Summary of Generative Vs. Discriminative Modeling Under the Lens Of Uncertainty Quantification, by Elouan Argouarc’h et al.
Generative vs. Discriminative modeling under the lens of uncertainty quantification
by Elouan Argouarc’h, François Desbouvries, Eric Barat, Eiji Kawasaki
First submitted to arxiv on: 13 Jun 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); Methodology (stat.ME)
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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 paper compares generative and discriminative approaches for learning parametric models from datasets. These approaches differ in their construction and inference problems, with generative methods modeling the joint distribution of variables and discriminative methods focusing on conditional probability distributions. The paper assesses the role of prior distributions and observed variables in both approaches, discussing issues like imbalanced datasets and semi-supervised learning. Practical insights are provided for supervised and semi-supervised learning using neural networks. The authors propose a general sampling scheme for both approaches and validate their findings through classification simulations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper compares two ways to learn from data: generative and discriminative models. Generative models try to recreate the underlying process that generated the data, while discriminative models focus on making predictions about new data. The authors compare how these different approaches work and which one is better at handling certain types of data. They also discuss some common challenges, like dealing with imbalanced datasets and using a mix of labeled and unlabeled training data. The paper shows that one approach can be better than the other in certain situations. |
Keywords
» Artificial intelligence » Classification » Inference » Probability » Semi supervised » Supervised
