Summary of Control-theoretic Techniques For Online Adaptation Of Deep Neural Networks in Dynamical Systems, by Jacob G. Elkins and Farbod Fahimi
Control-Theoretic Techniques for Online Adaptation of Deep Neural Networks in Dynamical Systems
by Jacob G. Elkins, Farbod Fahimi
First submitted to arxiv on: 1 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Neural and Evolutionary Computing (cs.NE); Robotics (cs.RO); Systems and Control (eess.SY)
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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 Deep neural networks (DNNs) are widely used in artificial intelligence, machine learning, and data science. Typically, they’re trained offline through supervised or reinforcement learning and deployed online for inference. However, training DNNs without performance guarantees or bounds can be limiting. Moreover, many applications experience domain shift from the training distribution to the real-world distribution, which hinders transfer learning. To address these issues, we propose using control theory techniques to update DNN parameters online. We formulate the fully-connected feedforward DNN as a continuous-time dynamical system and develop novel last-layer update laws that ensure error convergence under various conditions. Additionally, spectral normalization training controls the upper bound of error trajectories, which is crucial when working with noisy state measurements or numerically differentiated quantities. Our proposed methods are validated in simulation, demonstrating the effectiveness of control-theoretic techniques in improving DNN performance and guarantees. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Artificial intelligence relies heavily on deep neural networks (DNNs). These networks are trained offline and then used to make predictions online. However, there’s a problem: training DNNs doesn’t guarantee how well they’ll perform or what happens when the environment changes. To solve this issue, researchers propose using control theory techniques to update DNN parameters in real-time. This helps ensure that the network makes accurate predictions even when the conditions change. The method is tested in simulations and shown to be effective. |
Keywords
* Artificial intelligence * Inference * Machine learning * Reinforcement learning * Supervised * Transfer learning
