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
High-dimensional additive Gaussian processes under monotonicity constraintsCode1
DendroMap: Visual Exploration of Large-Scale Image Datasets for Machine Learning with TreemapsCode1
DistilProtBert: A distilled protein language model used to distinguish between real proteins and their randomly shuffled counterpartsCode1
Dimension Reduction for Efficient Dense Retrieval via Conditional AutoencoderCode1
Application of Clustering Algorithms for Dimensionality Reduction in Infrastructure Resilience Prediction ModelsCode1
Uniform Manifold Approximation with Two-phase OptimizationCode1
Diagnosing and Fixing Manifold Overfitting in Deep Generative ModelsCode1
DMCNet: Diversified Model Combination Network for Understanding Engagement from Video ScreengrabsCode1
Neural Implicit Flow: a mesh-agnostic dimensionality reduction paradigm of spatio-temporal dataCode1
Correlation-based feature selection to identify functional dynamics in proteinsCode1
Show:102550
← PrevPage 10 of 331Next →

Benchmark Results

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