SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

Showing 12411250 of 3304 papers

TitleStatusHype
Exploring Dimensionality Reduction Techniques in Multilingual Transformers0
A dynamical systems based framework for dimension reduction0
Diagnosing and Fixing Manifold Overfitting in Deep Generative ModelsCode1
Wassmap: Wasserstein Isometric Mapping for Image Manifold LearningCode0
DMCNet: Diversified Model Combination Network for Understanding Engagement from Video ScreengrabsCode1
Assessment of convolutional recurrent autoencoder network for learning wave propagation0
T- Hop: Tensor representation of paths in graph convolutional networks0
RMFGP: Rotated Multi-fidelity Gaussian process with Dimension Reduction for High-dimensional Uncertainty Quantification0
Weight Matrix Dimensionality Reduction in Deep Learning via Kronecker Multi-layer ArchitecturesCode0
MultiAuto-DeepONet: A Multi-resolution Autoencoder DeepONet for Nonlinear Dimension Reduction, Uncertainty Quantification and Operator Learning of Forward and Inverse Stochastic Problems0
Show:102550
← PrevPage 125 of 331Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1UDRNClassification Accuracy90.9Unverified
2tSNEClassification Accuracy51.5Unverified
3IVISClassification Accuracy46.6Unverified
4UMAPClassification Accuracy41.3Unverified
#ModelMetricClaimedVerifiedStatus
1UDRNClassification Accuracy71.1Unverified
2QSClassification Accuracy68Unverified