SOTAVerified

Dimensionality Reduction

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

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Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1891–1900 of 3304 papers

TitleStatusHype
Real-time Inflation Forecasting Using Non-linear Dimension Reduction Techniques—0
Real-time optimal control of high-dimensional parametrized systems by deep learning-based reduced order models—0
Real-time simulation of parameter-dependent fluid flows through deep learning-based reduced order models—0
Realtime Simulation of Thin-Shell Deformable Materials using CNN-Based Mesh Embedding—0
Real-time Wireless Transmitter Authorization: Adapting to Dynamic Authorized Sets with Information Retrieval—0
Reconstructing Big Semantic Similarity Networks—0
Recovery of Linear Components: Reduced Complexity Autoencoder Designs—0
Recurrent Neural Networks for Long and Short-Term Sequential Recommendation—0
Recurrent neural networks learn robust representations by dynamically balancing compression and expansion—0
Reduced Basis Decomposition: a Certified and Fast Lossy Data Compression Algorithm—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