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 1751–1760 of 3304 papers

TitleStatusHype
Explainable AI for Multivariate Time Series Pattern Exploration: Latent Space Visual Analytics with Temporal Fusion Transformer and Variational Autoencoders in Power Grid Event Diagnosis—0
Explainable Light-Weight Deep Learning Pipeline for Improved Drought Stress Identification—0
Explainable, Stable, and Scalable Graph Convolutional Networks for Learning Graph Representation—0
Explaining Genetic Programming Trees using Large Language Models—0
Exploiting Capacity of Sewer System Using Unsupervised Learning Algorithms Combined with Dimensionality Reduction—0
Exploiting Vulnerability of Pooling in Convolutional Neural Networks by Strict Layer-Output Manipulation for Adversarial Attacks—0
Exploiting Wireless Channel State Information Structures Beyond Linear Correlations: A Deep Learning Approach—0
Explore intrinsic geometry of sleep dynamics and predict sleep stage by unsupervised learning techniques—0
Exploring Dimensionality Reduction Techniques in Multilingual Transformers—0
Exploring Narrative Clustering in Large Language Models: A Layerwise Analysis of BERT—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