Summary of Beyond Scaleup: Knowledge-aware Parsimony Learning From Deep Networks, by Quanming Yao and Yongqi Zhang and Yaqing Wang and Nan Yin and James Kwok and Qiang Yang
Beyond Scaleup: Knowledge-aware Parsimony Learning from Deep Networks
by Quanming Yao, Yongqi Zhang, Yaqing Wang, Nan Yin, James Kwok, Qiang Yang
First submitted to arxiv on: 29 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 This paper proposes a novel approach to developing more robust learning models by leveraging domain-specific knowledge instead of relying solely on scaleup. The authors argue that traditional methods, which focus on increasing dataset size and computational power, are unsustainable due to bottlenecks in data, computation, and trust. To address this issue, they introduce a framework that uses symbols, logic, and formulas as “building blocks” for model design, training, and interpretation. Empirical results show that their methods outperform those that follow the scaling law, with specific applications demonstrated in AI for science, such as drug-drug interaction prediction. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is trying to find a better way to make learning models work. Right now, people are just adding more data and computers to make them better, but this isn’t sustainable because it’s hard to get more data and computers are expensive. The authors are suggesting that we should use special knowledge about the problem we’re trying to solve to make our models simpler and more effective. They’ve developed a system that uses symbols, logic, and formulas to build these models, which they think will be better than just scaling up what we already have. They tested this idea with a specific task in AI for science, called predicting how two drugs interact. |
