SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 701–725 of 3304 papers

TitleStatusHype
Computing Approximate _p Sensitivities—0
Visualizing DNA reaction trajectories with deep graph embedding approachesCode0
ViDa: Visualizing DNA hybridization trajectories with biophysics-informed deep graph embeddingsCode0
Practical considerations for variable screening in the super learnerCode0
TailorMe: Self-Supervised Learning of an Anatomically Constrained Volumetric Human Shape Model—0
3-Dimensional residual neural architecture search for ultrasonic defect detection—0
Spectral Clustering of Attributed Multi-relational GraphsCode1
Language Model Training Paradigms for Clinical Feature EmbeddingsCode0
Learning Collective Behaviors from Observation—0
Deep Compressed Learning for 3D Seismic Inversion—0
Modular Boundaries in Recurrent Neural NetworksCode2
Modified Genetic Algorithm for Feature Selection and Hyper Parameter Optimization: Case of XGBoost in Spam PredictionCode0
Gauge-optimal approximate learning for small data classification problems—0
Neural Stress Fields for Reduced-order Elastoplasticity and Fracture—0
Low-Dimensional Gradient Helps Out-of-Distribution Detection—0
Instance-wise Linearization of Neural Network for Model Interpretation—0
Nonlinear dimensionality reduction then and now: AIMs for dissipative PDEs in the ML era—0
K-Nearest-Neighbors Induced Topological PCA for scRNA Sequence Data Analysis—0
Noise-robust latent vector reconstruction in ptychography using deep generative models—0
Layered Models can "Automatically" Regularize and Discover Low-Dimensional Structures via Feature LearningCode0
Efficiently Visualizing Large GraphsCode0
Mori-Zwanzig latent space Koopman closure for nonlinear autoencoder—0
When Collaborative Filtering is not Collaborative: Unfairness of PCA for Recommendations—0
Electrical Grid Anomaly Detection via Tensor Decomposition—0
Accelerating Vision Transformers Based on Heterogeneous Attention Patterns—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