SOTAVerified

Density Estimation

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

Papers

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Showing 501–550 of 1394 papers

TitleStatusHype
ADASSM: Adversarial Data Augmentation in Statistical Shape Models From Images—0
Fast Private Kernel Density Estimation via Locality Sensitive QuantizationCode0
A maximal inequality for local empirical processes under weak dependence—0
Numerical Data Imputation for Multimodal Data Sets: A Probabilistic Nearest-Neighbor Kernel Density ApproachCode0
About the Cost of Central Privacy in Density Estimation—0
Distributive Pre-Training of Generative Modeling Using Matrix-Product States—0
Data Structures for Density EstimationCode0
Probabilistic Matching of Real and Generated Data Statistics in Generative Adversarial NetworksCode0
Learning Unnormalized Statistical Models via Compositional Optimization—0
A New Probabilistic Distance Metric With Application In Gaussian Mixture Reduction—0
MESSY Estimation: Maximum-Entropy based Stochastic and Symbolic densitY Estimation—0
High-dimensional and Permutation Invariant Anomaly DetectionCode0
Learning to Simulate Tree-Branch Dynamics for Manipulation—0
Enhanced Distribution Modelling via Augmented Architectures For Neural ODE Flows—0
Faster Training of Diffusion Models and Improved Density Estimation via Parallel Score Matching—0
Approximate Stein Classes for Truncated Density EstimationCode0
Anomaly Detection with Variance Stabilized Density EstimationCode0
Balanced Training of Energy-Based Models with Adaptive Flow Sampling—0
Hinge-Wasserstein: Estimating Multimodal Aleatoric Uncertainty in Regression TasksCode0
A Fitness-assignment Method for Evolutionary Constrained Multi-objective Optimization—0
One-Line-of-Code Data Mollification Improves Optimization of Likelihood-based Generative ModelsCode0
Kernel Density Matrices for Probabilistic Deep LearningCode0
Generalizable Pose Estimation Using Implicit Scene RepresentationsCode0
Hybrid Energy Based Model in the Feature Space for Out-of-Distribution DetectionCode0
Counterfactual Generative Models for Time-Varying TreatmentsCode0
Improving Heterogeneous Model Reuse by Density EstimationCode0
A Model Stealing Attack Against Multi-Exit Networks—0
Complexity measure, kernel density estimation, bandwidth selection, and the efficient market hypothesis—0
Evidence Networks: simple losses for fast, amortized, neural Bayesian model comparisonCode0
Bounded KRnet and its applications to density estimation and approximation—0
Grenander-type Density Estimation under Myerson Regularity—0
MEGABYTE: Predicting Million-byte Sequences with Multiscale Transformers—0
Unsupervised Domain Adaptation for Medical Image Segmentation via Feature-space Density Matching—0
Unlocking the Power of Representations in Long-term Novelty-based Exploration—0
Deep Learning assisted microwave-plasma interaction based technique for plasma density estimation—0
Positive definite nonparametric regression using an evolutionary algorithm with application to covariance function estimationCode0
Persistently Trained, Diffusion-assisted Energy-based ModelsCode0
Probabilistic Forecast-based Portfolio Optimization of Electricity Demand at Low Aggregation Levels—0
Finite-Sample Bounds for Adaptive Inverse Reinforcement Learning using Passive Langevin Dynamics—0
Cross Attention Transformers for Multi-modal Unsupervised Whole-Body PET Anomaly Detection—0
Copula-Based Density Estimation Models for Multivariate Zero-Inflated Continuous Data—0
Thread Counting in Plain Weave for Old Paintings Using Semi-Supervised Regression Deep Learning ModelsCode0
Clean-NeRF: Reformulating NeRF to account for View-Dependent Observations—0
Kernel Density Bayesian Inverse Reinforcement LearningCode0
Ultra-High-Resolution Detector Simulation with Intra-Event Aware GAN and Self-Supervised Relational ReasoningCode0
Progressive Bayesian Particle Flows based on Optimal Transport Map Sequences—0
A System of ODEs for Representing Trends of CGM Signals—0
Mixtures of All TreesCode0
Comparative Study of Coupling and Autoregressive Flows through Robust Statistical TestsCode0
Private Statistical Estimation of Many Quantiles—0
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All datasetsCIFAR-10MNISTUCI POWERBSDS300ImageNet 32x32UCI GASUCI HEPMASSUCI MINIBOONEOMNIGLOTCaltech-101FreyfacesCelebA-HQ 256x256

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MAFLog-likelihood (nats)3,049—Unverified
2DDPMNLL (bits/dim)3.69—Unverified
3MRCNFNLL (bits/dim)3.54—Unverified
4FFJORDNLL (bits/dim)3.4—Unverified
5RNODENLL (bits/dim)3.38—Unverified
6Pixel CNNNLL (bits/dim)3.03—Unverified
7score SDENLL (bits/dim)2.99—Unverified
8Flow matchingNLL (bits/dim)2.99—Unverified
9Pixel CNN ++NLL (bits/dim)2.92—Unverified
10Image TransformerNLL (bits/dim)2.9—Unverified
#ModelMetricClaimedVerifiedStatus
1DVP-VAENLL77.1—Unverified
2PaddingFlowMMD-L211—Unverified
3FFJORDNLL (bits/dim)0.99—Unverified
4RNODENLL (bits/dim)0.97—Unverified
5IdentityNLL (bits/dim)0.13—Unverified
6MADE MoGLog-likelihood (nats)-1,038.5—Unverified
#ModelMetricClaimedVerifiedStatus
1nMDMALog-likelihood1.78—Unverified
2DDELog-likelihood0.97—Unverified
3B-NAFLog-likelihood0.61—Unverified
4FFJORDLog-likelihood0.46—Unverified
5MADE MoGLog-likelihood0.4—Unverified
6PaddingFlowCD0.14—Unverified
#ModelMetricClaimedVerifiedStatus
1TANLog-likelihood159.8—Unverified
2FFJORDLog-likelihood157.4—Unverified
3B-NAFLog-likelihood157.36—Unverified
4MADE MoGLog-likelihood153.71—Unverified
5PaddingFlowCD0.5—Unverified
#ModelMetricClaimedVerifiedStatus
1GlowNLL (bits/dim)4.09—Unverified
2Image TransformerNLL (bits/dim)3.77—Unverified
3VDMNLL (bits/dim)3.72—Unverified
4i-DODENLL (bits/dim)3.69—Unverified
5MuLANNLL (bits/dim)3.67—Unverified
#ModelMetricClaimedVerifiedStatus
1B-NAFLog-likelihood12.06—Unverified
2DDELog-likelihood9.73—Unverified
3FFJORDLog-likelihood8.59—Unverified
4MADE MoGLog-likelihood8.47—Unverified
5PaddingFlowCD0.89—Unverified
#ModelMetricClaimedVerifiedStatus
1PaddingFlowCD13.8—Unverified
2DDELog-likelihood-11.3—Unverified
3B-NAFLog-likelihood-14.71—Unverified
4FFJORDLog-likelihood-14.92—Unverified
5MADE MoGLog-likelihood-15.15—Unverified
#ModelMetricClaimedVerifiedStatus
1PaddingFlowCD24.5—Unverified
2DDELog-likelihood-6.94—Unverified
3B-NAFLog-likelihood-8.95—Unverified
4FFJORDLog-likelihood-10.43—Unverified
5MADE MoGLog-likelihood-12.27—Unverified
#ModelMetricClaimedVerifiedStatus
1FFJORDNegative ELBO98.33—Unverified
2B-NAFNegative ELBO94.83—Unverified
3DVp-VAENLL89.07—Unverified
4PaddingFlowMMD-L220.3—Unverified
#ModelMetricClaimedVerifiedStatus
1FFJORDNegative ELBO104.03—Unverified
2B-NAFNegative ELBO94.91—Unverified
3PaddingFlowMMD-L217.9—Unverified
#ModelMetricClaimedVerifiedStatus
1FFJORDNegative ELBO4.39—Unverified
2B-NAFNegative ELBO4.33—Unverified
3PaddingFlowMMD-L20.62—Unverified
#ModelMetricClaimedVerifiedStatus
1RNODELog-likelihood1.04—Unverified
#ModelMetricClaimedVerifiedStatus
1MAFLog-likelihood5,872—Unverified
#ModelMetricClaimedVerifiedStatus
1RNODELog-likelihood3.83—Unverified