Summary of Control, Transport and Sampling: Towards Better Loss Design, by Qijia Jiang et al.
Control, Transport and Sampling: Towards Better Loss Design
by Qijia Jiang, David Nabergoj
First submitted to arxiv on: 22 May 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); Computation (stat.CO)
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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 novel objective functions leverage connections between diffusion-based sampling, optimal transport, and stochastic optimal control to efficiently sample from a target distribution μ. By optimizing controlled dynamics, the approach can be used to transport ν to μ. The pathwise perspective is crucial, with various optimality conditions playing key roles in designing valid training losses. This formalism, based on the Schrödinger bridge problem, also allows for incorporating inductive bias during Neural Network training. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper explores a new way to sample from a target distribution μ. It combines ideas from different areas of mathematics and computer science to create a method that’s efficient and useful. By controlling dynamics, we can transport one distribution to another. The approach is based on the concept of Schrödinger bridge and has practical applications in training Neural Networks. |
Keywords
» Artificial intelligence » Diffusion » Neural network
