, Jonathan Spitz, Christian Daniel, Stefan Falkner
Transfer and meta-learning algorithms leverage evaluations on related tasks in order to significantly speed up learning or optimization on a new problem.