SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 431–440 of 3304 papers

TitleStatusHype
Bayesian calibration of stochastic agent based model via random forestCode0
Specific language impairment (SLI) detection pipeline from transcriptions of spontaneous narratives—0
A review of unsupervised learning in astronomy—0
EvolvED: Evolutionary Embeddings to Understand the Generation Process of Diffusion Models—0
Learning When the Concept Shifts: Confounding, Invariance, and Dimension Reduction—0
Latent diffusion models for parameterization and data assimilation of facies-based geomodels—0
Coupled Input-Output Dimension Reduction: Application to Goal-oriented Bayesian Experimental Design and Global Sensitivity AnalysisCode0
Pattern or Artifact? Interactively Exploring Embedding Quality with TRACECode1
Sharp detection of low-dimensional structure in probability measures via dimensional logarithmic Sobolev inequalities—0
Sparsifying dimensionality reduction of PDE solution data with Bregman learning—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