SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 731–740 of 3304 papers

TitleStatusHype
Risk factor aggregation and stress testing—0
Demystifying Embedding Spaces using Large Language Models—0
Deep learning modelling of manufacturing and build variations on multi-stage axial compressors aerodynamics—0
Simultaneous Dimensionality Reduction: A Data Efficient Approach for Multimodal Representations Learning—0
History Matching for Geological Carbon Storage using Data-Space Inversion with Spatio-Temporal Data Parameterization—0
Interpolating between Clustering and Dimensionality Reduction with Gromov-Wasserstein—0
Deep Variational Multivariate Information Bottleneck -- A Framework for Variational Losses—0
DQ-LoRe: Dual Queries with Low Rank Approximation Re-ranking for In-Context LearningCode1
Randomized Dimension Reduction with Statistical Guarantees—0
Subsurface Characterization using Ensemble-based Approaches with Deep Generative ModelsCode0
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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