Deep PDF: Probabilistic Surface Optimization and Density Estimation
Dmitry Kopitkov, Vadim Indelman

TL;DR
DeepPDF introduces a neural network-based non-parametric density estimation method using Probabilistic Surface Optimization, enabling accurate, fast, and flexible density inference from samples, surpassing traditional KDE techniques.
Contribution
The paper proposes DeepPDF, a novel neural network approach for density estimation that employs PSO for high accuracy and efficiency, overcoming limitations of kernel-based methods.
Findings
DeepPDF achieves higher accuracy than KDE.
The method allows fast evaluation of density at query points.
PSO can infer sample frequencies and assist in other statistical tasks.
Abstract
A probability density function (pdf) encodes the entire stochastic knowledge about data distribution, where data may represent stochastic observations in robotics, transition state pairs in reinforcement learning or any other empirically acquired modality. Inferring data pdf is of prime importance, allowing to analyze various model hypotheses and perform smart decision making. However, most density estimation techniques are limited in their representation expressiveness to specific kernel type or predetermined distribution family, and have other restrictions. For example, kernel density estimation (KDE) methods require meticulous parameter search and are extremely slow at querying new points. In this paper we present a novel non-parametric density estimation approach, DeepPDF, that uses a neural network to approximate a target pdf given samples from thereof. Such a representation…
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Taxonomy
TopicsGenerative Adversarial Networks and Image Synthesis · Gaussian Processes and Bayesian Inference · Advanced Neural Network Applications
