SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 1071–1080 of 3304 papers

TitleStatusHype
Difficulty in estimating visual information from randomly sampled images—0
Differential Privacy Meets Neural Network Pruning—0
Differential Privacy for Clustering Under Continual Observation—0
Differentially Private Sliced Inverse Regression: Minimax Optimality and Algorithm—0
A Tutorial on Distance Metric Learning: Mathematical Foundations, Algorithms, Experimental Analysis, Prospects and Challenges (with Appendices on Mathematical Background and Detailed Algorithms Explanation)—0
A Meta-learning Formulation of the Autoencoder Problem for Non-linear Dimensionality Reduction—0
Additive Component Analysis—0
Differentially private sliced inverse regression in the federated paradigm—0
Attention or memory? Neurointerpretable agents in space and time—0
Diffeomorphic Dimensionality Reduction—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