SOTAVerified

Dimensionality Reduction

Dimensionality reduction is the task of reducing the dimensionality of a dataset.

( Image credit: openTSNE )

Papers

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Showing 2391–2400 of 3304 papers

TitleStatusHype
Variable noise and dimensionality reduction for sparse Gaussian processes—0
Variational Autoencoders for Feature Detection of Magnetic Resonance Imaging Data—0
Variational autoencoders for tissue heterogeneity exploration from (almost) no preprocessed mass spectrometry imaging data—0
Variational Bayesian Optimal Experimental Design with Normalizing Flows—0
Variational Bayesian surrogate modelling with application to robust design optimisation—0
Variational embedding of protein folding simulations using gaussian mixture variational autoencoders—0
Variational Gaussian Process Dynamical Systems—0
Variational Inference for Uncertainty on the Inputs of Gaussian Process Models—0
Variational Koopman models: slow collective variables and molecular kinetics from short off-equilibrium simulations—0
Variational Learning of Gaussian Process Latent Variable Models through Stochastic Gradient Annealed Importance Sampling—0
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All datasetsMCAEMNIST

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1UDRNClassification Accuracy90.9—Unverified
2tSNEClassification Accuracy51.5—Unverified
3IVISClassification Accuracy46.6—Unverified
4UMAPClassification Accuracy41.3—Unverified
#ModelMetricClaimedVerifiedStatus
1UDRNClassification Accuracy71.1—Unverified
2QSClassification Accuracy68—Unverified