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Dimensionality Reduction

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

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Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 701–750 of 3304 papers

TitleStatusHype
Computing Approximate _p Sensitivities—0
Visualizing DNA reaction trajectories with deep graph embedding approachesCode0
Practical considerations for variable screening in the super learnerCode0
ViDa: Visualizing DNA hybridization trajectories with biophysics-informed deep graph embeddingsCode0
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
Jaeger: A Concatenation-Based Multi-Transformer VQA Model—0
Accelerating Vision Transformers Based on Heterogeneous Attention Patterns—0
Multiview Transformer: Rethinking Spatial Information in Hyperspectral Image Classification—0
Hybrid Quantum-Classical Machine Learning for Sentiment Analysis—0
Human-in-the-loop: The future of Machine Learning in Automated Electron Microscopy—0
A Process for Topic Modelling Via Word Embeddings—0
Demystifying Embedding Spaces using Large Language Models—0
Risk factor aggregation and stress testing—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
History Matching for Geological Carbon Storage using Data-Space Inversion with Spatio-Temporal Data Parameterization—0
Deep Variational Multivariate Information Bottleneck -- A Framework for Variational Losses—0
DQ-LoRe: Dual Queries with Low Rank Approximation Re-ranking for In-Context LearningCode1
Randomized Dimension Reduction with Statistical Guarantees—0
Subsurface Characterization using Ensemble-based Approaches with Deep Generative ModelsCode0
Latent Dynamic Networked System Identification with High-Dimensional Networked Data—0
Statistical physics, Bayesian inference and neural information processing—0
On the Power of SVD in the Stochastic Block Model—0
Cluster Exploration using Informative Manifold ProjectionsCode0
Only 5\% Attention Is All You Need: Efficient Long-range Document-level Neural Machine Translation—0
Matrix Factorization in Tropical and Mixed Tropical-Linear Algebras—0
Robust Principal Component Analysis using Density Power Divergence—0
Elastic deep autoencoder for text embedding clustering by an improved graph regularization—0
CA-PCA: Manifold Dimension Estimation, Adapted for Curvature—0
Analysing race and sex bias in brain age prediction—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