SOTAVerified

Dimensionality Reduction

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

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Papers

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Showing 1721–1730 of 3304 papers

TitleStatusHype
ESPACE: Dimensionality Reduction of Activations for Model Compression—0
Estimates on the domain of validity for Lyapunov-Schmidt reduction—0
Estimating a Manifold from a Tangent Bundle Learner—0
Estimating Conditional Average Treatment Effects via Sufficient Representation Learning—0
Estimating covariance and precision matrices along subspaces—0
Estimating Model Uncertainty of Neural Networks in Sparse Information Form—0
Estimating Model Uncertainty of Neural Network in Sparse Information Form—0
Estimation of Cross-Sectional Dependence in Large Panels—0
Estimation of Dimensions Contributing to Detected Anomalies with Variational Autoencoders—0
Evaluating deep variational autoencoders trained on pan-cancer gene expression—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