SOTAVerified

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 451–500 of 3304 papers

TitleStatusHype
Autoencoding topology—0
A Multi-Fidelity Methodology for Reduced Order Models with High-Dimensional Inputs—0
Autoencoding with a Learning Classifier System: Initial Results—0
A Hybrid Data-Driven Approach For Analyzing And Predicting Inpatient Length Of Stay In Health Centre—0
Automated Anomaly Detection on European XFEL Klystrons—0
Automated detection of Zika and dengue in Aedes aegypti using neural spiking analysis—0
Automated Disease Normalization with Low Rank Approximations—0
Automatic Collection Creation and Recommendation—0
A Deep Learning Approach with an Attention Mechanism for Automatic Sleep Stage Classification—0
Automatic dimensionality reduction of Twin-in-the-Loop Observers—0
Automatic Double Reinforcement Learning in Semiparametric Markov Decision Processes with Applications to Long-Term Causal Inference—0
Automatic Debiased Estimation with Machine Learning-Generated Regressors—0
Automatic Prediction of the Performance of Every Parser—0
Active Linear Regression for _p Norms and Beyond—0
Automatic Selection of t-SNE Perplexity—0
An Accelerated Doubly Stochastic Gradient Method with Faster Explicit Model Identification—0
Autonomous Collaborative Scheduling of Time-dependent UAVs, Workers and Vehicles for Crowdsensing in Disaster Response—0
Autonomous Dimension Reduction by Flattening Deformation of Data Manifold under an Intrinsic Deforming Field—0
Autonomous Learning of Features for Control: Experiments with Embodied and Situated Agents—0
ELBD: Efficient score algorithm for feature selection on latent variables of VAE—0
AutoQML: Automatic Generation and Training of Robust Quantum-Inspired Classifiers by Using Genetic Algorithms on Grayscale Images—0
Auto-weighted Mutli-view Sparse Reconstructive Embedding—0
Brain Inspired Face Recognition: A Computational Framework—0
AVIDA: Alternating method for Visualizing and Integrating Data—0
A Visual Analytics Framework for Reviewing Multivariate Time-Series Data with Dimensionality Reduction—0
A Visual Interaction Framework for Dimensionality Reduction Based Data Exploration—0
A Wasserstein perspective of Vanilla GANs—0
A Wavelet Diffusion GAN for Image Super-Resolution—0
A witness function based construction of discriminative models using Hermite polynomials—0
An Algorithmic Information Calculus for Causal Discovery and Reprogramming Systems—0
Approximated and User Steerable tSNE for Progressive Visual Analytics—0
Active Learning with TensorBoard Projector—0
Analyse en Composante Principale pour l'extraction des i-vecteurs en v\'erification du locuteur (Principal Component Analysis for i-vector extraction in speaker verification.) [in French]—0
Bayesian Calibration for Activity Based Models—0
Analysing race and sex bias in brain age prediction—0
Bayesian Compressed Regression—0
Bayesian Data Sketching for Varying Coefficient Regression Models—0
Bayesian Exponential Family PCA—0
Bayesian full waveform inversion with sequential surrogate model refinement—0
Bayesian Inference on Matrix Manifolds for Linear Dimensionality Reduction—0
Bayesian Inverse Problems with Conditional Sinkhorn Generative Adversarial Networks in Least Volume Latent Spaces—0
Bayesian inverse regression for dimension reduction with small datasets—0
Analysis of Evolving Cortical Neuronal Networks Using Visual Informatics—0
Bayesian Learning of Dynamic Multilayer Networks—0
Approaching Metaheuristic Deep Learning Combos for Automated Data Mining—0
A Dimensionality Reduction Method for Finding Least Favorable Priors with a Focus on Bregman Divergence—0
Bayesian model and dimension reduction for uncertainty propagation: applications in random media—0
Bayesian neural networks and dimensionality reduction—0
ANALYTiC: Understanding Decision Boundaries and Dimensionality Reduction in Machine Learning—0
Applying Supervised Learning Algorithms and a New Feature Selection Method to Predict Coronary Artery Disease—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