Summary of Robust Gradient Descent For Phase Retrieval, by Alex Buna et al.
Robust Gradient Descent for Phase Retrieval
by Alex Buna, Patrick Rebeschini
First submitted to arxiv on: 14 Oct 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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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 paper proposes an approach to improve the Wirtinger Flow algorithm in tackling non-convex problems, specifically phase retrieval, by leveraging robust gradient descent techniques. The method aims to cope with heavy-tailed noise and adversarial corruption in both input and output data. The authors address two scenarios: known zero-mean noise and completely unknown noise, proposing a preprocessing step for the latter. This approach can potentially resolve phase retrieval problems that traditional methods cannot handle. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about solving a tricky math problem called phase retrieval. It’s like trying to reconstruct an image from just its brightness levels, without knowing the colors or directions of the light. Usually, we use special algorithms to solve this problem, but they don’t work well when there’s lots of noise or bad data mixed in. The researchers are proposing a new way to improve these algorithms so they can handle noisy and tricky data better. They’re trying to find ways to make it work even when the noise is really extreme. |
Keywords
» Artificial intelligence » Gradient descent
