Neural Networks for Density Estimation

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Generative Moment Matching Networks (2015, background): “Magdon-Ismail & Atiya (1998) proposed to use a neural network to learn a transformation from the data space to another space where the transformed data points are uniformly distributed.”
Noise Estimation Using Density Estimation for Self-Supervised Multimodal Learning (2020, methodology): “Existing density estimation algorithms can be divided into two categories: (a) parametric or semiparametric approaches such as Gaussian Mixture models [57,1,16,40,90] and probabilistic graphical models [25,29,87]; and (b) non-parametric approaches such as histograms [65], Splines [72,9], neural netw”
The Case for Learned Index Structures (2017, methodology): “Modeling CDFs: Our models for both range and point indexes are closely tied to models of the CDF. Estimating the CDF is non-trivial and has been studied in the machine learning community [50] with a few applications such as ranking [42].”
CLUB: A Contrastive Log-ratio Upper Bound of Mutual Information (2020, background): “However, learning a good marginal approximation r ( y ) without any additional information, recognized as the distribution density estimation problem (Magdon-Ismail & Atiya, 1999), is challenging, especially when variable y is in a high-dimensional space.”