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


Use these libraries to find Density Estimation models and implementations

Most implemented papers

Density estimation using Real NVP

tensorflow/models 27 May 2016

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

Importance Weighted Autoencoders

AntixK/PyTorch-VAE 1 Sep 2015

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

gpapamak/maf NeurIPS 2017

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

mgermain/MADE 12 Feb 2015

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

neka-nat/probreg 15 May 2009

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

DylanWusee/pointconv CVPR 2019

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

bayesiains/nsf NeurIPS 2019

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

google-research/google-research ICLR 2022

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

rtqichen/ffjord ICLR 2019

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

neocxi/pixelsnail-public ICML 2018

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