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 1951–1960 of 3304 papers

TitleStatusHype
Contrastive Multiple Correspondence Analysis (cMCA): Using Contrastive Learning to Identify Latent Subgroups in Political PartiesCode0
Attention or memory? Neurointerpretable agents in space and time—0
Linear Tensor Projection Revealing Nonlinearity—0
Manifold Learning via Manifold Deflation—0
Offline versus Online Triplet Mining based on Extreme Distances of Histopathology PatchesCode0
Generative Modeling for Atmospheric Convection—0
Deep Neural Networks for Nonlinear Model Order Reduction of Unsteady Flows—0
Deep neural networks for the evaluation and design of photonic devices—0
Subspace approximation with outliers—0
VAE-KRnet and its applications to variational Bayes—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