Summary of Disentangling Impact Of Capacity, Objective, Batchsize, Estimators, and Step-size on Flow Vi, by Abhinav Agrawal and Justin Domke
Disentangling impact of capacity, objective, batchsize, estimators, and step-size on flow VI
by Abhinav Agrawal, Justin Domke
First submitted to arxiv on: 11 Dec 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 paper investigates the performance inconsistencies in normalizing flow-based variational inference (flow VI), an approximate inference approach, by examining the impact of various algorithmic choices. The authors analyze five key factors: capacity, objectives, gradient estimators, batchsize, and step-sizes, while controlling for other variables using parallel computation and insights from previous steps. A curated benchmark of synthetic targets is used to evaluate high-fidelity performance. The study aims to provide specific recommendations for different factors and proposes a flow VI recipe that rivals leading turnkey Hamiltonian Monte Carlo (HMC) methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at ways to make an AI method called “flow-based variational inference” work better. Right now, it doesn’t always do well in tests. The researchers try to figure out why this is by looking at five important things that affect how the method works: how powerful it is, what goals it has, how it estimates changes, how many times it does these changes, and how fast it does them. They use special computers to make sure they’re not messing up their tests. The goal is to find a way to make this AI method do better in tests, possibly even as well as other similar methods. |
Keywords
» Artificial intelligence » Inference
