SOTAVerified

Active Learning

Active Learning is a paradigm in supervised machine learning which uses fewer training examples to achieve better optimization by iteratively training a predictor, and using the predictor in each iteration to choose the training examples which will increase its chances of finding better configurations and at the same time improving the accuracy of the prediction model

Source: Polystore++: Accelerated Polystore System for Heterogeneous Workloads

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 2526–2550 of 3073 papers

TitleStatusHype
Actively Learning Hemimetrics with Applications to Eliciting User Preferences—0
Actively learning to learn causal relationships—0
Actively Learning what makes a Discrete Sequence Valid—0
ActiveMatch: End-to-end Semi-supervised Active Representation Learning—0
Active metric learning and classification using similarity queries—0
Active Metric Learning for Supervised Classification—0
Active Metric Learning from Relative Comparisons—0
Active Mining Sample Pair Semantics for Image-text Matching—0
Active Model Aggregation via Stochastic Mirror Descent—0
Active Multi-Information Source Bayesian Quadrature—0
Active Multi-Kernel Domain Adaptation for Hyperspectral Image Classification—0
Active Multi-Task Representation Learning—0
Active Nearest-Neighbor Learning in Metric Spaces—0
Active Neural 3D Reconstruction with Colorized Surface Voxel-based View Selection—0
Active operator learning with predictive uncertainty quantification for partial differential equations—0
Active Output Selection Strategies for Multiple Learning Regression Models—0
Active partitioning: inverting the paradigm of active learning—0
Active Perceptual Similarity Modeling with Auxiliary Information—0
Active PETs: Active Data Annotation Prioritisation for Few-Shot Claim Verification with Pattern Exploiting Training—0
Active Player Modelling—0
Active Preference Learning for Large Language Models—0
Active Preference Learning with Discrete Choice Data—0
ActivePusher: Active Learning and Planning with Residual Physics for Nonprehensile Manipulation—0
Active Refinement for Multi-Label Learning: A Pseudo-Label Approach—0
ACTIVE REFINEMENT OF WEAKLY SUPERVISED MODELS—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1TypiClustAccuracy93.2—Unverified
2PT4ALAccuracy93.1—Unverified
3Learning lossAccuracy91.01—Unverified
4CoreGCNAccuracy90.7—Unverified
5Core-setAccuracy89.92—Unverified
6Random Baseline (Resnet18)Accuracy88.45—Unverified
7Random Baseline (VGG16)Accuracy85.09—Unverified