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 1171–1180 of 3304 papers

TitleStatusHype
Extreme compression of sentence-transformer ranker models: faster inference, longer battery life, and less storage on edge devices—0
Feature Learning for Nonlinear Dimensionality Reduction toward Maximal Extraction of Hidden Patterns—0
Stochastic Optimal Control for Collective Variable Free Sampling of Molecular Transition PathsCode1
Estimation and Inference in High-Dimensional Panel Data Models with Interactive Fixed Effects—0
Self-Supervised Training with Autoencoders for Visual Anomaly Detection—0
Deep Reinforcement Learning-Assisted Federated Learning for Robust Short-term Utility Demand Forecasting in Electricity Wholesale Markets—0
Derivative-Informed Neural Operator: An Efficient Framework for High-Dimensional Parametric Derivative LearningCode1
Meta Reinforcement Learning with Finite Training Tasks -- a Density Estimation ApproachCode0
Analysis of Self-Supervised Learning and Dimensionality Reduction Methods in Clustering-Based Active Learning for Speech Emotion RecognitionCode0
Improving Triplet-Based Channel Charting on Distributed Massive MIMO Measurements—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