10 citations · 10 across the 16 of their papers we have counts for
6 papers · 1 filter
Distill the Best, Ignore the Rest: Improving Dataset Distillation with Loss-Value-Based Pruning
Brian B. Moser, Federico Raue, Tobias C. Nauen +2
Dataset distillation has gained significant interest in recent years, yet existing approaches typically distill from the entire dataset, potentially including non-beneficial sample…
Zoomed In, Diffused Out: Towards Local Degradation-Aware Multi-Diffusion for Extreme Image Super-Resolution
Brian B. Moser, Stanislav Frolov, Tobias C. Nauen +2
Large-scale, pre-trained Text-to-Image (T2I) diffusion models have gained significant popularity in image generation tasks and have shown unexpected potential in image Super-Resolu…
Just Leaf It: Accelerating Diffusion Classifiers with Hierarchical Class Pruning
Arundhati S. Shanbhag, Brian B. Moser, Tobias C. Nauen +3
Diffusion models, celebrated for their generative capabilities, have recently demonstrated surprising effectiveness in image classification tasks by using Bayes' theorem. Yet, curr…
A Low-Resolution Image is Worth 1x1 Words: Enabling Fine Image Super-Resolution with Transformers and TaylorShift
Sanath Budakegowdanadoddi Nagaraju, Brian Bernhard Moser, Tobias Christian Nauen +3
Transformer-based architectures have recently advanced the image reconstruction quality of super-resolution (SR) models. Yet, their scalability remains limited by quadratic attenti…
Stochastic Control with Signatures
P. Bank, C. Bayer, P. P. Hager +2
This paper proposes to parameterize open loop controls in stochastic optimal control problems via suitable classes of functionals depending on the driver's path signature, a concep…
TaylorShift: Shifting the Complexity of Self-Attention from Squared to Linear (and Back) using Taylor-Softmax
Tobias Christian Nauen, Sebastian Palacio, Andreas Dengel
The quadratic complexity of the attention mechanism represents one of the biggest hurdles for processing long sequences using Transformers. Current methods, relying on sparse repre…