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 551–600 of 1394 papers

TitleStatusHype
Clustering Unclustered Data: Unsupervised Binary Labeling of Two Datasets Having Different Class Balances—0
Dynamic Sizing of Frequency Control Ancillary Service Requirements for a Philippine Grid—0
Dynamic Maintenance of Kernel Density Estimation Data Structure: From Practice to Theory—0
Clustering consistency with Dirichlet process mixtures—0
A RAD approach to deep mixture models—0
Dynamic DBSCAN with Euler Tour Sequences—0
Clustering by Deep Nearest Neighbor Descent (D-NND): A Density-based Parameter-Insensitive Clustering Method—0
DroneNet: Crowd Density Estimation using Self-ONNs for Drones—0
ClusterCluster: Parallel Markov Chain Monte Carlo for Dirichlet Process Mixtures—0
Double Machine Learning Density Estimation for Local Treatment Effects with Instruments—0
Closed-Loop Neural Operator-Based Observer of Traffic Density—0
Document Set Expansion with Positive-Unlabeled Learning: A Density Estimation-based Approach—0
Diversity-Robust Acoustic Feature Signatures Based on Multiscale Fractal Dimension for Similarity Search of Environmental Sounds—0
CLIP: Train Faster with Less Data—0
Quasi-Bayesian sequential deconvolution—0
AgRegNet: A Deep Regression Network for Flower and Fruit Density Estimation, Localization, and Counting in Orchards—0
Distributive Pre-Training of Generative Modeling Using Matrix-Product States—0
Distribution learning via neural differential equations: minimal energy regularization and approximation theory—0
Distribution learning via neural differential equations: a nonparametric statistical perspective—0
C-Learning: Learning to Achieve Goals via Recursive Classification—0
Distribution-aware Goal Prediction and Conformant Model-based Planning for Safe Autonomous Driving—0
Clean-NeRF: Reformulating NeRF to account for View-Dependent Observations—0
Distributed Nonparametric Estimation under Communication Constraints—0
Choice of V for V-Fold Cross-Validation in Least-Squares Density Estimation—0
Approximating Hierarchical MV-sets for Hierarchical Clustering—0
Distributed Nonparametric Estimation: from Sparse to Dense Samples per Terminal—0
Distributed Mean-Field Density Estimation for Large-Scale Systems—0
Distributed Density Filtering for Large-Scale Systems Using Mean-Filed Models—0
Distributed Collaborative 3D-Deployment of UAV Base Stations for On-Demand Coverage—0
Characteristic Neural Ordinary Differential Equations—0
Distributed Bayesian Estimation in Sensor Networks: Consensus on Marginal Densities—0
Distilling Normalizing Flows—0
Distances with mixed type variables some modified Gower's coefficients—0
Distances for WiFi Based Topological Indoor Mapping—0
Causal Covariate Shift Correction using Fisher information penalty—0
A PCA-Based Change Detection Framework for Multidimensional Data Streams: Change Detection in Multidimensional Data Streams—0
Agnostic Private Density Estimation for GMMs via List Global Stability—0
Adaptive learning of density ratios in RKHS—0
Content Reduction, Surprisal and Information Density Estimation for Long Documents—0
Cauchy-Schwarz Regularized Autoencoder—0
Discrepancy, Coresets, and Sketches in Machine Learning—0
Case Studies for Computing Density of Reachable States for Safe Autonomous Motion Planning—0
A partial likelihood approach to tree-based density modeling and its application in Bayesian inference—0
Discovery and density estimation of latent confounders in Bayesian networks with evidence lower bound—0
Discovering the neural correlate informed nosological relation among multiple neuropsychiatric disorders through dual utilisation of diagnostic information—0
DiSciPLE: Learning Interpretable Programs for Scientific Visual Discovery—0
Dirichlet process mixture models for non-stationary data streams—0
Can Kernel Transfer Operators Help Flow based Generative Models?—0
A parallelizable sampling method for parameter inference of large biochemical reaction models—0
Direct Importance Estimation with Model Selection and Its Application to Covariate Shift Adaptation—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