Summary of Discrete Copula Diffusion, by Anji Liu et al.
Discrete Copula Diffusion
by Anji Liu, Oliver Broadrick, Mathias Niepert, Guy Van den Broeck
First submitted to arxiv on: 2 Oct 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 Discrete diffusion models have made significant progress in modeling complex data like natural languages and DNA sequences. However, unlike diffusion models for continuous data, which can generate high-quality samples in just a few denoising steps, modern discrete diffusion models still require hundreds or thousands of steps to perform well. This is because they fail to capture dependencies between output variables at each step. We identified this limitation and introduced a general approach to supplement the missing dependency information by incorporating another deep generative model, called the copula model. Our method doesn’t require fine-tuning either model, yet it enables high-quality sample generation with significantly fewer steps. When we applied this approach to autoregressive copula models, the combined model outperformed both models individually in unconditional and conditional text generation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Discrete diffusion models are great for modeling complex data like languages and DNA. But they have a problem: they can’t capture relationships between different parts of the output as easily as continuous models can. We found this limitation and came up with a way to fix it by combining discrete diffusion models with another type of model called copula models. This new approach doesn’t require adjusting either model, and it lets us generate high-quality samples with fewer steps. When we tested this idea on autoregressive copula models, the combined model did better than each individual model in generating text. |
Keywords
» Artificial intelligence » Autoregressive » Diffusion » Fine tuning » Generative model » Text generation
