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 1171–1180 of 3304 papers

TitleStatusHype
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
Evaluating Explanatory Capabilities of Machine Learning Models in Medical Diagnostics: A Human-in-the-Loop Approach—0
Evaluating Feature Extraction Methods for Knowledge-based Biomedical Word Sense Disambiguation—0
Evaluating Graph Signal Processing for Neuroimaging Through Classification and Dimensionality Reduction—0
CitiusNLP at SemEval-2018 Task 10: The Use of Transparent Distributional Models and Salient Contexts to Discriminate Word Attributes—0
Adversarial Vulnerability as a Consequence of On-Manifold Inseparibility—0
Efficient Nearest Neighbor based Uncertainty Estimation for Natural Language Processing Tasks—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