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 1971–1980 of 3304 papers

TitleStatusHype
Regularized Pooling—0
Stochastic Bottleneck: Rateless Auto-Encoder for Flexible Dimensionality Reduction—0
Probing Criticality in Quantum Spin Chains with Neural Networks—0
Renormalized Mutual Information for Artificial Scientific DiscoveryCode1
On time series representations for multi-label NILMCode1
Sign Bits Are All You Need for Black-Box AttacksCode1
BayesOpt Adversarial AttackCode1
The Information Bottleneck Problem and Its Applications in Machine Learning—0
Data-Space Inversion Using a Recurrent Autoencoder for Time-Series Parameterization—0
Tired of Topic Models? Clusters of Pretrained Word Embeddings Make for Fast and Good Topics too!Code1
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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