Fourier Basis Density Model
<|reference_start|>Fourier Basis Density Model: 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.<|reference_end|>
@article{de la fuente2024fourier,
    title={Fourier Basis Density Model},
    author={Alfredo De la Fuente, Saurabh Singh, Johannes Ball'e},
    journal={arXiv preprint arXiv:2402.15345},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.15345},
    primaryClass={cs.LG stat.ML}
}