Expanding Flow Maps
- Published
- Source
- arXiv
- Paper number
- 711
- Field
- Machine Learning
- arXiv ID
- 2607.21585
Key points
- It is the first to propose a new generative model structure called Expansion Flow, or EFlow, which increases dimensionality during generation.
- It alternates expand and transport operations to generate high-quality samples in only one or two steps.
- For molecular graph generation, it reduces the one-step FCD error to 0.44 versus 2.14 for the prior CFM method.
- It also outperforms fixed-length models on language generation, which shows that variable-length generation does not hurt quality.
- It demonstrates that the same framework works in both continuous domains, such as molecular structure, and discrete domains, such as graphs and language.
Paper links
External research summaries. These are not HDATF publications or measured product results.