← Search

Lea Bogensperger

3 accepted papers

2026

Understanding, Accelerating, and Improving MeanFlow Training

CVPR 2026

MeanFlow promises high-quality generative modeling in few steps, by jointly learning instantaneous and average velocity fields. Yet, the underlying training dynamics remain unclear. We analyze the interaction between the two velocities and find: (i) well-established instantaneous velocity is a prere

Cited by 0SourcecodeScholar
2025

A Variational Perspective on Generative Protein Fitness Optimization

ICML 2025poster

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 landscape, along with the discrete nature of protein sequences, pose significant challenges when trying to determine the gradie…

Cited by 0SourcePDFScholar
2025

Energy Matching: Unifying Flow Matching and Energy-Based Models for Generative Modeling

NeurIPS 2025poster

Current state-of-the-art generative models map noise to data distributions by matching flows or scores. A key limitation of these models is their inability to readily integrate available partial observations and additional priors. In contrast, energy-based models (EBMs) address this by incorporating…

Cited by 0SourceScholar