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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 151–200 of 1394 papers

TitleStatusHype
Bayesian Binary Search—0
Positional Encoder Graph Quantile Neural Networks for Geographic DataCode2
AgRegNet: A Deep Regression Network for Flower and Fruit Density Estimation, Localization, and Counting in Orchards—0
Schrödinger bridge based deep conditional generative learning—0
Improving satellite imagery segmentation using multiple Sentinel-2 revisitsCode0
From optimal score matching to optimal sampling—0
Toward Model-Agnostic Detection of New Physics Using Data-Driven Signal Regions—0
Average Causal Effect Estimation in DAGs with Hidden Variables: Extensions of Back-Door and Front-Door CriteriaCode0
Validation of musculoskeletal segmentation model with uncertainty estimation for bone and muscle assessment in hip-to-knee clinical CT images—0
Differentially Private Kernel Density Estimation—0
Hierarchical Visual Categories Modeling: A Joint Representation Learning and Density Estimation Framework for Out-of-Distribution Detection—0
Quasi-Bayesian sequential deconvolution—0
Prior Learning in Introspective VAEs—0
TANGO: Clustering with Typicality-Aware Nonlocal Mode-Seeking and Graph-Cut Optimization—0
Markov Balance Satisfaction Improves Performance in Strictly Batch Offline Imitation Learning—0
Latent Anomaly Detection Through Density Matrices—0
Kernel Density Estimators in Large Dimensions—0
Highly Efficient Self-Adaptive Reward Shaping for Reinforcement Learning—0
Estimating Pore Location of PBF-LB/M Processes with Segmentation Models—0
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
Learning Distances from Data with Normalizing Flows and Score Matching—0
Density Estimation via Binless Multidimensional IntegrationCode2
Unmasking Trees for Tabular DataCode1
Entropy-Informed Weighting Channel Normalizing FlowCode0
Agnostic Private Density Estimation for GMMs via List Global Stability—0
SPINEX: Similarity-based Predictions with Explainable Neighbors Exploration for Anomaly and Outlier Detection—0
FlowCon: Out-of-Distribution Detection using Flow-Based Contrastive LearningCode0
Uniform Transformation: Refining Latent Representation in Variational AutoencodersCode0
Instance-Optimal Private Density Estimation in the Wasserstein Distance—0
Evaluating and Benchmarking Foundation Models for Earth Observation and Geospatial AI—0
Flexible Tails for Normalizing FlowsCode0
High-probability minimax lower bounds—0
Quasi-Bayes meets Vines—0
Diffusion Generative Modelling for Divide-and-Conquer MCMCCode0
Injective flows for star-like manifolds—0
MEMO-QCD: Quantum Density Estimation through Memetic Optimisation for Quantum Circuit DesignCode0
Efficient Mixture Learning in Black-Box Variational InferenceCode0
Faster Spectral Density Estimation and Sparsification in the Nuclear Norm—0
Decoupled Marked Temporal Point Process using Neural Ordinary Differential Equations—0
Neural-g: A Deep Learning Framework for Mixing Density EstimationCode0
Perturbation Towards Easy Samples Improves Targeted Adversarial TransferabilityCode1
Auditing Differential Privacy Guarantees Using Density Estimation—0
Winner-takes-all learners are geometry-aware conditional density estimatorsCode0
Spectral Toolkit of Algorithms for Graphs: Technical Report (2)—0
Mitigating Bias in Dataset DistillationCode0
ContextFlow++: Generalist-Specialist Flow-based Generative Models with Mixed-Variable Context EncodingCode0
Estimation of tail risk measures in finance: Approaches to extreme value mixture modeling—0
Masked Language Modeling Becomes Conditional Density Estimation for Tabular Data Synthesis—0
Anomaly Detection Using Normalizing Flow-Based Density Estimation and Synthetic Defect ClassificationCode0
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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