Summary of Fi-cbl: a Probabilistic Method For Concept-based Learning with Expert Rules, by Lev V. Utkin et al.
FI-CBL: A Probabilistic Method for Concept-Based Learning with Expert Rules
by Lev V. Utkin, Andrei V. Konstantinov, Stanislav R. Kirpichenko
First submitted to arxiv on: 28 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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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 Frequentist Inference CBL (FI-CBL) method tackles concept-based learning problems by transforming annotated images into patches, then clustering these patches based on their embeddings generated using an autoencoder. To identify concepts in new images, FI-CBL employs frequentist inference, calculating prior and posterior probabilities of concepts based on patch rates from training data with specific concept values. This approach allows incorporating expert rules as logic functions to update probabilistic calculations. Numerical experiments demonstrate that FI-CBL outperforms the concept bottleneck model when training data is limited. The algorithm’s code is publicly available for further exploration. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to learn concepts is being explored by scientists. They’re taking images and breaking them into small pieces, then grouping these pieces based on what they look like. This helps them figure out which concepts are present in a new image. It’s like using a special kind of logic to make predictions. The team has made this process more accurate by incorporating expert knowledge into the calculations. They’ve tested their method and found it works better than another popular approach when there isn’t much training data available. |
Keywords
» Artificial intelligence » Autoencoder » Clustering » Inference
