New path-gradient estimator for continuous normalizing flows.
arXiv research
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Improved sampling efficiency for molecular systems using path gradients after Flow Matching.
A new path gradient estimator speeds up normalizing flows without sacrificing accuracy.
Improved KL divergence estimators for normalizing flows lead to faster convergence and better approximations.
We develop a normative framework for hierarchical model-based policy optimization based on applying second-order methods in the space of all possible state-action paths. The resulting natural path gradient performs policy updates in a manner which is sensitive to the long-range correlational structure of the induced st…
The high computational and parameter complexity of neural networks makes their training very slow and difficult to deploy on energy and storage-constrained computing systems. Many network complexity reduction techniques have been proposed including fixed-point implementation. However, a systematic approach for designin…
Improves BED scalability for implicit models.
A new method uses string method to explore diffusion models.