Summary of Verlet Flows: Exact-likelihood Integrators For Flow-based Generative Models, by Ezra Erives et al.
Verlet Flows: Exact-Likelihood Integrators for Flow-Based Generative Models
by Ezra Erives, Bowen Jing, Tommi Jaakkola
First submitted to arxiv on: 5 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel class of continuous normalizing flow models, called Verlet flows, is introduced to improve the approximation of model likelihoods in Boltzmann distribution importance sampling. By leveraging symplectic integrators from Hamiltonian dynamics and carefully constructed Taylor-Verlet integrators, these exact-likelihood generative models generalize coupled flow architectures while imposing minimal expressivity constraints. Experimental results on toy densities show that Verlet flows perform comparably to full autograd trace computations while being significantly faster than the commonly used Hutchinson trace estimator. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new type of model called Verlet flows, which helps improve the accuracy of computing likelihoods in Boltzmann distributions. The idea comes from Hamiltonian dynamics and uses special integrators to make sure the models are exact-likelihood models that can generate data. This is important because it allows us to use these models for sampling and makes them more efficient. |
Keywords
» Artificial intelligence » Likelihood
