SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 741–750 of 3304 papers

TitleStatusHype
Data-Driven Reduced-Order Modeling of Spatiotemporal Chaos with Neural Ordinary Differential Equations—0
Data-driven Uncertainty Quantification in Computational Human Head Models—0
Data efficiency, dimensionality reduction, and the generalized symmetric information bottleneck—0
Data-efficient Meta-models for Evaluation of Context-based Questions and Answers in LLMs—0
Data-Enabled Predictive Control for Flexible Spacecraft—0
Data-independent Low-complexity KLT Approximations for Image and Video Coding—0
Deep Learning Multidimensional Projections—0
A quantitative fusion strategy of stock picking and timing based on Particle Swarm Optimized-Back Propagation Neural Network and Multivariate Gaussian-Hidden Markov Model—0
Data-Space Inversion Using a Recurrent Autoencoder for Time-Series Parameterization—0
An information-geometric approach to feature extraction and moment reconstruction in dynamical systems—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