SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

Showing 201225 of 3304 papers

TitleStatusHype
Quick and Robust Feature Selection: the Strength of Energy-efficient Sparse Training for AutoencodersCode1
Recursive KL Divergence Optimization: A Dynamic Framework for Representation LearningCode1
A local approach to parameter space reduction for regression and classification tasksCode1
Correlation-based feature selection to identify functional dynamics in proteinsCode1
Distributional Principal AutoencodersCode1
Rethinking Spatial Dimensions of Vision TransformersCode1
R-PointHop: A Green, Accurate, and Unsupervised Point Cloud Registration MethodCode1
Scalable semi-supervised dimensionality reduction with GPU-accelerated EmbedSOMCode1
Effective Sample Size, Dimensionality, and Generalization in Covariate Shift AdaptationCode1
SELFormer: Molecular Representation Learning via SELFIES Language ModelsCode1
Curvature-based Feature Selection with Application in Classifying Electronic Health RecordsCode1
DartMinHash: Fast Sketching for Weighted SetsCode1
DataLens: Scalable Privacy Preserving Training via Gradient Compression and AggregationCode1
SLISEMAP: Supervised dimensionality reduction through local explanationsCode1
Deep active subspaces - a scalable method for high-dimensional uncertainty propagationCode1
A Memory Efficient Baseline for Open Domain Question AnsweringCode1
Deep Convolutional Autoencoders for reconstructing magnetic resonance images of the healthy brainCode1
Stochastic Optimal Control for Collective Variable Free Sampling of Molecular Transition PathsCode1
Supervised Domain Adaptation using Graph EmbeddingCode1
Symplectic Autoencoders for Model Reduction of Hamiltonian SystemsCode1
Deep Domain Adaptation: A Sim2Real Neural Approach for Improving Eye-Tracking SystemsCode1
Deep Dimension Reduction for Supervised Representation LearningCode1
Deep Learning for Functional Data Analysis with Adaptive Basis LayersCode1
Deep Learning of Individual AestheticsCode1
EVNet: An Explainable Deep Network for Dimension ReductionCode1
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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