SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 691–700 of 3304 papers

TitleStatusHype
Solving ARC visual analogies with neural embeddings and vector arithmetic: A generalized methodCode0
High Dimensional Binary Choice Model with Unknown Heteroskedasticity or Instrumental Variables—0
Cricket Player Profiling: Unraveling Strengths and Weaknesses Using Text Commentary Data—0
Inference and Interference: The Role of Clipping, Pruning and Loss Landscapes in Differentially Private Stochastic Gradient Descent—0
High-dimensional mixed-categorical Gaussian processes with application to multidisciplinary design optimization for a green aircraftCode2
Differentiable VQ-VAE's for Robust White Matter Streamline EncodingsCode0
Covering Number of Real Algebraic Varieties and Beyond: Improved Bounds and Applications—0
Perfecting Liquid-State Theories with Machine Intelligence—0
Algorithms for Non-Negative Matrix Factorization on Noisy Data With Negative ValuesCode0
Computing Approximate _p Sensitivities—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