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

The essence of the trick is to refactor each stochastic node into a differentiable function of its parameters and a random variable with fixed distribution.

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

Ranked #11 on Image Generation on ImageNet 32x32

In this paper, by comparing several density estimators on five machine translation tasks, we find that the correlation between rankings of models based on log-likelihood and BLEU varies significantly depending on the range of the model families being compared.

DENSITY ESTIMATION LATENT VARIABLE MODELS MACHINE TRANSLATION STRUCTURED PREDICTION

We propose the Gaussian Gated Linear Network (G-GLN), an extension to the recently proposed GLN family of deep neural networks.

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.

Ranked #1 on Density Estimation on CIFAR-10

Our focus is on approximate nearest neighbor retrieval in metric and non-metric spaces.

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.

Through our analysis, we expect to make reasonable inference and prediction for the future development of crowd counting, and meanwhile, it can also provide feasible solutions for the problem of object counting in other fields.

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

Ranked #1 on Density Estimation on CIFAR-10 (NLL metric)

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

Ranked #4 on Density Estimation on UCI GAS