Summary of Gradient Estimation and Variance Reduction in Stochastic and Deterministic Models, by Ronan Keane
Gradient Estimation and Variance Reduction in Stochastic and Deterministic Models
by Ronan Keane
First submitted to arxiv on: 14 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Systems and Control (eess.SY); Optimization and Control (math.OC)
GrooveSquid.com Paper Summaries
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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 Machine learning educators can summarize this research paper by stating: A growing trend in scientific research relies on computers, computation, and data. Machine learning and artificial intelligence have become essential in various fields. The use of larger models has become a significant focus. This dissertation explores unconstrained nonlinear optimization problems using gradient-based methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary For curious learners or general audiences without a technical background, this paper is about how scientists are using computers to help with their research. It’s like having a superpower assistant! Researchers are working on ways to make computer models bigger and better so they can solve more complex problems. The goal is to make discoveries faster and more accurately. |
Keywords
» Artificial intelligence » Machine learning » Optimization
