Summary of Variational Inference Via Smoothed Particle Hydrodynamics, by Yongchao Huang
Variational Inference via Smoothed Particle Hydrodynamics
by Yongchao Huang
First submitted to arxiv on: 12 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG); 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 A novel variational inference approach, SPH-ParVI, leveraging smoothed particle hydrodynamics (SPH), is introduced for sampling partially known densities or utilizing gradients. By simulating the flow of a fluid under external forces driven by the target density, transient or steady-state fluid dynamics approximate the target distribution. The continuum fluid is modelled as an interacting particle system (IPS) via SPH, where each particle carries smoothed properties and evolves according to Navier-Stokes equations. This mesh-free, Lagrangian simulation method enables fast, flexible, scalable, and deterministic sampling and inference for a range of probabilistic models in Bayesian inference and generative modelling. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary SPH-ParVI is a new way to guess the shape of a probability distribution based on a limited amount of information. Imagine you’re trying to figure out what’s inside a jar without being able to see or touch it. This method uses a special kind of math that helps us simulate how a fluid would move if we were to put it under certain conditions, which then allows us to get a better idea of the shape of the probability distribution. |
Keywords
» Artificial intelligence » Bayesian inference » Inference » Probability
