SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

Showing 76100 of 3304 papers

TitleStatusHype
Effective Sample Size, Dimensionality, and Generalization in Covariate Shift AdaptationCode1
DartMinHash: Fast Sketching for Weighted SetsCode1
DataLens: Scalable Privacy Preserving Training via Gradient Compression and AggregationCode1
Adversarial AutoencodersCode1
A Hybrid Architecture for Out of Domain Intent Detection and Intent DiscoveryCode1
Deep reconstruction of strange attractors from time seriesCode1
Deep Learning of Individual AestheticsCode1
Derivative-Informed Neural Operator: An Efficient Framework for High-Dimensional Parametric Derivative LearningCode1
Detection and Retrieval of Out-of-Distribution Objects in Semantic SegmentationCode1
A local approach to parameter space reduction for regression and classification tasksCode1
DIAS: A Dataset and Benchmark for Intracranial Artery Segmentation in DSA sequencesCode1
Binary Bleed: Fast Distributed and Parallel Method for Automatic Model SelectionCode1
Bayesian Optimization of Sampling Densities in MRICode1
A Memory Efficient Baseline for Open Domain Question AnsweringCode1
ActUp: Analyzing and Consolidating tSNE and UMAPCode1
DistilProtBert: A distilled protein language model used to distinguish between real proteins and their randomly shuffled counterpartsCode1
Distributed Out-of-Memory NMF on CPU/GPU ArchitecturesCode1
An Additive Autoencoder for Dimension EstimationCode1
DMCNet: Diversified Model Combination Network for Understanding Engagement from Video ScreengrabsCode1
BasisVAE: Translation-invariant feature-level clustering with Variational AutoencodersCode1
A New Basis for Sparse Principal Component AnalysisCode1
An Embedding is Worth a Thousand Noisy LabelsCode1
Enhanced MRI brain tumor detection and classification via topological data analysis and low-rank tensor decompositionCode1
Towards a More Rigorous Science of Blindspot Discovery in Image Classification ModelsCode1
BayesOpt Adversarial AttackCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1UDRNClassification Accuracy90.9Unverified
2tSNEClassification Accuracy51.5Unverified
3IVISClassification Accuracy46.6Unverified
4UMAPClassification Accuracy41.3Unverified
#ModelMetricClaimedVerifiedStatus
1UDRNClassification Accuracy71.1Unverified
2QSClassification Accuracy68Unverified