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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2402.15345 |
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| _version_ | 1866914689748828160 |
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| author | De la Fuente, Alfredo Singh, Saurabh Ballé, Johannes |
| author_facet | De la Fuente, Alfredo Singh, Saurabh Ballé, Johannes |
| contents | We introduce a lightweight, flexible and end-to-end trainable probability density model parameterized by a constrained Fourier basis. We assess its performance at approximating a range of multi-modal 1D densities, which are generally difficult to fit. In comparison to the deep factorized model introduced in [1], our model achieves a lower cross entropy at a similar computational budget. In addition, we also evaluate our method on a toy compression task, demonstrating its utility in learned compression. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_15345 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Fourier Basis Density Model De la Fuente, Alfredo Singh, Saurabh Ballé, Johannes Machine Learning We introduce a lightweight, flexible and end-to-end trainable probability density model parameterized by a constrained Fourier basis. We assess its performance at approximating a range of multi-modal 1D densities, which are generally difficult to fit. In comparison to the deep factorized model introduced in [1], our model achieves a lower cross entropy at a similar computational budget. In addition, we also evaluate our method on a toy compression task, demonstrating its utility in learned compression. |
| title | Fourier Basis Density Model |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2402.15345 |