SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 91–100 of 3304 papers

TitleStatusHype
PCA-RAG: Principal Component Analysis for Efficient Retrieval-Augmented Generation—0
Bayesian optimization for mixed variables using an adaptive dimension reduction process: applications to aircraft design—0
Dimension reduction for derivative-informed operator learning: An analysis of approximation errors—0
Hodge Laplacians and Hodge Diffusion Maps—0
LayerFlow: Layer-wise Exploration of LLM Embeddings using Uncertainty-aware Interlinked Projections—0
Hybrid machine learning models based on physical patterns to accelerate CFD simulations: a short guide on autoregressive models—0
Adaptive Locally Linear Embedding—0
Deep Fair Learning: A Unified Framework for Fine-tuning Representations with Sufficient Networks—0
econSG: Efficient and Multi-view Consistent Open-Vocabulary 3D Semantic Gaussians—0
Nes2Net: A Lightweight Nested Architecture for Foundation Model Driven Speech Anti-spoofingCode2
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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