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 476–500 of 3304 papers

TitleStatusHype
A Visual Interaction Framework for Dimensionality Reduction Based Data Exploration—0
A Wasserstein perspective of Vanilla GANs—0
A Wavelet Diffusion GAN for Image Super-Resolution—0
A witness function based construction of discriminative models using Hermite polynomials—0
An Algorithmic Information Calculus for Causal Discovery and Reprogramming Systems—0
Approximated and User Steerable tSNE for Progressive Visual Analytics—0
Active Learning with TensorBoard Projector—0
Analyse en Composante Principale pour l'extraction des i-vecteurs en v\'erification du locuteur (Principal Component Analysis for i-vector extraction in speaker verification.) [in French]—0
Bayesian Calibration for Activity Based Models—0
Analysing race and sex bias in brain age prediction—0
Bayesian Compressed Regression—0
Bayesian Data Sketching for Varying Coefficient Regression Models—0
Bayesian Exponential Family PCA—0
Bayesian full waveform inversion with sequential surrogate model refinement—0
Bayesian Inference on Matrix Manifolds for Linear Dimensionality Reduction—0
Bayesian Inverse Problems with Conditional Sinkhorn Generative Adversarial Networks in Least Volume Latent Spaces—0
Bayesian inverse regression for dimension reduction with small datasets—0
Analysis of Evolving Cortical Neuronal Networks Using Visual Informatics—0
Bayesian Learning of Dynamic Multilayer Networks—0
Approaching Metaheuristic Deep Learning Combos for Automated Data Mining—0
A Dimensionality Reduction Method for Finding Least Favorable Priors with a Focus on Bregman Divergence—0
Bayesian model and dimension reduction for uncertainty propagation: applications in random media—0
Bayesian neural networks and dimensionality reduction—0
ANALYTiC: Understanding Decision Boundaries and Dimensionality Reduction in Machine Learning—0
Applying Supervised Learning Algorithms and a New Feature Selection Method to Predict Coronary Artery Disease—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