Summary of Model Based and Physics Informed Deep Learning Neural Network Structures, by Ali Mohammad-djafari et al.
Model Based and Physics Informed Deep Learning Neural Network Structures
by Ali Mohammad-Djafari, Ning Chu, Li Wang, Caifang Cai, Liang Yu
First submitted to arxiv on: 13 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 research work focuses on addressing the open problem of selecting or proposing a new neural network (NN) structure for specific data, signal, or image processing tasks. The study highlights the challenges of choosing an appropriate NN architecture and proposes five classes of methods to tackle this issue. These methods include explicit analytical solutions, transform domain decomposition, operator decomposition, optimization algorithm unfolding, and physics-informed neural network (PINN) approaches. Examples are provided for each category. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research aims to find a solution to the problem of choosing the right neural network structure for specific tasks. Neural networks have been very successful in many areas. When we train a neural network, we need to determine its parameters using an optimization algorithm and a criterion. Then, we can use it for predictions or inference. However, there are many hyperparameters related to the optimization criteria and algorithms, which makes it necessary to validate our model before using it. This study proposes five classes of methods to help solve this problem. |
Keywords
» Artificial intelligence » Inference » Neural network » Optimization
