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 751–800 of 1394 papers

TitleStatusHype
Differentially Private Normalizing Flows for Privacy-Preserving Density Estimation—0
Semi-Supervised Learning for Bone Mineral Density Estimation in Hip X-ray Images—0
Out-of-Distribution Detection of Melanoma using Normalizing Flows—0
Flow-based Self-supervised Density Estimation for Anomalous Sound Detection—0
Spatial Dependency Networks: Neural Layers for Improved Generative Image ModelingCode1
FloMo: Tractable Motion Prediction with Normalizing FlowsCode0
Lossless Compression using Continuously-Indexed Normalizing Flows—0
Learning to Shift Attention for Motion GenerationCode0
Probabilistic Generating CircuitsCode0
Faster Kernel Matrix Algebra via Density Estimation—0
Sliced Multi-Marginal Optimal Transport—0
Manifold Density Estimation via Generalized Dequantization—0
A Statistical Relational Approach to Learning Distance-based GCNs—0
Projected Wasserstein gradient descent for high-dimensional Bayesian inferenceCode0
Robust and integrative Bayesian neural networks for likelihood-free parameter inference—0
Learning with Density Matrices and Random FeaturesCode1
HGAN: Hybrid Generative Adversarial Network—0
Diversity-Robust Acoustic Feature Signatures Based on Multiscale Fractal Dimension for Similarity Search of Environmental Sounds—0
Invertible DenseNets with Concatenated LipSwishCode1
Predicting the probability distribution of bus travel time to move towards reliable planning of public transport services—0
Anomaly Detection of Time Series with Smoothness-Inducing Sequential Variational Auto-Encoder—0
Adaptive Random Bandwidth for Inference in CAViaR Models—0
Rates of convergence for density estimation with generative adversarial networks—0
Sequential prediction under log-loss and misspecification—0
Parameter and density estimation from real-world traffic data: A kinetic compartmental approach—0
Unsupervised tree boosting for learning probability distributionsCode0
Morphological Change Forecasting for Prostate Glands using Feature-based Registration and Kernel Density Extrapolation—0
Denoising Score Matching with Random Fourier Features—0
Distances with mixed type variables some modified Gower's coefficients—0
Cauchy-Schwarz Regularized Autoencoder—0
Scale-Aware Network with Regional and Semantic Attentions for Crowd Counting under Cluttered Background—0
Copula Flows for Synthetic Data Generation—0
Can Kernel Transfer Operators Help Flow based Generative Models?—0
Neural Point Process for Forecasting Spatiotemporal Events—0
EMTL: A Generative Domain Adaptation Approach—0
TraDE: A Simple Self-Attention-Based Density Estimator—0
ARMCMC: ONLINE MODEL PARAMETERS DENSITY ESTIMATION IN BAYESIAN PARADIGM—0
Parametric Density Estimation with Uncertainty using Deep Ensembles—0
Learning and Generalization in Univariate Overparameterized Normalizing Flows—0
Unpacking Information Bottlenecks: Surrogate Objectives for Deep Learning—0
Variational Determinant Estimation with Spherical Normalizing Flows—0
Unsupervised in-distribution anomaly detection of new physics through conditional density estimation—0
Process monitoring based on orthogonal locality preserving projection with maximum likelihood estimation—0
Convex Potential Flows: Universal Probability Distributions with Optimal Transport and Convex OptimizationCode1
Uncertainty Intervals for Graph-based Spatio-Temporal Traffic Prediction—0
Accelerating Continuous Normalizing Flow with Trajectory Polynomial RegularizationCode0
Interpretable deep learning regression for breast density estimation on MRI—0
Multivariate Density Estimation with Deep Neural Mixture Models—0
Graph Mixture Density NetworksCode1
The Hidden Uncertainty in a Neural Networks Activations—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