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
Equipping Diffusion Models with Differentiable Spatial Entropy for Low-Light Image Enhancement
shermanlian/spatial-entropy-loss
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In this work, we propose a novel method that shifts the focus from a deterministic pixel-by-pixel comparison to a statistical perspective, emphasizing the learning of distributions rather than individual pixel values.
Normalizing Flows on the Product Space of SO(3) Manifolds for Probabilistic Human Pose Modeling
Normalizing flows have proven their efficacy for density estimation in Euclidean space, but their application to rotational representations, crucial in various domains such as robotics or human pose modeling, remains underexplored.
Document Set Expansion with Positive-Unlabelled Learning Using Intractable Density Estimation
beautifuldog01/document-set-expansion-pude
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The Document Set Expansion (DSE) task involves identifying relevant documents from large collections based on a limited set of example documents.
PaddingFlow: Improving Normalizing Flows with Padding-Dimensional Noise
To implement PaddingFlow, only the dimension of normalizing flows needs to be modified.
Sequential transport maps using SoS density estimation and $α$-divergences
Transport-based density estimation methods are receiving growing interest because of their ability to efficiently generate samples from the approximated density.
Gaussian Plane-Wave Neural Operator for Electron Density Estimation
holywater2/GPWNO
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This work studies machine learning for electron density prediction, which is fundamental for understanding chemical systems and density functional theory (DFT) simulations.
Less is KEN: a Universal and Simple Non-Parametric Pruning Algorithm for Large Language Models
itsmattei/ken
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This approach maintains model performance while allowing storage of only the optimized subnetwork, leading to significant memory savings.
Deep Conditional Generative Learning: Model and Error Analysis
burning489/conditionalfollmerflow
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We introduce an Ordinary Differential Equation (ODE) based deep generative method for learning a conditional distribution, named the Conditional Follmer Flow.
Vehicle Perception from Satellite
Satellites are capable of capturing high-resolution videos.
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.
