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 1571–1580 of 3304 papers

TitleStatusHype
Quality-Diversity Meta-Evolution: customising behaviour spaces to a meta-objectiveCode0
Quantum-Classical Hybrid Machine Learning for Image Classification (ICCAD Special Session Paper)—0
Detection of Epileptic Seizures on EEG Signals Using ANFIS Classifier, Autoencoders and Fuzzy Entropies—0
Ligand-induced protein dynamics differences correlate with protein-ligand binding affinities: An unsupervised deep learning approach—0
An Empirical Study on the Joint Impact of Feature Selection and Data Re-sampling on Imbalance Classification—0
Data-Driven Reduced-Order Modeling of Spatiotemporal Chaos with Neural Ordinary Differential Equations—0
Bubblewrap: Online tiling and real-time flow prediction on neural manifoldsCode0
Variational voxelwise rs-fMRI representation learning: Evaluation of sex, age, and neuropsychiatric signatures—0
Variational embedding of protein folding simulations using gaussian mixture variational autoencoders—0
Convolutional Autoencoders for Reduced-Order Modeling—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