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 451–500 of 1394 papers

TitleStatusHype
Flowification: Everything is a Normalizing FlowCode0
Accelerating Continuous Normalizing Flow with Trajectory Polynomial RegularizationCode0
Approaches Toward Physical and General Video Anomaly DetectionCode0
Consistent and Flexible Selectivity Estimation for High-Dimensional DataCode0
Mitigating Bias in Dataset DistillationCode0
Conformalized High-Density Quantile Regression via Dynamic Prototypes-based Probability Density EstimationCode0
Fast Private Kernel Density Estimation via Locality Sensitive QuantizationCode0
Flow++: Improving Flow-Based Generative Models with Variational Dequantization and Architecture DesignCode0
A Good Score Does not Lead to A Good Generative ModelCode0
Fast ε-free Inference of Simulation Models with Bayesian Conditional Density EstimationCode0
A Survey of Recent Advances in CNN-based Single Image Crowd Counting and Density EstimationCode0
Fast ε-free Inference of Simulation Models with Bayesian Conditional Density EstimationCode0
Fast Dynamic Routing Based on Weighted Kernel Density EstimationCode0
Space and Time Efficient Kernel Density Estimation in High DimensionsCode0
Document Set Expansion with Positive-Unlabelled Learning Using Intractable Density EstimationCode0
Exploring the Training Robustness of Distributional Reinforcement Learning against Noisy State ObservationsCode0
ABC-CDE: Towards Approximate Bayesian Computation with Complex High-Dimensional Data and Limited SimulationsCode0
Exponential Family Model-Based Reinforcement Learning via Score MatchingCode0
Uncovering Process Noise in LTV Systems via Kernel DeconvolutionCode0
Dynamic Feature Acquisition Using Denoising AutoencodersCode0
A Kernel Test of Goodness of FitCode0
Conditional Image Generation with PixelCNN DecodersCode0
Data Augmentation through Expert-guided Symmetry Detection to Improve Performance in Offline Reinforcement LearningCode0
Summarizing Bayesian Nonparametric Mixture Posterior -- Sliced Optimal Transport Metrics for Gaussian MixturesCode0
Extremely Randomized CNets for Multi-label ClassificationCode0
Efficient and principled score estimation with Nyström kernel exponential familiesCode0
CNN-based Cascaded Multi-task Learning of High-level Prior and Density Estimation for Crowd CountingCode0
TabPFN: One Model to Rule Them All?Code0
Fast Kernel Density Estimation with Density Matrices and Random Fourier FeaturesCode0
Conditional Density Estimation with Neural Networks: Best Practices and BenchmarksCode0
Conditional Density Estimation with Histogram TreesCode0
The DEformer: An Order-Agnostic Distribution Estimating TransformerCode0
EX2: Exploration with Exemplar Models for Deep Reinforcement LearningCode0
Evidence Networks: simple losses for fast, amortized, neural Bayesian model comparisonCode0
Efficient Mixture Learning in Black-Box Variational InferenceCode0
Estimating Density Models with Truncation Boundaries using Score MatchingCode0
Conditional Density Estimation Tools in Python and R with Applications to Photometric Redshifts and Likelihood-Free Cosmological InferenceCode0
Efficient Out-of-Distribution Detection of Melanoma with Wavelet-based Normalizing FlowsCode0
Tractable Density Estimation on Learned Manifolds with Conformal Embedding FlowsCode0
Training Normalizing Flows from Dependent DataCode0
Unsupervised tree boosting for learning probability distributionsCode0
Estimating Feature-Label Dependence Using Gini Distance StatisticsCode0
Entropy-Informed Weighting Channel Normalizing FlowCode0
Enhancing Quantitative Image Synthesis through Pretraining and Resolution Scaling for Bone Mineral Density Estimation from a Plain X-ray ImageCode0
Estimating Probability Densities with Transformer and Denoising DiffusionCode0
Expected Information Maximization: Using the I-Projection for Mixture Density EstimationCode0
Fast Nonparametric Conditional Density EstimationCode0
Gaussian map predictions for 3D surface feature localisation and countingCode0
NeuroCodeBench: a plain C neural network benchmark for software verificationCode0
Warped Mixtures for Nonparametric Cluster ShapesCode0
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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