Associate Professor, TU Berlin
2 papers at NeurIPS 2025
We present Fractional Diffusion Bridge Models (FDBM), a novel generative diffusion bridge framework that enables generative diffusion bridge modeling with fractional noise for both paired and unpaired training data.
Representational alignment based on concept discovery across ViTs trained on different tasks.
