Density Estimation
416 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
Benchmarks
These leaderboards are used to track progress in Density Estimation
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Libraries
Use these libraries to find Density Estimation models and implementationsDatasets
CIFAR-10
MNIST
CelebA-HQ
BSD
Caltech-101
Omniglot
JHU-CROWD++
UCF-CC-50
Silhouettes
Latest papers with no code
Risk Bounds for Mixture Density Estimation on Compact Domains via the $h$-Lifted Kullback--Leibler Divergence
We consider the problem of estimating probability density functions based on sample data, using a finite mixture of densities from some component class.
Online Estimation via Offline Estimation: An Information-Theoretic Framework
Our main results settle the statistical and computational complexity of online estimation in this framework.
Topological Feature Search Method for Multichannel EEG: Application in ADHD classification
Then, multi-dimensional time series are re-embedded, and TDA is applied to obtain topological feature information.
MCNet: A crowd denstity estimation network based on integrating multiscale attention module
Finally, this paper integrates IMA module and the lightweight crowd texture feature extraction network to construct the MCNet, and validate the feasibility of this network on image classification dataset: Cifar10 and four crowd density datasets: PETS2009, Mall, QUT and SH_METRO to validate the MCNet whether can be a suitable solution for crowd density estimation in metro video surveillance where there are image processing challenges such as high density, high occlusion, perspective distortion and limited hardware resources.
Goal-Oriented Bayesian Optimal Experimental Design for Nonlinear Models using Markov Chain Monte Carlo
We present a computational framework of predictive goal-oriented OED (GO-OED) suitable for nonlinear observation and prediction models, which seeks the experimental design providing the greatest EIG on the QoIs.
Improved Soft-k-Means Clustering Algorithm for Balancing Energy Consumption in Wireless Sensor Networks
Energy load balancing is an essential issue in designing wireless sensor networks (WSNs).
MULDE: Multiscale Log-Density Estimation via Denoising Score Matching for Video Anomaly Detection
We propose a novel approach to video anomaly detection: we treat feature vectors extracted from videos as realizations of a random variable with a fixed distribution and model this distribution with a neural network.
Probabilistic reachable sets of stochastic nonlinear systems with contextual uncertainties
The existing methods of computing probabilistic reachable sets normally assume that the uncertainties are independent of the state.
Neural-Kernel Conditional Mean Embeddings
In conditional density estimation tasks, our NN-CME hybrid achieves competitive performance and often surpasses existing deep learning-based methods.
SCILLA: SurfaCe Implicit Learning for Large Urban Area, a volumetric hybrid solution
SCILLA's hybrid architecture models two separate implicit fields: one for the volumetric density and another for the signed distance to the surface.
