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 991–1000 of 3304 papers

TitleStatusHype
Dimensionality Reduction for Sentiment Classification: Evolving for the Most Prominent and Separable Features—0
Disease-oriented image embedding with pseudo-scanner standardization for content-based image retrieval on 3D brain MRI—0
Dimensionality Reduction for Representing the Knowledge of Probabilistic Models—0
Advancing the dimensionality reduction of speaker embeddings for speaker diarisation: disentangling noise and informing speech activity—0
Automated Anomaly Detection on European XFEL Klystrons—0
Disentangling Generative Factors of Physical Fields Using Variational Autoencoders—0
A Multimodal Deep Learning Approach for White Matter Shape Prediction in Diffusion MRI Tractography—0
Disentangling stellar atmospheric parameters in astronomical spectra using Generative Adversarial Neural Networks—0
A Deep Learning approach for parametrized and time dependent Partial Differential Equations using Dimensionality Reduction and Neural ODEs—0
A Class-Based Agreement Model for Generating Accurately Inflected Translations—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