Summary of Learn: Learnable and Adaptive Representations For Nonlinear Dynamics in System Identification, by Arunabh Singh and Joyjit Mukherjee
LeARN: Learnable and Adaptive Representations for Nonlinear Dynamics in System Identification
by Arunabh Singh, Joyjit Mukherjee
First submitted to arxiv on: 16 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Robotics (cs.RO)
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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 a novel framework called LeARN (Learning-based Algorithmic Reasoning Network) for nonlinear system identification. LeARN learns the library of basis functions directly from data, eliminating the need for prior domain knowledge and enhancing adaptability to evolving system dynamics under varying noise conditions. The framework uses a lightweight deep neural network (DNN) to dynamically refine these basis functions, capturing intricate system behaviors while adapting seamlessly to new dynamical regimes. The authors validate LeARN on the Neural Fly dataset, showcasing its robust adaptation and generalization capabilities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new way to understand complex systems using machine learning. It develops a framework called LeARN that can learn how systems work without needing specific knowledge about those systems. This helps the algorithm adapt to changing conditions and learn from data. The authors test this method on a dataset of fly brain activity, showing it works well. |
Keywords
» Artificial intelligence » Generalization » Machine learning » Neural network
