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 301–350 of 1394 papers

TitleStatusHype
Resilient Multiple Choice Learning: A learned scoring scheme with application to audio scene analysisCode1
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
MixerFlow: MLP-Mixer meets Normalising Flows—0
Free-form Flows: Make Any Architecture a Normalizing FlowCode1
A General Theory for Softmax Gating Multinomial Logistic Mixture of Experts—0
Diffusion-Model-Assisted Supervised Learning of Generative Models for Density Estimation—0
MFCC-GAN Codec: A New AI-based Audio Coding—0
Conformal Drug Property Prediction with Density Estimation under Covariate Shift—0
From Identifiable Causal Representations to Controllable Counterfactual Generation: A Survey on Causal Generative Modeling—0
Probabilistic Classification by Density Estimation Using Gaussian Mixture Model and Masked Autoregressive FlowCode0
ARTree: A Deep Autoregressive Model for Phylogenetic InferenceCode0
Investigating the Adversarial Robustness of Density Estimation Using the Probability Flow ODE—0
AnoDODE: Anomaly Detection with Diffusion ODE—0
Of heading, posture and body rotations derived from data acquired by animal-borne accelerometers, magnetometers and gyrometers, kernel density estimation of the corresponding spherical distributions, and fine-scale movement reconstruction—0
Stable Training of Probabilistic Models Using the Leave-One-Out Maximum Log-Likelihood ObjectiveCode0
Learning A Disentangling Representation For PU Learning—0
Stochastic force inference via density estimation—0
Beyond the Benchmark: Detecting Diverse Anomalies in VideosCode0
Improved Variational Bayesian Phylogenetic Inference using MixturesCode0
Light Schrödinger BridgeCode1
It HAS to be Subjective: Human Annotator Simulation via Zero-shot Density EstimationCode0
Density Estimation via Measure Transport: Outlook for Applications in the Biological Sciences—0
Statistical Perspective of Top-K Sparse Softmax Gating Mixture of Experts—0
Bounded rationality in structured density estimation—0
Bounded rationality in structured density estimation—0
Latent Space Energy-based Model for Fine-grained Open Set Recognition—0
Crowdotic: A Privacy-Preserving Hospital Waiting Room Crowd Density Estimation with Non-speech Audio—0
Content Reduction, Surprisal and Information Density Estimation for Long Documents—0
Correcting sampling biases via importance reweighting for spatial modeling—0
NeuroCodeBench: a plain C neural network benchmark for software verificationCode0
Tropical Geometric Tools for Machine Learning: the TML packageCode0
Distribution learning via neural differential equations: a nonparametric statistical perspective—0
Affine-Transformation-Invariant Image Classification by Differentiable Arithmetic Distribution Module—0
Self-Supervision for Tackling Unsupervised Anomaly Detection: Pitfalls and Opportunities—0
Arbitrary Distributions Mapping via SyMOT-Flow: A Flow-based Approach Integrating Maximum Mean Discrepancy and Optimal Transport—0
Out-of-distribution detection using normalizing flows on the data manifold—0
Bayesian Exploration Networks—0
Towards Automated Animal Density Estimation with Acoustic Spatial Capture-Recapture—0
Conditional Kernel Imitation Learning for Continuous State Environments—0
Cell Spatial Analysis in Crohn's Disease: Unveiling Local Cell Arrangement Pattern with Graph-based SignaturesCode0
Fast Inference and Update of Probabilistic Density Estimation on Trajectory PredictionCode1
High-Probability Risk Bounds via Sequential Predictors—0
Learning Distributions via Monte-Carlo Marginalization—0
Generative Forests—0
Statistical Estimation Under Distribution Shift: Wasserstein Perturbations and Minimax TheoryCode0
Adaptive learning of density ratios in RKHS—0
Continual Learning in Predictive Autoscaling—0
MVMR-FS : Non-parametric feature selection algorithm based on Maximum inter-class Variation and Minimum Redundancy—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