Unlocking Guidance for Discrete State-Space Diffusion and Flow Models
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866910893308116992 |
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| author | Nisonoff, Hunter Xiong, Junhao Allenspach, Stephan Listgarten, Jennifer |
| author_facet | Nisonoff, Hunter Xiong, Junhao Allenspach, Stephan Listgarten, Jennifer |
| contents | Generative models on discrete state-spaces have a wide range of potential applications, particularly in the domain of natural sciences. In continuous state-spaces, controllable and flexible generation of samples with desired properties has been realized using guidance on diffusion and flow models. However, these guidance approaches are not readily amenable to discrete state-space models. Consequently, we introduce a general and principled method for applying guidance on such models. Our method depends on leveraging continuous-time Markov processes on discrete state-spaces, which unlocks computational tractability for sampling from a desired guided distribution. We demonstrate the utility of our approach, Discrete Guidance, on a range of applications including guided generation of small-molecules, DNA sequences and protein sequences. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2406_01572 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Unlocking Guidance for Discrete State-Space Diffusion and Flow Models Nisonoff, Hunter Xiong, Junhao Allenspach, Stephan Listgarten, Jennifer Machine Learning Generative models on discrete state-spaces have a wide range of potential applications, particularly in the domain of natural sciences. In continuous state-spaces, controllable and flexible generation of samples with desired properties has been realized using guidance on diffusion and flow models. However, these guidance approaches are not readily amenable to discrete state-space models. Consequently, we introduce a general and principled method for applying guidance on such models. Our method depends on leveraging continuous-time Markov processes on discrete state-spaces, which unlocks computational tractability for sampling from a desired guided distribution. We demonstrate the utility of our approach, Discrete Guidance, on a range of applications including guided generation of small-molecules, DNA sequences and protein sequences. |
| title | Unlocking Guidance for Discrete State-Space Diffusion and Flow Models |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2406.01572 |