SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 1201–1225 of 3304 papers

TitleStatusHype
Explainable, Stable, and Scalable Graph Convolutional Networks for Learning Graph Representation—0
Adversarial Vulnerability as a Consequence of On-Manifold Inseparibility—0
Explaining Genetic Programming Trees using Large Language Models—0
Exploiting Capacity of Sewer System Using Unsupervised Learning Algorithms Combined with Dimensionality Reduction—0
Exploiting Vulnerability of Pooling in Convolutional Neural Networks by Strict Layer-Output Manipulation for Adversarial Attacks—0
Exploiting Wireless Channel State Information Structures Beyond Linear Correlations: A Deep Learning Approach—0
Explore intrinsic geometry of sleep dynamics and predict sleep stage by unsupervised learning techniques—0
Exploring Dimensionality Reduction Techniques in Multilingual Transformers—0
An information-geometric approach to feature extraction and moment reconstruction in dynamical systems—0
A Fully Convolutional Network for MR Fingerprinting—0
Exploring Narrative Clustering in Large Language Models: A Layerwise Analysis of BERT—0
Exploring Predictive States via Cantor Embeddings and Wasserstein Distance—0
Exploring Semantic Clustering in Deep Reinforcement Learning for Video Games—0
Exploring the Deep Feature Space of a Cell Classification Neural Network—0
Efficient Nearest Neighbor based Uncertainty Estimation for Natural Language Processing Tasks—0
Exploring the Limits of KV Cache Compression in Visual Autoregressive Transformers—0
Exploring the Manifold of Neural Networks Using Diffusion Geometry—0
Exploring the Unfairness of DP-SGD Across Settings—0
Broadcast Product: Shape-aligned Element-wise Multiplication and Beyond—0
A Functional approach for Two Way Dimension Reduction in Time Series—0
Exponential Convergence of CAVI for Bayesian PCA—0
Exponential Family Embeddings—0
Expressing Facial Structure and Appearance Information in Frequency Domain for Face Recognition—0
Extending classical surrogate modelling to high-dimensions through supervised dimensionality reduction: a data-driven approach—0
Fair Kernel Learning—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