Summary of A Strong Baseline For Molecular Few-shot Learning, by Philippe Formont et al.
A Strong Baseline for Molecular Few-Shot Learning
by Philippe Formont, Hugo Jeannin, Pablo Piantanida, Ismail Ben Ayed
First submitted to arxiv on: 2 Apr 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 The proposed regularized quadratic-probe loss and block-coordinate descent optimizer for molecular data achieve highly competitive performances in few-shot learning, rivaling state-of-the-art meta-learning strategies while being more straightforward to implement. By fine-tuning pre-trained models on small datasets, the approach removes the need for specific episodic pre-training strategies and is applicable to black-box settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A simple approach to few-shot learning in drug discovery uses a regularized loss function based on Mahalanobis distance and a dedicated optimizer. This method achieves good results without requiring complex meta-learning strategies. It’s even better at handling changes between training and test data sets. |
Keywords
» Artificial intelligence » Few shot » Fine tuning » Loss function » Meta learning
