SOTAVerified

Dimensionality Reduction

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

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Papers

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Showing 2671–2680 of 3304 papers

TitleStatusHype
Extracting low-dimensional dynamics from multiple large-scale neural population recordings by learning to predict correlations—0
From which world is your graph?—0
The neighborhood lattice for encoding partial correlations in a Hilbert spaceCode0
Approximation of Functions over Manifolds: A Moving Least-Squares Approach—0
Sampling and multilevel coarsening algorithms for fast matrix approximations—0
Learning Neural Representations of Human Cognition across Many fMRI StudiesCode0
Stochastic variance reduced multiplicative update for nonnegative matrix factorization—0
Time-lagged autoencoders: Deep learning of slow collective variables for molecular kinetics—0
Dimensionality reduction methods for molecular simulations—0
Automatic Differentiation in PyTorchCode0
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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