SOTAVerified

Dimensionality Reduction

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

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Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 876–900 of 3304 papers

TitleStatusHype
Classification Methods Based on Machine Learning for the Analysis of Fetal Health Data—0
Bit Cipher -- A Simple yet Powerful Word Representation System that Integrates Efficiently with Language Models—0
Handling Overlapping Asymmetric Datasets -- A Twice Penalized P-Spline Approach—0
Utilizing VQ-VAE for End-to-End Health Indicator Generation in Predicting Rolling Bearing RUL—0
Finding Real-World Orbital Motion Laws from Data—0
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
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
Manifold learning: what, how, and why—0
Computing Approximate _p Sensitivities—0
Visualizing DNA reaction trajectories with deep graph embedding approachesCode0
ViDa: Visualizing DNA hybridization trajectories with biophysics-informed deep graph embeddingsCode0
Practical considerations for variable screening in the super learnerCode0
3-Dimensional residual neural architecture search for ultrasonic defect detection—0
TailorMe: Self-Supervised Learning of an Anatomically Constrained Volumetric Human Shape Model—0
Learning Collective Behaviors from Observation—0
Language Model Training Paradigms for Clinical Feature EmbeddingsCode0
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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