1 citations · 1 across the 5 of their papers we have counts for
8 papers
Understanding, Accelerating, and Improving MeanFlow Training
Jin-Young Kim, Hyojun Go, Lea Bogensperger +5
MeanFlow promises high-quality generative modeling in few steps, by jointly learning instantaneous and average velocity fields. Yet, the underlying training dynamics remain unclear…
LoRA-Ensemble: Efficient Uncertainty Modelling for Self-Attention Networks
Dominik J. Mühlematter, Michelle Halbheer, Alexander Becker +4
Numerous real-world decisions rely on machine learning algorithms and require calibrated uncertainty estimates. However, modern methods often yield overconfident, uncalibrated pred…
Text-to-3D by Stitching a Multi-view Reconstruction Network to a Video Generator
Hyojun Go, Dominik Narnhofer, Goutam Bhat +3
The rapid progress of large, pretrained models for both visual content generation and 3D reconstruction opens up new possibilities for text-to-3D generation. Intuitively, one could…
Continuous Space-Time Video Super-Resolution with 3D Fourier Fields
Alexander Becker, Julius Erbach, Dominik Narnhofer +1
We introduce a novel formulation for continuous space-time video super-resolution. Instead of decoupling the representation of a video sequence into separate spatial and temporal c…
Repurposing Protein Language Models for Latent Flow-Based Fitness Optimization
Amaru Caceres Arroyo, Lea Bogensperger, Ahmed Allam +3
Protein fitness optimization is challenged by a vast combinatorial landscape where high-fitness variants are extremely sparse. Many current methods either underperform or require c…
A Variational Perspective on Generative Protein Fitness Optimization
Lea Bogensperger, Dominik Narnhofer, Ahmed Allam +2
The goal of protein fitness optimization is to discover new protein variants with enhanced fitness for a given use. The vast search space and the sparsely populated fitness landsca…