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 641–650 of 3304 papers

TitleStatusHype
Credit Risk Assessment Model for UAE Commercial Banks: A Machine Learning Approach—0
Efficient Nearest Neighbor based Uncertainty Estimation for Natural Language Processing Tasks—0
Fully invertible hyperbolic neural networks for segmenting large-scale surface and sub-surface data—0
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
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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