Summary of Cadam: Confidence-based Optimization For Online Learning, by Shaowen Wang et al.
CAdam: Confidence-Based Optimization for Online Learning
by Shaowen Wang, Anan Liu, Jian Xiao, Huan Liu, Yuekui Yang, Cong Xu, Qianqian Pu, Suncong Zheng, Wei Zhang, Jian Li
First submitted to arxiv on: 29 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
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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 In this paper, researchers propose an optimization strategy called CAdam, designed to address the challenges posed by online learning data. The Adam optimizer is widely used for updating neural networks, but it may not perform well when dealing with distribution shifts and noise in the data. To mitigate these issues, CAdam assesses the consistency between momentum and gradient for each parameter dimension before making updates. If the momentum and gradient are in sync, CAdam proceeds with standard Adam updates; otherwise, it temporarily withholds updates to monitor potential shifts in data distribution. The authors demonstrate that CAdam outperforms other optimizers, including Adam, on both synthetic and real-world datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary CAdam is a new way for machines to learn from changing data. Right now, most recommendation systems use an old optimizer called Adam. But Adam doesn’t work well when the data changes suddenly or has a lot of noise. CAdam is different because it checks if the momentum and gradient are in sync before making updates. If they’re not, CAdam waits until the data looks more stable before updating again. This helps CAdam avoid mistakes caused by sudden changes or noise. The researchers tested CAdam on fake and real datasets and found that it works better than Adam. They even tested CAdam in a real recommendation system and saw a big increase in its performance. |
Keywords
» Artificial intelligence » Online learning » Optimization
