Summary of Discussion: Effective and Interpretable Outcome Prediction by Training Sparse Mixtures Of Linear Experts, By Francesco Folino et al.
Discussion: Effective and Interpretable Outcome Prediction by Training Sparse Mixtures of Linear Experts
by Francesco Folino, Luigi Pontieri, Pietro Sabatino
First submitted to arxiv on: 18 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 sparse Mixture-of-Experts model combines ensemble and deep learning methods to predict discrete process outcomes from partial traces, achieving top accuracy while maintaining transparency. The model features Logistic Regressors as both gate and expert sub-nets, automatically selecting input features in each sub-net during training. This end-to-end approach replaces traditional global feature selection, improving interpretability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper predicts a discrete property of an unfinished process from its partial trace using a sparse Mixture-of-Experts model. The model uses Logistic Regressors for both gate and expert sub-nets, selecting features automatically during training. This helps keep the prediction transparent and accurate. |
Keywords
* Artificial intelligence * Deep learning * Feature selection * Mixture of experts
