Summary of Reinforcement Learning Of Adaptive Acquisition Policies For Inverse Problems, by Gianluigi Silvestri et al.
Reinforcement Learning of Adaptive Acquisition Policies for Inverse Problems
by Gianluigi Silvestri, Fabio Valerio Massoli, Tribhuvanesh Orekondy, Afshin Abdi, Arash Behboodi
First submitted to arxiv on: 10 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Signal Processing (eess.SP)
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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 innovative method to reduce the cost of acquiring high-dimensional signals by collecting a limited number of low-dimensional measurements and solving an under-determined inverse problem. The authors focus on developing adaptive acquisition schemes that minimize the number of measurements required for signal recovery. They introduce a reinforcement learning-based approach that sequentially collects measurements to improve signal recovery, applying to general inverse problems with continuous action spaces. The method is designed using variational formulation and evaluated on multiple datasets with two measurement spaces (Gaussian, Radon). Results confirm the benefits of adaptive strategies in low-acquisition horizon settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explores a new way to make it cheaper to get high-dimensional signals by taking fewer measurements and solving an inverse problem. The authors are working on ways to adapt how many measurements they take based on what they’ve learned so far. They’re using machine learning to figure out the best way to collect data, which can be used for many different types of problems. They tested their approach on several datasets and found that it works well. |
Keywords
» Artificial intelligence » Machine learning » Reinforcement learning
