Density Estimation
417 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
Nonparametric Estimation via Variance-Reduced Sketching
ivanpeng0414/nonparametric-estimation-via-variance-reduced-sketching
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In this paper, we introduce a new framework called Variance-Reduced Sketching (VRS), specifically designed to estimate density functions and nonparametric regression functions in higher dimensions with a reduced curse of dimensionality.
Learning from Sparse Offline Datasets via Conservative Density Estimation
Offline reinforcement learning (RL) offers a promising direction for learning policies from pre-collected datasets without requiring further interactions with the environment.
A Good Score Does not Lead to A Good Generative Model
In particular, it has been shown that SGMs can generate samples from a distribution that is close to the ground-truth if the underlying score function is learned well, suggesting the success of SGM as a generative model.
PhilEO Bench: Evaluating Geo-Spatial Foundation Models
Massive amounts of unlabelled data are captured by Earth Observation (EO) satellites, with the Sentinel-2 constellation generating 1. 6 TB of data daily.
Count What You Want: Exemplar Identification and Few-shot Counting of Human Actions in the Wild
To develop and evaluate our approach, we introduce a diverse and realistic dataset consisting of real-world data from 37 subjects and 50 action categories, encompassing both sensor and audio data.
Mean-field underdamped Langevin dynamics and its spacetime discretization
qiangfu09/nula
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We propose a new method called the N-particle underdamped Langevin algorithm for optimizing a special class of non-linear functionals defined over the space of probability measures.
Diffusion Models With Learned Adaptive Noise
Diffusion models have gained traction as powerful algorithms for synthesizing high-quality images.
Label-Free Multivariate Time Series Anomaly Detection
zqhang/mtgflow
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In this paper, we propose MTGFlow, an unsupervised anomaly detection approach for MTS anomaly detection via dynamic Graph and entity-aware normalizing Flow.
Stochastic Bayesian Optimization with Unknown Continuous Context Distribution via Kernel Density Estimation
lamda-bbo/sbokde
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Considering that the estimated PDF may have high estimation error when the true distribution is complicated, we further propose the second algorithm that optimizes the distributionally robust objective.
$ρ$-Diffusion: A diffusion-based density estimation framework for computational physics
In physics, density $\rho(\cdot)$ is a fundamentally important scalar function to model, since it describes a scalar field or a probability density function that governs a physical process.
