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 501–550 of 1394 papers

TitleStatusHype
Neural PCA for Flow-Based Representation Learning—0
Efficient Out-of-Distribution Detection of Melanoma with Wavelet-based Normalizing FlowsCode0
Dynamic Maintenance of Kernel Density Estimation Data Structure: From Practice to Theory—0
Detecting Multivariate Time Series Anomalies with Zero Known LabelCode1
AdaCat: Adaptive Categorical Discretization for Autoregressive ModelsCode1
Fast Kernel Density Estimation with Density Matrices and Random Fourier FeaturesCode0
Learning Transfer Operators by Kernel Density EstimationCode0
Quantum Adaptive Fourier Features for Neural Density EstimationCode0
Implementing a Hierarchical Deep Learning Approach for Simulating Multi-Level Auction DataCode0
On minimax density estimation via measure transport—0
Robust Simulation-Based Inference in Cosmology with Bayesian Neural NetworksCode0
Neural Posterior Estimation with Differentiable Simulators—0
Accelerated Deep Lossless Image Coding with Unified Paralleleized GPU Coding Architecture—0
BMD-GAN: Bone mineral density estimation using x-ray image decomposition into projections of bone-segmented quantitative computed tomography using hierarchical learningCode0
Riemannian Diffusion Schrödinger Bridge—0
Probability density estimation for sets of large graphs with respect to spectral information using stochastic block modelsCode2
Representation Learning with Information Theory for COVID-19 Detection—0
A New Index for Clustering Evaluation Based on Density EstimationCode0
Score Matching for Truncated Density Estimation on a ManifoldCode0
I-SEA: Importance Sampling and Expected Alignment-Based Deep Distance Metric Learning for Time Series Analysis and EmbeddingCode0
Generative Anomaly Detection for Time Series Datasets—0
Split Localized Conformal PredictionCode0
LED: Latent Variable-based Estimation of Density—0
UNeRF: Time and Memory Conscious U-Shaped Network for Training Neural Radiance Fields—0
Neural Inverse Transform SamplerCode0
Neural Implicit Manifold Learning for Topology-Aware Density EstimationCode0
Meta Reinforcement Learning with Finite Training Tasks -- a Density Estimation ApproachCode0
Lyapunov Density Models: Constraining Distribution Shift in Learning-Based Control—0
C^*-algebra Net: A New Approach Generalizing Neural Network Parameters to C^*-algebra—0
Meta-learning for Out-of-Distribution Detection via Density Estimation in Latent Space—0
Spherical Sliced-WassersteinCode1
Pythae: Unifying Generative Autoencoders in Python -- A Benchmarking Use Case—0
Quasi-Bayesian Nonparametric Density Estimation via Autoregressive Predictive Updates—0
Discovery and density estimation of latent confounders in Bayesian networks with evidence lower bound—0
MammoFL: Mammographic Breast Density Estimation using Federated Learning—0
Log-concave density estimation in undirected graphical modelsCode0
Tropical Density Estimation of Phylogenetic Trees—0
Sequential Density Estimation via Nonlinear Continuous Weighted Finite Automata—0
Joint Manifold Learning and Density Estimation Using Normalizing Flows—0
AUTM Flow: Atomic Unrestricted Time Machine for Monotonic Normalizing Flows—0
MCD: Marginal Contrastive Discrimination for conditional density estimation—0
Noise2NoiseFlow: Realistic Camera Noise Modeling without Clean Images—0
An optimal transport approach for selecting a representative subsample with application in efficient kernel density estimation—0
k-Means Maximum Entropy Exploration—0
An Effective and Efficient Evolutionary Algorithm for Many-Objective Optimization—0
Flowification: Everything is a Normalizing FlowCode0
Backpropagation through Combinatorial Algorithms: Identity with Projection WorksCode1
Towards Instance-Wise Calibration: Local Amortized Diagnostics and Reshaping of Conditional Densities (LADaR)Code0
Exemplar Free Class Agnostic Counting—0
Multivariate Probabilistic Forecasting of Intraday Electricity Prices using Normalizing Flows—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