Summary of Streamfp: Learnable Fingerprint-guided Data Selection For Efficient Stream Learning, by Tongjun Shi et al.
StreamFP: Learnable Fingerprint-guided Data Selection for Efficient Stream Learning
by Tongjun Shi, Shuhao Zhang, Binbin Chen, Bingsheng He
First submitted to arxiv on: 11 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 Stream Learning (SL) is a challenging machine learning problem that requires models to quickly adapt to continuously evolving data. Effective data selection is crucial in SL to balance information retention and training efficiency. Traditional rule-based methods struggle with dynamic streaming data, making innovative solutions essential. Our proposed StreamFP approach uses learnable parameters called fingerprints to enhance data selection efficiency and adaptability in stream learning. StreamFP optimizes coreset selection through its fingerprint-guided mechanism for efficient training while ensuring robust buffer updates that respond to data dynamics. Experimental results demonstrate that StreamFP outperforms state-of-the-art methods by achieving accuracy improvements of 15.99%, 29.65%, and 51.24% compared to baseline models across varying data arrival rates, alongside a training throughput increase of 4.6x. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re trying to learn from a huge amount of changing data, like real-time social media posts or financial transactions. It’s hard for machines to keep up and make accurate predictions because the data is always shifting. One way to help with this problem is by choosing which pieces of data are most important to focus on. Our new approach, StreamFP, uses special “fingerprints” to select the best data quickly and efficiently. This helps the machine learning model adapt better to changing data patterns. In tests, our method worked much better than existing methods, achieving higher accuracy and faster training times. |
Keywords
» Artificial intelligence » Machine learning
