SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 761–770 of 3304 papers

TitleStatusHype
Deep Amortized Variational Inference for Multivariate Time Series Imputation with Latent Gaussian Process Models—0
Deep Autoencoders for Dimensionality Reduction of High-Content Screening Data—0
A Fully Convolutional Network for MR Fingerprinting—0
Deep Clustering using Dirichlet Process Gaussian Mixture and Alpha Jensen-Shannon Divergence Clustering Loss—0
Deep Compressed Learning for 3D Seismic Inversion—0
Adaptive Dimension Reduction and Variational Inference for Transductive Few-Shot Classification—0
Deep topic modeling by multilayer bootstrap network and lasso—0
Deep denoising autoencoder-based non-invasive blood flow detection for arteriovenous fistula—0
A review of unsupervised learning in astronomy—0
Click prediction boosting via Bayesian hyperparameter optimization based ensemble learning pipelines—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