Summary of Ensemble Model with Bert,roberta and Xlnet For Molecular Property Prediction, by Junling Hu
Ensemble Model With Bert,Roberta and Xlnet For Molecular property prediction
by Junling Hu
First submitted to arxiv on: 30 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Chemical Physics (physics.chem-ph)
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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 an innovative approach for predicting molecular properties with high accuracy, leveraging ensemble learning and supervised fine-tuning of BERT, RoBERTa, and XLNet models. By significantly outperforming existing advanced models, this method addresses the computational resource constraints faced by experimental groups, enabling accurate prediction of molecular properties without extensive pre-training. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to predict how molecules behave without needing lots of training data or powerful computers. It’s important because scientists who work with molecules often don’t have access to these resources. The approach uses special AI models that can be fine-tuned for specific tasks, making it more accurate and cost-effective than existing methods. |
Keywords
» Artificial intelligence » Bert » Fine tuning » Supervised
