Summary of Control Theoretic Approach to Fine-tuning and Transfer Learning, by Erkan Bayram et al.
Control Theoretic Approach to Fine-Tuning and Transfer Learning
by Erkan Bayram, Shenyu Liu, Mohamed-Ali Belabbas, Tamer Başar
First submitted to arxiv on: 17 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Optimization and Control (math.OC)
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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 This paper introduces a novel approach to learning control systems that can adapt to changes in the training set without forgetting previously learned information. The authors propose an iterative algorithm that updates the control strategy based on new training data, while preserving the knowledge gained from previous iterations. This is achieved by projecting the updated control onto the kernel of the end-point mapping generated by the controlled dynamics at the learned samples. The proposed method, dubbed “tuning without forgetting,” aims to overcome the limitation of existing methods that require restarting from scratch when the training set expands. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explains a complex idea in simple terms: imagine you have a machine that can move objects around, and you want it to learn how to do this based on a set of examples. Normally, if you add more examples, the machine would forget what it learned from the previous ones. This new method helps the machine remember what it already knows while still learning from the new examples. |
