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 1626–1650 of 3304 papers

TitleStatusHype
Deep neural networks for the evaluation and design of photonic devices—0
A study of semantic augmentation of word embeddings for extractive summarization—0
Incremental Ensemble Gaussian Processes—0
Generalizing Correspondence Analysis for Applications in Machine Learning—0
Individualized Multilayer Tensor Learning with An Application in Imaging Analysis—0
Interpretability Beyond Classification Output: Semantic Bottleneck Networks—0
Inference and Interference: The Role of Clipping, Pruning and Loss Landscapes in Differentially Private Stochastic Gradient Descent—0
Inference for Regression with Variables Generated by AI or Machine Learning—0
Deep Reinforcement Learning-Assisted Federated Learning for Robust Short-term Utility Demand Forecasting in Electricity Wholesale Markets—0
Influ\^encia de T\'ecnicas N\~ao-supervisionadas de Redu \~ao de Dimensionalidade para Organiza \~ao Flex\' de Documentos (The Unsupervised Dimensionality Reduction Weight on Flexible Document Organization)[In Portuguese]—0
InfoClus: Informative Clustering of High-dimensional Data Embeddings—0
Information loss from dimensionality reduction in 5D-Gaussian spectral data—0
Information Processing by Neuron Populations in the Central Nervous System: Mathematical Structure of Data and Operations—0
Information retrieval in single cell chromatin analysis using TF-IDF transformation methods—0
Information-Theoretic Bounds and Approximations in Neural Population Coding—0
Information Theoretic Feature Transformation Learning for Brain Interfaces—0
Information-Theoretic Representation Learning for Positive-Unlabeled Classification—0
Feature Clustering for Support Identification in Extreme Regions—0
Infrastructure-Assisted Collaborative Perception in Automated Valet Parking: A Safety Perspective—0
Injecting Wiktionary to improve token-level contextual representations using contrastive learning—0
Input Guided Multiple Deconstruction Single Reconstruction neural network models for Matrix Factorization—0
In search of the most efficient and memory-saving visualization of high dimensional data—0
Deep topic modeling by multilayer bootstrap network and lasso—0
An Empirical Study on Fault Detection and Root Cause Analysis of Indium Tin Oxide Electrodes by Processing S-parameter Patterns—0
Interpretable and Efficient Data-driven Discovery and Control of Distributed Systems—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