# Density Estimation

358 papers with code • 15 benchmarks • 15 datasets

The goal of **Density Estimation** is to give an accurate description of the underlying probabilistic density distribution of an observable data set with unknown density.

Source: Contrastive Predictive Coding Based Feature for Automatic Speaker Verification

## Libraries

Use these libraries to find Density Estimation models and implementations## Datasets

## Most implemented papers

# Density estimation using Real NVP

Unsupervised learning of probabilistic models is a central yet challenging problem in machine learning.

# Importance Weighted Autoencoders

The variational autoencoder (VAE; Kingma, Welling (2014)) is a recently proposed generative model pairing a top-down generative network with a bottom-up recognition network which approximates posterior inference.

# Masked Autoregressive Flow for Density Estimation

By constructing a stack of autoregressive models, each modelling the random numbers of the next model in the stack, we obtain a type of normalizing flow suitable for density estimation, which we call Masked Autoregressive Flow.

# MADE: Masked Autoencoder for Distribution Estimation

There has been a lot of recent interest in designing neural network models to estimate a distribution from a set of examples.

# Point-Set Registration: Coherent Point Drift

The goal of point set registration is to assign correspondences between two sets of points and to recover the transformation that maps one point set to the other.

# PointConv: Deep Convolutional Networks on 3D Point Clouds

Besides, our experiments converting CIFAR-10 into a point cloud showed that networks built on PointConv can match the performance of convolutional networks in 2D images of a similar structure.

# Neural Spline Flows

A normalizing flow models a complex probability density as an invertible transformation of a simple base density.

# Progressive Distillation for Fast Sampling of Diffusion Models

Second, we present a method to distill a trained deterministic diffusion sampler, using many steps, into a new diffusion model that takes half as many sampling steps.

# FFJORD: Free-form Continuous Dynamics for Scalable Reversible Generative Models

The result is a continuous-time invertible generative model with unbiased density estimation and one-pass sampling, while allowing unrestricted neural network architectures.

# PixelSNAIL: An Improved Autoregressive Generative Model

Autoregressive generative models consistently achieve the best results in density estimation tasks involving high dimensional data, such as images or audio.