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 1001–1050 of 1394 papers

TitleStatusHype
Laplacian Mixture Model Point Based Registration—0
Latent Anomaly Detection Through Density Matrices—0
Latent Space Energy-based Model for Fine-grained Open Set Recognition—0
LCDnet: A Lightweight Crowd Density Estimation Model for Real-time Video Surveillance—0
Learnable Explicit Density for Continuous Latent Space and Variational Inference—0
Learning a Deep Part-based Representation by Preserving Data Distribution—0
Learning A Disentangling Representation For PU Learning—0
Learning and Generalization in Univariate Overparameterized Normalizing Flows—0
Learning Arbitrary Sum-Product Network Leaves with Expectation-Maximization—0
Learning Deep Generative Models With Discrete Latent Variables—0
Learning Deep Generative Models with Doubly Stochastic MCMC—0
Learning Deep Models from Synthetic Data for Extracting Dolphin Whistle Contours—0
Learning Distances from Data with Normalizing Flows and Score Matching—0
Learning Distributions over Logical Forms for Referring Expression Generation—0
Learning Distributions via Monte-Carlo Marginalization—0
Learning Energy-Based Models by Self-normalising the Likelihood—0
Learning k-Modal Distributions via Testing—0
Learning Mixtures of Linear Regressions in Subexponential Time via Fourier Moments—0
Learning Nonparametric Forest Graphical Models with Prior Information—0
Learning Short-Cut Connections for Object Counting—0
Learning the Solution Manifold in Optimization and Its Application in Motion Planning—0
Learning to Adapt to Domain Shifts with Few-shot Samples in Anomalous Sound Detection—0
Learning to Simulate Tree-Branch Dynamics for Manipulation—0
Learning Unnormalized Statistical Models via Compositional Optimization—0
Learning Unsupervised Word Mapping by Maximizing Mean Discrepancy—0
Learn to Scale: Generating Multipolar Normalized Density Maps for Crowd Counting—0
LED: Latent Variable-based Estimation of Density—0
Leveraging Heterogeneous Auxiliary Tasks to Assist Crowd Counting—0
LieDetect: Detection of representation orbits of compact Lie groups from point clouds—0
Lie PCA: Density estimation for symmetric manifolds—0
Likelihood-Free Adaptive Bayesian Inference via Nonparametric Distribution Matching—0
Likelihood-free inference of experimental Neutrino Oscillations using Neural Spline Flows—0
Linear-time Algorithms for Pairwise Statistical Problems—0
Liouville PDE-based sliced-Wasserstein flow for fair regression—0
LMD3: Language Model Data Density Dependence—0
Localised Generative Flows—0
LoMar: A Local Defense Against Poisoning Attack on Federated Learning—0
Lossless Compression using Continuously-Indexed Normalizing Flows—0
Lumbar Bone Mineral Density Estimation from Chest X-ray Images: Anatomy-aware Attentive Multi-ROI Modeling—0
Lyapunov Density Models: Constraining Distribution Shift in Learning-Based Control—0
Machine and Deep Learning for Crowd Analytics—0
MAE: Mutual Posterior-Divergence Regularization for Variational AutoEncoders—0
MammoFL: Mammographic Breast Density Estimation using Federated Learning—0
Manifold Density Estimation via Generalized Dequantization—0
Manifold Density Estimation via Generalized Dequantization—0
Manifold Learning Using Kernel Density Estimation and Local Principal Components Analysis—0
Manifold Optimization for Gaussian Mixture Models—0
Mapping oil palm density at country scale: An active learning approach—0
Markov Balance Satisfaction Improves Performance in Strictly Batch Offline Imitation Learning—0
Masked Language Modeling Becomes Conditional Density Estimation for Tabular Data Synthesis—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