SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 76–100 of 3304 papers

TitleStatusHype
Effective Sample Size, Dimensionality, and Generalization in Covariate Shift AdaptationCode1
DartMinHash: Fast Sketching for Weighted SetsCode1
DataLens: Scalable Privacy Preserving Training via Gradient Compression and AggregationCode1
BIKED: A Dataset for Computational Bicycle Design with Machine Learning BenchmarksCode1
Deep Learning for Reduced Order Modelling and Efficient Temporal Evolution of Fluid SimulationsCode1
Deep Learning of Individual AestheticsCode1
DeepView: Visualizing Classification Boundaries of Deep Neural Networks as Scatter Plots Using Discriminative Dimensionality ReductionCode1
Detection and Retrieval of Out-of-Distribution Objects in Semantic SegmentationCode1
Diagnosing and Fixing Manifold Overfitting in Deep Generative ModelsCode1
A local approach to parameter space reduction for regression and classification tasksCode1
Dimensionality Reduction of Longitudinal 'Omics Data using Modern Tensor FactorizationCode1
Binary Bleed: Fast Distributed and Parallel Method for Automatic Model SelectionCode1
Bayesian Optimization of Sampling Densities in MRICode1
Discovering Distinctive "Semantics" in Super-Resolution NetworksCode1
ActUp: Analyzing and Consolidating tSNE and UMAPCode1
An Additive Autoencoder for Dimension EstimationCode1
Distributed Out-of-Memory NMF on CPU/GPU ArchitecturesCode1
DMCNet: Diversified Model Combination Network for Understanding Engagement from Video ScreengrabsCode1
DMT-HI: MOE-based Hyperbolic Interpretable Deep Manifold Transformation for Unspervised Dimensionality ReductionCode1
Application of Clustering Algorithms for Dimensionality Reduction in Infrastructure Resilience Prediction ModelsCode1
An Embedding is Worth a Thousand Noisy LabelsCode1
A New Basis for Sparse Principal Component AnalysisCode1
Enhanced MRI brain tumor detection and classification via topological data analysis and low-rank tensor decompositionCode1
Towards a More Rigorous Science of Blindspot Discovery in Image Classification ModelsCode1
BasisVAE: Translation-invariant feature-level clustering with Variational AutoencodersCode1
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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