Summary of Variational Distillation Of Diffusion Policies Into Mixture Of Experts, by Hongyi Zhou et al.
Variational Distillation of Diffusion Policies into Mixture of Experts
by Hongyi Zhou, Denis Blessing, Ge Li, Onur Celik, Xiaogang Jia, Gerhard Neumann, Rudolf Lioutikov
First submitted to arxiv on: 18 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Robotics (cs.RO)
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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 paper introduces Variational Diffusion Distillation (VDD), a novel method that distills denoising diffusion policies into Mixtures of Experts (MoE) through variational inference. It combines the expressiveness of Diffusion Models with the benefits of Mixture Models, addressing drawbacks such as intractable likelihoods and long inference times. The approach leverages a decompositional upper bound of the variational objective to train each expert separately, resulting in a robust optimization scheme for MoEs. The VDD method demonstrates accuracy in distilling complex distributions learned by diffusion models, outperforming existing state-of-the-art distillation methods, and surpassing conventional methods for training MoE. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary VDD is a new way to use machine learning to help robots learn from humans. It’s based on “diffusion models” which are very good at learning complex things like how humans move or behave. But these models have some problems – it takes them a long time to make predictions, and they’re hard to understand. VDD solves this by using something called “mixtures of experts”. This makes the model faster and easier to understand, while still being able to learn complex things. |
Keywords
» Artificial intelligence » Diffusion » Distillation » Inference » Machine learning » Optimization
