Summary of Gradient-based Non-linear Inverse Learning, by Abhishake et al.
Gradient-Based Non-Linear Inverse Learning
by Abhishake, Nicole Mücke, Tapio Helin
First submitted to arxiv on: 21 Dec 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 studies statistical inverse learning in the context of nonlinear inverse problems under random design, employing gradient descent (GD) and stochastic gradient descent (SGD) with mini-batching. The analysis derives convergence rates for both algorithms under classical assumptions on the smoothness of the target function, expressed through the integral operator associated with the tangent kernel. The results demonstrate the efficacy of GD and SGD in achieving optimal rates for nonlinear inverse problems in random design. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how to solve complex math problems by using special computer algorithms. It tests two types of algorithms: gradient descent (GD) and stochastic gradient descent (SGD). These algorithms are used to figure out the solution to a problem that’s hard to solve directly. The researchers show that these algorithms can be effective in solving this type of problem, even when some information is missing. |
Keywords
» Artificial intelligence » Gradient descent » Stochastic gradient descent
