SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

Showing 91100 of 3304 papers

TitleStatusHype
An Additive Autoencoder for Dimension EstimationCode1
Distributed Out-of-Memory NMF on CPU/GPU ArchitecturesCode1
DMCNet: Diversified Model Combination Network for Understanding Engagement from Video ScreengrabsCode1
DMT-HI: MOE-based Hyperbolic Interpretable Deep Manifold Transformation for Unspervised Dimensionality ReductionCode1
Application of Clustering Algorithms for Dimensionality Reduction in Infrastructure Resilience Prediction ModelsCode1
An Embedding is Worth a Thousand Noisy LabelsCode1
A New Basis for Sparse Principal Component AnalysisCode1
Enhanced MRI brain tumor detection and classification via topological data analysis and low-rank tensor decompositionCode1
Towards a More Rigorous Science of Blindspot Discovery in Image Classification ModelsCode1
BasisVAE: Translation-invariant feature-level clustering with Variational AutoencodersCode1
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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