SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 51–75 of 3304 papers

TitleStatusHype
A Memory Efficient Baseline for Open Domain Question AnsweringCode1
DIAS: A Dataset and Benchmark for Intracranial Artery Segmentation in DSA sequencesCode1
Deep Learning for Reduced Order Modelling and Efficient Temporal Evolution of Fluid SimulationsCode1
Dimension Reduction for Efficient Dense Retrieval via Conditional AutoencoderCode1
An Additive Autoencoder for Dimension EstimationCode1
An efficient aggregation method for the symbolic representation of temporal dataCode1
Disentangling Identifiable Features from Noisy Data with Structured Nonlinear ICACode1
DistilProtBert: A distilled protein language model used to distinguish between real proteins and their randomly shuffled counterpartsCode1
catch22: CAnonical Time-series CHaracteristicsCode1
DMCNet: Diversified Model Combination Network for Understanding Engagement from Video ScreengrabsCode1
DQ-LoRe: Dual Queries with Low Rank Approximation Re-ranking for In-Context LearningCode1
A New Basis for Sparse Principal Component AnalysisCode1
CatBoost: gradient boosting with categorical features supportCode1
CBMAP: Clustering-based manifold approximation and projection for dimensionality reductionCode1
BIKED: A Dataset for Computational Bicycle Design with Machine Learning BenchmarksCode1
Bayesian Optimization of Sampling Densities in MRICode1
Binary Bleed: Fast Distributed and Parallel Method for Automatic Model SelectionCode1
Clustering with UMAP: Why and How Connectivity MattersCode1
Algorithmic Stability and Generalization of an Unsupervised Feature Selection AlgorithmCode1
ATD: Augmenting CP Tensor Decomposition by Self SupervisionCode1
A hyperparameter-tuning approach to automated inverse planningCode1
BasisVAE: Translation-invariant feature-level clustering with Variational AutoencodersCode1
Adversarial AutoencodersCode1
BayesOpt Adversarial AttackCode1
Autoencoding with a Classifier SystemCode1
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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