SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

Showing 26812690 of 3304 papers

TitleStatusHype
Probing Latent Subspaces in LLM for AI Security: Identifying and Manipulating Adversarial States0
Process monitoring based on orthogonal locality preserving projection with maximum likelihood estimation0
Progressive Disentanglement Using Relevant Factor VAE0
Weight Matrix Dimensionality Reduction in Deep Learning via Kronecker Multi-layer ArchitecturesCode0
Conditional Latent Block Model: a Multivariate Time Series Clustering Approach for Autonomous Driving ValidationCode0
Dimensionality Reduction Meets Message Passing for Graph Node EmbeddingsCode0
Spectral Clustering via Orthogonalization-Free MethodsCode0
Meta-learning of Pooling Layers for Character RecognitionCode0
Meta Reinforcement Learning with Finite Training Tasks -- a Density Estimation ApproachCode0
How to Evaluate Dimensionality Reduction? - Improving the Co-ranking MatrixCode0
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Benchmark Results

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