SOTAVerified

Dimensionality Reduction

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

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Papers

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Showing 1751–1800 of 3304 papers

TitleStatusHype
Explainable AI for Multivariate Time Series Pattern Exploration: Latent Space Visual Analytics with Temporal Fusion Transformer and Variational Autoencoders in Power Grid Event Diagnosis—0
Explainable Light-Weight Deep Learning Pipeline for Improved Drought Stress Identification—0
Explainable, Stable, and Scalable Graph Convolutional Networks for Learning Graph Representation—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
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
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
Exploring UMAP in hybrid models of entropy-based and representativeness sampling for active learning in biomedical segmentation—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
Extension of PCA to Higher Order Data Structures: An Introduction to Tensors, Tensor Decompositions, and Tensor PCA—0
Extração e Classificação de Características Radiômicas em Gliomas de Baixo Grau para Análise da Codeleção 1p/19q—0
Extracting Geography from Trade Data—0
Extracting grid characteristics from spatially distributed place cell inputs using non-negative PCA—0
Extracting lexico-semantic relations from specialized corpora using a word space model (Analyse distributionnelle de corpus sp\'ecialis\'es pour l'identification de relations lexico-s\'emantiques) [in French]—0
Extracting low-dimensional dynamics from multiple large-scale neural population recordings by learning to predict correlations—0
Extreme compression of sentence-transformer ranker models: faster inference, longer battery life, and less storage on edge devices—0
Extreme Dimension Reduction for Handling Covariate Shift—0
Extreme heatwave sampling and prediction with analog Markov chain and comparisons with deep learning—0
Extreme-SAX: Extreme Points Based Symbolic Representation for Time Series Classification—0
Face Recognition using Curvelet Transform—0
Face Recognition using Hough Peaks extracted from the significant blocks of the Gradient Image—0
Facilitate the Parametric Dimension Reduction by Gradient Clipping—0
Factor Analysis, Probabilistic Principal Component Analysis, Variational Inference, and Variational Autoencoder: Tutorial and Survey—0
Factor-augmented sparse MIDAS regressions with an application to nowcasting—0
Factorization of Latent Variables in Distributional Semantic Models—0
Factor Network Autoregressions—0
Factors in Fashion: Factor Analysis towards the Mode—0
Factors of Transferability for a Generic ConvNet Representation—0
Fair and skill-diverse student group formation via constrained k-way graph partitioning—0
Fair Kernel Learning—0
Fair Principal Component Analysis and Filter Design—0
Fair Recommendation by Geometric Interpretation and Analysis of Matrix Factorization—0
FakeCatcher: Detection of Synthetic Portrait Videos using Biological Signals—0
Fast and Accurate Reduced-Order Modeling of a MOOSE-based Additive Manufacturing Model with Operator Learning—0
Fast and Large-scale Unsupervised Relation Extraction—0
Fast clustering for scalable statistical analysis on structured images—0
Fast Data-driven Greedy Sensor Selection for Ridge Regression—0
Fast Data-independent KLT Approximations Based on Integer Functions—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