Density Estimation
412 papers with code • 14 benchmarks • 14 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 implementationsDatasets
Most implemented papers
Denoising Diffusion Probabilistic Models
We present high quality image synthesis results using diffusion probabilistic models, a class of latent variable models inspired by considerations from nonequilibrium thermodynamics.
Density estimation using Real NVP
Unsupervised learning of probabilistic models is a central yet challenging problem in machine learning.
Glow: Generative Flow with Invertible 1x1 Convolutions
Flow-based generative models (Dinh et al., 2014) are conceptually attractive due to tractability of the exact log-likelihood, tractability of exact latent-variable inference, and parallelizability of both training and synthesis.
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.
Conditional Image Generation with PixelCNN Decoders
This work explores conditional image generation with a new image density model based on the PixelCNN architecture.
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.
Score-Based Generative Modeling through Stochastic Differential Equations
Combined with multiple architectural improvements, we achieve record-breaking performance for unconditional image generation on CIFAR-10 with an Inception score of 9. 89 and FID of 2. 20, a competitive likelihood of 2. 99 bits/dim, and demonstrate high fidelity generation of 1024 x 1024 images for the first time from a score-based generative model.
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.