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 626–650 of 3304 papers

TitleStatusHype
Optimal high-precision shadow estimation—0
Word Embedding Dimension Reduction via Weakly-Supervised Feature SelectionCode0
ModalChorus: Visual Probing and Alignment of Multi-modal Embeddings via Modal Fusion MapCode0
TrIM: Transformed Iterative Mondrian Forests for Gradient-based Dimension Reduction and High-Dimensional RegressionCode0
Spectral Self-supervised Feature Selection—0
Unveiling the Potential of BERTopic for Multilingual Fake News Analysis -- Use Case: Covid-19—0
Physics-Informed Geometric Operators to Support Surrogate, Dimension Reduction and Generative Models for Engineering Design—0
Robust Partial Least Squares Using Low Rank and Sparse Decomposition—0
Automatic Prediction of the Performance of Every Parser—0
Single-Sequence-Based Protein Secondary Structure Prediction using One-Hot and Chemical Encodings of Amino Acids—0
Autoencoded Image Compression for Secure and Fast TransmissionCode0
Adversarial Robustness of VAEs across Intersectional SubgroupsCode0
Statistical Advantages of Oblique Randomized Decision Trees and Forests—0
Message-Relevant Dimension Reduction of Neural Populations—0
Latent Diffusion Model for Generating Ensembles of Climate Simulations—0
Credit Risk Assessment Model for UAE Commercial Banks: A Machine Learning Approach—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
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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