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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

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Datasets

Greatest papers with code

The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables

2 Nov 2016tensorflow/models

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.

DENSITY ESTIMATION STRUCTURED PREDICTION

Density estimation using Real NVP

27 May 2016tensorflow/models

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

DENSITY ESTIMATION IMAGE GENERATION

On the Discrepancy between Density Estimation and Sequence Generation

17 Feb 2020tensorflow/tensor2tensor

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

Gaussian Gated Linear Networks

NeurIPS 2020 deepmind/deepmind-research

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

DENOISING DENSITY ESTIMATION MULTI-ARMED BANDITS

Masked Autoregressive Flow for Density Estimation

NeurIPS 2017 tensorflow/probability

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.

DENSITY ESTIMATION

Learning to Prune in Metric and Non-Metric Spaces

NeurIPS 2013 searchivarius/NonMetricSpaceLib

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

DENSITY ESTIMATION

Importance Weighted Autoencoders

1 Sep 2015AntixK/PyTorch-VAE

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.

DENSITY ESTIMATION

CNN-based Density Estimation and Crowd Counting: A Survey

28 Mar 2020gjy3035/Awesome-Crowd-Counting

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.

CROWD COUNTING DENSITY ESTIMATION OBJECT COUNTING

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

ICLR 2019 rtqichen/ffjord

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)

DENSITY ESTIMATION IMAGE GENERATION VARIATIONAL INFERENCE

MADE: Masked Autoencoder for Distribution Estimation

12 Feb 2015karpathy/pytorch-made

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

DENSITY ESTIMATION IMAGE GENERATION