Summary of Generative Conditional Distributions by Neural (entropic) Optimal Transport, By Bao Nguyen et al.
Generative Conditional Distributions by Neural (Entropic) Optimal Transport
by Bao Nguyen, Binh Nguyen, Hieu Trung Nguyen, Viet Anh Nguyen
First submitted to arxiv on: 4 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 This paper introduces a novel neural entropic optimal transport method for learning generative models of conditional distributions. The goal is to learn multiple distributions that correspond to different instances of the covariates, which is challenging due to limited sample sizes. The proposed approach relies on two neural networks: one for inverse cumulative distribution functions and another for conditional Kantorovich potential. To prevent overfitting, a regularization term is added to penalize the Lipschitz constant. Experimental results on real-world datasets demonstrate the effectiveness of the algorithm compared to state-of-the-art techniques. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us learn how things might look if certain conditions are met. It’s hard because we need many examples, but what if we only have a few? The researchers created a new way to use neural networks to solve this problem. They use two types of networks: one that creates patterns and another that adjusts the patterns based on the conditions. To make sure their results aren’t too perfect and don’t fit the noise, they add a special penalty term. They tested their method on real data and it worked better than other methods. |
Keywords
» Artificial intelligence » Overfitting » Regularization
