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Dimensionality Reduction

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

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Papers

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Showing 426–450 of 3304 papers

TitleStatusHype
Message-Relevant Dimension Reduction of Neural Populations—0
Statistical Advantages of Oblique Randomized Decision Trees and Forests—0
Latent Diffusion Model for Generating Ensembles of Climate Simulations—0
Efficient Nearest Neighbor based Uncertainty Estimation for Natural Language Processing Tasks—0
Fully invertible hyperbolic neural networks for segmenting large-scale surface and sub-surface data—0
Bayesian calibration of stochastic agent based model via random forestCode0
Specific language impairment (SLI) detection pipeline from transcriptions of spontaneous narratives—0
A review of unsupervised learning in astronomy—0
EvolvED: Evolutionary Embeddings to Understand the Generation Process of Diffusion Models—0
Learning When the Concept Shifts: Confounding, Invariance, and Dimension Reduction—0
Latent diffusion models for parameterization and data assimilation of facies-based geomodels—0
Coupled Input-Output Dimension Reduction: Application to Goal-oriented Bayesian Experimental Design and Global Sensitivity AnalysisCode0
Pattern or Artifact? Interactively Exploring Embedding Quality with TRACECode1
Sharp detection of low-dimensional structure in probability measures via dimensional logarithmic Sobolev inequalities—0
Sparsifying dimensionality reduction of PDE solution data with Bregman learning—0
Oblivious subspace embeddings for compressed Tucker decompositions—0
Research on Early Warning Model of Cardiovascular Disease Based on Computer Deep Learning—0
Interpetable Target-Feature Aggregation for Multi-Task Learning based on Bias-Variance AnalysisCode0
Macroscopic Market Making Games via Multidimensional Decoupling Field—0
From Analog to Digital: Multi-Order Digital Joint Coding-Modulation for Semantic CommunicationCode1
Unsupervised learning of Data-driven Facial Expression Coding System (DFECS) using keypoint tracking—0
VERA: Generating Visual Explanations of Two-Dimensional Embeddings via Region AnnotationCode0
Enhancing Supervised Visualization through Autoencoder and Random Forest Proximities for Out-of-Sample Extension—0
Spherinator and HiPSter: Representation Learning for Unbiased Knowledge Discovery from SimulationsCode0
Noisy Data Visualization using Functional Data Analysis—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