Data-driven deep density estimation

@article{Puchert2021DatadrivenDD,
  title={Data-driven deep density estimation},
  author={Patrik Puchert and Pedro Hermosilla and Tobias Ritschel and Timo Ropinski},
  journal={ArXiv},
  year={2021},
  volume={abs/2107.11085}
}
Density estimation plays a crucial role in many data analysis tasks, as it infers a continuous probability density function (PDF) from discrete samples. Thus, it is used in tasks as diverse as analyzing population data, spatial locations in 2D sensor readings, or reconstructing scenes from 3D scans. In this paper, we introduce a learned, data-driven deep density estimation (DDE) to infer PDFs in an accurate and efficient manner, while being independent of domain dimensionality or sample size… Expand

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