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 881–890 of 3304 papers

TitleStatusHype
From Pretext to Purpose: Batch-Adaptive Self-Supervised Learning—0
Simple but Effective Unsupervised Classification for Specified Domain Images: A Case Study on Fungi Images—0
Solving ARC visual analogies with neural embeddings and vector arithmetic: A generalized methodCode0
The optimal resolution level of a protein is an emergent property of its structure and dynamicsCode0
High Dimensional Binary Choice Model with Unknown Heteroskedasticity or Instrumental Variables—0
Inference and Interference: The Role of Clipping, Pruning and Loss Landscapes in Differentially Private Stochastic Gradient Descent—0
Cricket Player Profiling: Unraveling Strengths and Weaknesses Using Text Commentary Data—0
Differentiable VQ-VAE's for Robust White Matter Streamline EncodingsCode0
Perfecting Liquid-State Theories with Machine Intelligence—0
Covering Number of Real Algebraic Varieties and Beyond: Improved Bounds and Applications—0
Show:102550
← PrevPage 89 of 331Next →
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