Density Deconvolution with Normalizing Flows
arXiv:2006.09396
Abstract
Density deconvolution is the task of estimating a probability density function given only noise-corrupted samples. We can fit a Gaussian mixture model to the underlying density by maximum likelihood if the noise is normally distributed, but would like to exploit the superior density estimation performance of normalizing flows and allow for arbitrary noise distributions. Since both adjustments lead to an intractable likelihood, we resort to amortized variational inference. We demonstrate some problems involved in this approach, however, experiments on real data demonstrate that flows can already out-perform Gaussian mixtures for density deconvolution.
Appearing at the second workshop on Invertible Neural Networks, Normalizing Flows, and Explicit Likelihood Models (ICML 2020), Virtual Conference. 8 pages, 6 figures, 5 tables
References in corpus (15)
- PyTorch: An Imperative Style, High-Performance Deep Learning Library
- Auto-Encoding Variational Bayes
- Neural Discrete Representation Learning
- Variational Inference with Normalizing Flows
- Deep Unsupervised Clustering with Gaussian Mixture Variational Autoencoders
- Normalizing Flows for Probabilistic Modeling and Inference
- MADE: Masked Autoencoder for Distribution Estimation
- Variational Lossy Autoencoder
- Importance Weighted Autoencoders
- Reinterpreting Importance-Weighted Autoencoders
- Sum-of-Squares Polynomial Flow
- Autoregressive Energy Machines
- SUMO: Unbiased Estimation of Log Marginal Probability for Latent Variable Models
- Modeling the Gaia Color-Magnitude Diagram with Bayesian Neural Flows to Constrain Distance Estimates
- Scalable Extreme Deconvolution