Summary of Training Neural Samplers with Reverse Diffusive Kl Divergence, by Jiajun He et al.
Training Neural Samplers with Reverse Diffusive KL Divergence
by Jiajun He, Wenlin Chen, Mingtian Zhang, David Barber, José Miguel Hernández-Lobato
First submitted to arxiv on: 16 Oct 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 approach to training generative models focuses on minimizing the reverse Kullback-Leibler divergence along diffusion trajectories of model and target densities. This allows for efficient generation of samples from the target distribution in one step, while also capturing multiple modes. The method is demonstrated to enhance sampling performance across various Boltzmann distributions, including synthetic multi-modal densities and n-body particle systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new way to train generative models that can sample from unnormalized density functions. It uses a different approach than traditional methods, which are limited by their tendency to seek modes in the data. The proposed method is called reverse diffusive KL divergence and it allows the model to capture multiple modes. This means the model can generate samples from complex distributions with ease. |
Keywords
» Artificial intelligence » Diffusion » Multi modal
