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 901–925 of 3304 papers

TitleStatusHype
Deep Compressed Learning for 3D Seismic Inversion—0
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
Low-Dimensional Gradient Helps Out-of-Distribution Detection—0
Neural Stress Fields for Reduced-order Elastoplasticity and Fracture—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
Layered Models can "Automatically" Regularize and Discover Low-Dimensional Structures via Feature LearningCode0
Noise-robust latent vector reconstruction in ptychography using deep generative models—0
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
Multiview Transformer: Rethinking Spatial Information in Hyperspectral Image Classification—0
Accelerating Vision Transformers Based on Heterogeneous Attention Patterns—0
Jaeger: A Concatenation-Based Multi-Transformer VQA Model—0
Hybrid Quantum-Classical Machine Learning for Sentiment Analysis—0
Human-in-the-loop: The future of Machine Learning in Automated Electron Microscopy—0
Risk factor aggregation and stress testing—0
Demystifying Embedding Spaces using Large Language Models—0
A Process for Topic Modelling Via Word Embeddings—0
Deep learning modelling of manufacturing and build variations on multi-stage axial compressors aerodynamics—0
Simultaneous Dimensionality Reduction: A Data Efficient Approach for Multimodal Representations Learning—0
Interpolating between Clustering and Dimensionality Reduction with Gromov-Wasserstein—0
Show:102550
← PrevPage 37 of 133Next →
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