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 1901–1925 of 3304 papers

TitleStatusHype
Generative Modeling for Atmospheric Convection—0
Deep Neural Networks for Nonlinear Model Order Reduction of Unsteady Flows—0
A New Basis for Sparse Principal Component AnalysisCode1
Subspace approximation with outliers—0
Deep neural networks for the evaluation and design of photonic devices—0
VAE-KRnet and its applications to variational Bayes—0
Local Neighbor Propagation Embedding—0
Physics-aware registration based auto-encoder for convection dominated PDEsCode1
Invertible Concept-based Explanations for CNN Models with Non-negative Concept Activation VectorsCode1
The Signature Kernel is the solution of a Goursat PDECode1
Nearest Neighbour Based Estimates of Gradients: Sharp Nonasymptotic Bounds and Applications—0
Neural Decomposition: Functional ANOVA with Variational AutoencodersCode1
A metric on directed graphs and Markov chains based on hitting probabilitiesCode0
Extracting the main trend in a dataset: the Sequencer algorithmCode1
ABID: Angle Based Intrinsic Dimensionality—0
Approximation Algorithms for Sparse Principal Component Analysis—0
On Compression Principle and Bayesian Optimization for Neural Networks—0
Similarity Search with Tensor Core Units—0
Controlling for sparsity in sparse factor analysis models: adaptive latent feature sharing for piecewise linear dimensionality reduction—0
A Neural Network for Determination of Latent Dimensionality in Nonnegative Matrix Factorization—0
Supervised dimensionality reduction by a Linear Discriminant Analysis on pre-trained CNN featuresCode0
Performance Evaluation of t-SNE and MDS Dimensionality Reduction Techniques with KNN, ENN and SVM Classifiers—0
Weakly-correlated synapses promote dimension reduction in deep neural networks—0
Estimating Model Uncertainty of Neural Networks in Sparse Information Form—0
Training (Overparametrized) Neural Networks in Near-Linear Time—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