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

Showing 451475 of 1394 papers

TitleStatusHype
Conditional Image Generation with PixelCNN DecodersCode0
Evidence Networks: simple losses for fast, amortized, neural Bayesian model comparisonCode0
Approaches Toward Physical and General Video Anomaly DetectionCode0
Expected Information Maximization: Using the I-Projection for Mixture Density EstimationCode0
Conditional Density Estimation with Neural Networks: Best Practices and BenchmarksCode0
Conditional Density Estimation with Histogram TreesCode0
Estimating Feature-Label Dependence Using Gini Distance StatisticsCode0
Entropy-Informed Weighting Channel Normalizing FlowCode0
A Good Score Does not Lead to A Good Generative ModelCode0
Estimating Density Models with Truncation Boundaries using Score MatchingCode0
Estimating Probability Densities with Transformer and Denoising DiffusionCode0
Efficient Out-of-Distribution Detection of Melanoma with Wavelet-based Normalizing FlowsCode0
Conditional Density Estimation Tools in Python and R with Applications to Photometric Redshifts and Likelihood-Free Cosmological InferenceCode0
Space and Time Efficient Kernel Density Estimation in High DimensionsCode0
Document Set Expansion with Positive-Unlabelled Learning Using Intractable Density EstimationCode0
Empirical non-parametric estimation of the Fisher InformationCode0
ABC-CDE: Towards Approximate Bayesian Computation with Complex High-Dimensional Data and Limited SimulationsCode0
Efficient Mixture Learning in Black-Box Variational InferenceCode0
Uncovering Process Noise in LTV Systems via Kernel DeconvolutionCode0
Dynamic Feature Acquisition Using Denoising AutoencodersCode0
A Kernel Test of Goodness of FitCode0
Efficient and principled score estimation with Nyström kernel exponential familiesCode0
Efficient Neural Network Approaches for Conditional Optimal Transport with Applications in Bayesian InferenceCode0
Summarizing Bayesian Nonparametric Mixture Posterior -- Sliced Optimal Transport Metrics for Gaussian MixturesCode0
Enhancing Quantitative Image Synthesis through Pretraining and Resolution Scaling for Bone Mineral Density Estimation from a Plain X-ray ImageCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MAFLog-likelihood (nats)3,049Unverified
2DDPMNLL (bits/dim)3.69Unverified
3MRCNFNLL (bits/dim)3.54Unverified
4FFJORDNLL (bits/dim)3.4Unverified
5RNODENLL (bits/dim)3.38Unverified
6Pixel CNNNLL (bits/dim)3.03Unverified
7score SDENLL (bits/dim)2.99Unverified
8Flow matchingNLL (bits/dim)2.99Unverified
9Pixel CNN ++NLL (bits/dim)2.92Unverified
10Image TransformerNLL (bits/dim)2.9Unverified
#ModelMetricClaimedVerifiedStatus
1DVP-VAENLL77.1Unverified
2PaddingFlowMMD-L211Unverified
3FFJORDNLL (bits/dim)0.99Unverified
4RNODENLL (bits/dim)0.97Unverified
5IdentityNLL (bits/dim)0.13Unverified
6MADE MoGLog-likelihood (nats)-1,038.5Unverified
#ModelMetricClaimedVerifiedStatus
1nMDMALog-likelihood1.78Unverified
2DDELog-likelihood0.97Unverified
3B-NAFLog-likelihood0.61Unverified
4FFJORDLog-likelihood0.46Unverified
5MADE MoGLog-likelihood0.4Unverified
6PaddingFlowCD0.14Unverified
#ModelMetricClaimedVerifiedStatus
1TANLog-likelihood159.8Unverified
2FFJORDLog-likelihood157.4Unverified
3B-NAFLog-likelihood157.36Unverified
4MADE MoGLog-likelihood153.71Unverified
5PaddingFlowCD0.5Unverified
#ModelMetricClaimedVerifiedStatus
1GlowNLL (bits/dim)4.09Unverified
2Image TransformerNLL (bits/dim)3.77Unverified
3VDMNLL (bits/dim)3.72Unverified
4i-DODENLL (bits/dim)3.69Unverified
5MuLANNLL (bits/dim)3.67Unverified
#ModelMetricClaimedVerifiedStatus
1B-NAFLog-likelihood12.06Unverified
2DDELog-likelihood9.73Unverified
3FFJORDLog-likelihood8.59Unverified
4MADE MoGLog-likelihood8.47Unverified
5PaddingFlowCD0.89Unverified
#ModelMetricClaimedVerifiedStatus
1PaddingFlowCD13.8Unverified
2DDELog-likelihood-11.3Unverified
3B-NAFLog-likelihood-14.71Unverified
4FFJORDLog-likelihood-14.92Unverified
5MADE MoGLog-likelihood-15.15Unverified
#ModelMetricClaimedVerifiedStatus
1PaddingFlowCD24.5Unverified
2DDELog-likelihood-6.94Unverified
3B-NAFLog-likelihood-8.95Unverified
4FFJORDLog-likelihood-10.43Unverified
5MADE MoGLog-likelihood-12.27Unverified
#ModelMetricClaimedVerifiedStatus
1FFJORDNegative ELBO98.33Unverified
2B-NAFNegative ELBO94.83Unverified
3DVp-VAENLL89.07Unverified
4PaddingFlowMMD-L220.3Unverified
#ModelMetricClaimedVerifiedStatus
1FFJORDNegative ELBO104.03Unverified
2B-NAFNegative ELBO94.91Unverified
3PaddingFlowMMD-L217.9Unverified
#ModelMetricClaimedVerifiedStatus
1FFJORDNegative ELBO4.39Unverified
2B-NAFNegative ELBO4.33Unverified
3PaddingFlowMMD-L20.62Unverified
#ModelMetricClaimedVerifiedStatus
1RNODELog-likelihood1.04Unverified
#ModelMetricClaimedVerifiedStatus
1MAFLog-likelihood5,872Unverified
#ModelMetricClaimedVerifiedStatus
1RNODELog-likelihood3.83Unverified