SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

Showing 126150 of 3304 papers

TitleStatusHype
Deep Learning for Functional Data Analysis with Adaptive Basis LayersCode1
PyKale: Knowledge-Aware Machine Learning from Multiple Sources in PythonCode1
Disentangling Identifiable Features from Noisy Data with Structured Nonlinear ICACode1
ATD: Augmenting CP Tensor Decomposition by Self SupervisionCode1
Improving Metric Dimensionality Reduction with Distributed TopologyCode1
HUMAP: Hierarchical Uniform Manifold Approximation and ProjectionCode1
Unsupervised Behaviour Discovery with Quality-Diversity OptimisationCode1
Large-scale optimal transport map estimation using projection pursuitCode1
Scalable conditional deep inverse Rosenblatt transports using tensor-trains and gradient-based dimension reductionCode1
HoroPCA: Hyperbolic Dimensionality Reduction via Horospherical ProjectionsCode1
SKFAC:Training Neural Networks with Faster Kronecker-Factored Approximate CurvatureCode1
WiCluster: Passive Indoor 2D/3D Positioning using WiFi without Precise LabelsCode1
Estimating leverage scores via rank revealing methods and randomizationCode1
A hyperparameter-tuning approach to automated inverse planningCode1
A tutorial on generalized eigendecomposition for denoising, contrast enhancement, and dimension reduction in multichannel electrophysiologyCode1
Equivariant Wavelets: Fast Rotation and Translation Invariant Wavelet Scattering TransformsCode1
ProsoBeast Prosody Annotation ToolCode1
VERB: Visualizing and Interpreting Bias Mitigation Techniques for Word RepresentationsCode1
Generative Locally Linear EmbeddingCode1
Rethinking Spatial Dimensions of Vision TransformersCode1
Drop-Bottleneck: Learning Discrete Compressed Representation for Noise-Robust ExplorationCode1
Low Dimensional Landscape Hypothesis is True: DNNs can be Trained in Tiny SubspacesCode1
DataLens: Scalable Privacy Preserving Training via Gradient Compression and AggregationCode1
Revisiting Dynamic Convolution via Matrix DecompositionCode1
R-PointHop: A Green, Accurate, and Unsupervised Point Cloud Registration MethodCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1UDRNClassification Accuracy90.9Unverified
2tSNEClassification Accuracy51.5Unverified
3IVISClassification Accuracy46.6Unverified
4UMAPClassification Accuracy41.3Unverified
#ModelMetricClaimedVerifiedStatus
1UDRNClassification Accuracy71.1Unverified
2QSClassification Accuracy68Unverified