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 401–450 of 1394 papers

TitleStatusHype
Just Cluster It: An Approach for Exploration in High-Dimensions using Clustering and Pre-Trained RepresentationsCode0
Less is KEN: a Universal and Simple Non-Parametric Pruning Algorithm for Large Language ModelsCode0
Decomposing Global Bank Network Connectedness: What is Common, Idiosyncratic and When?—0
Deep conditional distribution learning via conditional Föllmer flowCode0
Improving importance estimation in covariate shift for providing accurate prediction error—0
Vehicle Perception from SatelliteCode0
Multivariate Density Estimation via Variance-Reduced SketchingCode0
Document Set Expansion with Positive-Unlabeled Learning: A Density Estimation-based Approach—0
Robust Multi-Modal Density Estimation—0
Learning from Sparse Offline Datasets via Conservative Density EstimationCode0
Curriculum for Crowd Counting -- Is it Worthy?—0
A Good Score Does not Lead to A Good Generative ModelCode0
Federated Learning for distribution skewed data using sample weights—0
Transformer Neural Autoregressive Flows—0
CrowdDiff: Multi-hypothesis Crowd Density Estimation using Diffusion Models—0
Kernel Density Estimation for Multiclass Quantification—0
Count What You Want: Exemplar Identification and Few-shot Counting of Human Actions in the WildCode0
Adversarial Attacks on LoRa Device Identification and Rogue Signal Detection with Deep Learning—0
Mean-field underdamped Langevin dynamics and its spacetime discretizationCode0
Compressing Image-to-Image Translation GANs Using Local Density Structures on Their Learned Manifold—0
InvertibleNetworks.jl: A Julia package for scalable normalizing flows—0
Density Descent for Diversity Optimization—0
Stochastic Bayesian Optimization with Unknown Continuous Context Distribution via Kernel Density EstimationCode0
Unbiasing Enhanced Sampling on a High-dimensional Free Energy Surface with Deep Generative Model—0
ρ-Diffusion: A diffusion-based density estimation framework for computational physicsCode0
TERM Model: Tensor Ring Mixture Model for Density Estimation—0
Variational Auto-Encoder Based Deep Learning Technique For Filling Gaps in Reacting PIV Data—0
Beyond Classification: Definition and Density-based Estimation of Calibration in Object DetectionCode0
Concurrent Density Estimation with Wasserstein Autoencoders: Some Statistical Insights—0
On Tuning Neural ODE for Stability, Consistency and Faster Convergence—0
Robust Non-parametric Knowledge-based Diffusion Least Mean Squares over Adaptive Networks—0
Distributed Bayesian Estimation in Sensor Networks: Consensus on Marginal Densities—0
Bagged Regularized k-Distances for Anomaly Detection—0
Maximum Entropy Model Correction in Reinforcement Learning—0
InceptionHuman: Controllable Prompt-to-NeRF for Photorealistic 3D Human GenerationCode0
A density estimation perspective on learning from pairwise human preferencesCode0
Density Distribution-based Learning Framework for Addressing Online Continual Learning ChallengesCode0
Research on the Dynamic Evolution and Influencing Factors of Energy Resilience in China—0
A Video-Based Activity Classification of Human Pickers in Agriculture—0
Surprisal Driven k-NN for Robust and Interpretable Nonparametric Learning—0
High Dimensional Binary Choice Model with Unknown Heteroskedasticity or Instrumental Variables—0
Topology-Matching Normalizing Flows for Out-of-Distribution Detection in Robot Learning—0
Reframing Audience Expansion through the Lens of Probability Density EstimationCode0
Variational Weighting for Kernel Density RatiosCode0
Sample Complexity Bounds for Estimating Probability Divergences under Invariances—0
Structured Neural Networks for Density Estimation and Causal Inference—0
Bayesian Quantile Regression with Subset Selection: A Decision Analysis Perspective—0
Add and Thin: Diffusion for Temporal Point Processes—0
Density Estimation for Entry Guidance Problems using Deep Learning—0
Efficient Neural Network Approaches for Conditional Optimal Transport with Applications in Bayesian InferenceCode0
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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
7Flow matchingNLL (bits/dim)2.99—Unverified
8score SDENLL (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