Summary of Scalable Multi-task Transfer Learning For Molecular Property Prediction, by Chanhui Lee et al.
Scalable Multi-Task Transfer Learning for Molecular Property Prediction
by Chanhui Lee, Dae-Woong Jeong, Sung Moon Ko, Sumin Lee, Hyunseung Kim, Soorin Yim, Sehui Han, Sungwoong Kim, Sungbin Lim
First submitted to arxiv on: 1 Oct 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 Medium Difficulty summary: A novel paper proposes a data-driven approach to address limitations in foundation modeling for multi-task molecular property prediction. The current method relies on manual design of source-target task pairs using transfer learning, which is time-consuming and computationally expensive. To overcome these challenges, the authors introduce bi-level optimization to automatically obtain optimal transfer ratios. This enables scalable multi-task transfer learning for predicting 40 distinct molecular properties, resulting in improved performance and accelerated training convergence. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: Researchers are trying to find a better way to predict different properties of molecules. Right now, it’s hard to do this because we don’t have enough data or good models that can learn from what little data we have. A common solution is to use pre-trained models and adjust them for the specific task at hand. However, this approach has its limitations. To overcome these challenges, scientists developed a new method that uses computer algorithms to automatically find the best way to transfer knowledge between different tasks. This new method can predict many different molecular properties quickly and accurately. |
Keywords
» Artificial intelligence » Multi task » Optimization » Transfer learning
