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 351375 of 1394 papers

TitleStatusHype
Data Augmentation through Expert-guided Symmetry Detection to Improve Performance in Offline Reinforcement LearningCode0
An Alternative to EM for Gaussian Mixture Models: Batch and Stochastic Riemannian OptimizationCode0
DEDPUL: Difference-of-Estimated-Densities-based Positive-Unlabeled LearningCode0
Deep Autoencoding Gaussian Mixture Model for Unsupervised Anomaly DetectionCode0
Deep conditional distribution learning via conditional Föllmer flowCode0
On the representation and learning of monotone triangular transport mapsCode0
EX2: Exploration with Exemplar Models for Deep Reinforcement LearningCode0
Deep Density DestructorsCode0
Evidence Networks: simple losses for fast, amortized, neural Bayesian model comparisonCode0
Estimating Feature-Label Dependence Using Gini Distance StatisticsCode0
Estimating Probability Densities with Transformer and Denoising DiffusionCode0
ADVISE: ADaptive Feature Relevance and VISual Explanations for Convolutional Neural NetworksCode0
Estimating Density Models with Truncation Boundaries using Score MatchingCode0
Laplace approximation for logistic Gaussian process density estimation and regressionCode0
Deep Learning for MusicCode0
Bias and Generalization in Deep Generative Models: An Empirical StudyCode0
Expected Information Maximization: Using the I-Projection for Mixture Density EstimationCode0
Fast Kernel Density Estimation with Density Matrices and Random Fourier FeaturesCode0
Learning to Generate with MemoryCode0
Generalized Distribution Prediction for Asset ReturnsCode0
Efficient Mixture Learning in Black-Box Variational InferenceCode0
Efficient Neural Network Approaches for Conditional Optimal Transport with Applications in Bayesian InferenceCode0
A Triangular Network For Density EstimationCode0
Efficient and principled score estimation with Nyström kernel exponential familiesCode0
Efficient Out-of-Distribution Detection of Melanoma with Wavelet-based Normalizing FlowsCode0
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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
7Flow matchingNLL (bits/dim)2.99Unverified
8score SDENLL (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