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 1351–1394 of 1394 papers

TitleStatusHype
Sample-Optimal Density Estimation in Nearly-Linear Time—0
Distribution-aware Goal Prediction and Conformant Model-based Planning for Safe Autonomous Driving—0
Better Conditional Density Estimation for Neural Networks—0
Distribution learning via neural differential equations: a nonparametric statistical perspective—0
Distribution learning via neural differential equations: minimal energy regularization and approximation theory—0
Distributive Pre-Training of Generative Modeling Using Matrix-Product States—0
Sampling Constrained Continuous Probability Distributions: A Review—0
Diversity-Robust Acoustic Feature Signatures Based on Multiscale Fractal Dimension for Similarity Search of Environmental Sounds—0
Document Set Expansion with Positive-Unlabeled Learning: A Density Estimation-based Approach—0
VQ4ALL: Efficient Neural Network Representation via a Universal Codebook—0
Double Machine Learning Density Estimation for Local Treatment Effects with Instruments—0
DroneNet: Crowd Density Estimation using Self-ONNs for Drones—0
Benchmarking high-fidelity pedestrian tracking systems for research, real-time monitoring and crowd control—0
Dynamic DBSCAN with Euler Tour Sequences—0
Scalable Personalised Item Ranking through Parametric Density Estimation—0
Dynamic Maintenance of Kernel Density Estimation Data Structure: From Practice to Theory—0
Dynamic Sizing of Frequency Control Ancillary Service Requirements for a Philippine Grid—0
Bayesian Weapon System Reliability Modeling with Cox-Weibull Neural Network—0
Edge Detection based on Kernel Density Estimation—0
Effective LHC measurements with matrix elements and machine learning—0
Scalable Training of Mixture Models via Coresets—0
Efficient Bayesian Inverse Reinforcement Learning via Conditional Kernel Density Estimation—0
Efficient Bregman Range Search—0
Efficient computation and analysis of distributional Shapley values—0
Efficient Density Estimation via Piecewise Polynomial Approximation—0
Efficient Learning Under Density Shift in Incremental Settings Using Cramér-Rao-Based Regularization—0
Efficiently Computing Similarities to Private Datasets—0
Efficiently Learning Structured Distributions from Untrusted Batches—0
Efficient Minimax Strategies for Square Loss Games—0
Scale-Aware Network with Regional and Semantic Attentions for Crowd Counting under Cluttered Background—0
Efficient Multi-Frequency Phase Unwrapping using Kernel Density Estimation—0
Unbiasing Enhanced Sampling on a High-dimensional Free Energy Surface with Deep Generative Model—0
Uncertainty Intervals for Graph-based Spatio-Temporal Traffic Prediction—0
Efficient training of lightweight neural networks using Online Self-Acquired Knowledge Distillation—0
Eliminating Ratio Bias for Gradient-based Simulated Parameter Estimation—0
Empirical Density Estimation based on Spline Quasi-Interpolation with applications to Copulas clustering modeling—0
Empirical likelihood and uniform convergence rates for dyadic kernel density estimation—0
SceneGen: Generative Contextual Scene Augmentation using Scene Graph Priors—0
EMTL: A Generative Domain Adaptation Approach—0
Enabling Probabilistic Learning on Manifolds through Double Diffusion Maps—0
EndoBoost: a plug-and-play module for false positive suppression during computer-aided polyp detection in real-world colonoscopy (with dataset)—0
End-To-End Deep Learning-Based Adaptation Control for Frequency-Domain Adaptive System Identification—0
Enhanced Distribution Modelling via Augmented Architectures For Neural ODE Flows—0
Enhanced ECG Arrhythmia Detection Accuracy by Optimizing Divergence-Based Data Fusion—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