Summary of Incremental Online Learning Of Randomized Neural Network with Forward Regularization, by Junda Wang et al.
Incremental Online Learning of Randomized Neural Network with Forward Regularization
by Junda Wang, Minghui Hu, Ning Li, Abdulaziz Al-Ali, Ponnuthurai Nagaratnam Suganthan
First submitted to arxiv on: 17 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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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 Incremental Online Learning (IOL) process for Randomized Neural Networks (Randomized NN), aiming to alleviate challenges in online learning such as hysteretic non-incremental updating, increasing memory usage, past retrospective retraining, and catastrophic forgetting. The IOL framework incorporates ridge regularization (-R) and forward regularization (-F) to facilitate continuous improvements to Randomized NN performance. -R generates stepwise incremental updates without retrospective retraining, while -F enhances precognition learning ability using semi-supervision and achieves better online regrets to offline global experts compared to -R. The paper also derives algorithms for IOL with -R/-F on non-stationary batch streams, featuring recursive weight updates and variable learning rates. Additionally, the authors conduct a detailed analysis and theoretically derive relative cumulative regret bounds of Randomized NN learners with -R/-F in IOL under adversarial assumptions using a novel methodology. The paper validates the efficacy of IOL frameworks for Randomized NN and the advantages of forward regularization across regression and classification tasks on diverse datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps us learn better online by fixing some big problems that happen when we try to teach machines new things. Right now, it’s hard to make machines learn gradually without messing up what they already know. The authors propose a new way to do this called Incremental Online Learning (IOL), which uses something called Randomized Neural Networks (Randomized NN). They also add two special tricks to help the machine learn better: -R and -F. These tricks make it so the machine can learn quickly without forgetting what it already knows, and they work really well for both easy and hard tasks. |
Keywords
» Artificial intelligence » Classification » Online learning » Regression » Regularization » Semi supervision
