Summary of Iterated Energy-based Flow Matching For Sampling From Boltzmann Densities, by Dongyeop Woo et al.
Iterated Energy-based Flow Matching for Sampling from Boltzmann Densities
by Dongyeop Woo, Sungsoo Ahn
First submitted to arxiv on: 29 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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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 The proposed iterated energy-based flow matching (iEFM) method addresses a fundamental problem in probabilistic inference, enabling the training of continuous normalizing flow (CNF) models from unnormalized densities. This approach is crucial for scientific applications such as learning 3D coordinate distributions of molecules. iEFM introduces a simulation-free energy-based flow matching objective that trains the model to predict Monte Carlo estimations of marginal vector fields constructed from known energy functions. The framework is general and can be extended to variance-exploding (VE) and optimal transport (OT) conditional probability paths. iEFM outperforms existing methods in evaluating two-dimensional Gaussian mixture models (GMMs) and eight-dimensional four-particle double-well potentials (DW-4). This showcases the potential of iEFM for efficient and scalable probabilistic modeling in complex high-dimensional systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Training a generator from evaluations of energy functions or unnormalized densities is crucial for scientific applications like learning 3D molecule distributions. The new iterated energy-based flow matching (iEFM) method helps achieve this goal by proposing the first off-policy approach to train continuous normalizing flow (CNF) models from unnormalized densities. The iEFM framework introduces a simulation-free energy-based flow matching objective that trains the model to predict Monte Carlo estimations of marginal vector fields. This general approach can be extended to variance-exploding and optimal transport conditional probability paths. The method is evaluated on 2D Gaussian mixture models and an eight-dimensional double-well potential, showing it outperforms existing methods. |
Keywords
» Artificial intelligence » Inference » Probability
