SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 3026–3050 of 3304 papers

TitleStatusHype
Decentralized Equalization for Massive MIMO Systems With Colored Noise Samples—0
Decentralized Riemannian Algorithm for Nonconvex Minimax Problems—0
Decoding dynamic brain patterns from evoked responses: A tutorial on multivariate pattern analysis applied to time-series neuroimaging data—0
Decoding Emotional Experience through Physiological Signal Processing—0
Decoding Imagined Speech and Computer Control using Brain Waves—0
Decomposing and Coupling Saliency Map for Lesion Segmentation in Ultrasound Images—0
Deconfounding Scores: Feature Representations for Causal Effect Estimation with Weak Overlap—0
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
Deep Clustering using Dirichlet Process Gaussian Mixture and Alpha Jensen-Shannon Divergence Clustering Loss—0
Deep Compressed Learning for 3D Seismic Inversion—0
Deep denoising autoencoder-based non-invasive blood flow detection for arteriovenous fistula—0
Deep Distributed Random Samplings for Supervised Learning: An Alternative to Random Forests?—0
Deep Embedding Kernel—0
Deep Fair Learning: A Unified Framework for Fine-tuning Representations with Sufficient Networks—0
Deep Features for training Support Vector Machine—0
Deep Gaussian Mixture Models—0
Deep Generative Models Strike Back! Improving Understanding and Evaluation in Light of Unmet Expectations for OoD Data—0
Deep-gKnock: nonlinear group-feature selection with deep neural network—0
Deep learning and differential equations for modeling changes in individual-level latent dynamics between observation periods—0
Estimation of a function of low local dimensionality by deep neural networks—0
Deep learning approach based on dimensionality reduction for designing electromagnetic nanostructures—0
Deep Learning Architecture for Motor Imaged Words—0
Deep-learning based measurement of planetary radial velocities in the presence of stellar variability—0
Deep Learning for Causal Inference—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