Touring sampling with pushforward maps
Fuente:
arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
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| _version_ | 1866913237877915648 |
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| author | Cabannes, Vivien Arnal, Charles |
| author_facet | Cabannes, Vivien Arnal, Charles |
| contents | The number of sampling methods could be daunting for a practitioner looking to cast powerful machine learning methods to their specific problem. This paper takes a theoretical stance to review and organize many sampling approaches in the ``generative modeling'' setting, where one wants to generate new data that are similar to some training examples. By revealing links between existing methods, it might prove useful to overcome some of the current challenges in sampling with diffusion models, such as long inference time due to diffusion simulation, or the lack of diversity in generated samples. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2311_13845 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Touring sampling with pushforward maps Cabannes, Vivien Arnal, Charles Machine Learning Artificial Intelligence The number of sampling methods could be daunting for a practitioner looking to cast powerful machine learning methods to their specific problem. This paper takes a theoretical stance to review and organize many sampling approaches in the ``generative modeling'' setting, where one wants to generate new data that are similar to some training examples. By revealing links between existing methods, it might prove useful to overcome some of the current challenges in sampling with diffusion models, such as long inference time due to diffusion simulation, or the lack of diversity in generated samples. |
| title | Touring sampling with pushforward maps |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2311.13845 |