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 2101–2125 of 3073 papers

TitleStatusHype
Uncertainty quantification and exploration-exploitation trade-off in humans—0
Uncertainty Quantification in Continual Open-World Learning—0
Uncertainty Quantification in Graph Neural Networks with Shallow Ensembles—0
Uncertainty Sentence Sampling by Virtual Adversarial Perturbation—0
Unlocking the Power of LLM Uncertainty for Active In-Context Example Selection—0
Understand customer reviews with less data and in short time: pretrained language representation and active learning—0
Understanding Approximation for Bayesian Inference in Neural Networks—0
Understanding Discourse on Work and Job-Related Well-Being in Public Social Media—0
Understanding Goal-Oriented Active Learning via Influence Functions—0
Understanding the Success of Knowledge Distillation -- A Data Augmentation Perspective—0
Understanding Uncertainty-based Active Learning Under Model Mismatch—0
Unfolding Hidden Barriers by Active Enhanced Sampling—0
Margin-based sampling in high dimensions: When being active is less efficient than staying passive—0
Unsupervised Active Learning: Optimizing Labeling Cost-Effectiveness for Automatic Speech Recognition—0
Unsupervised Clustering Active Learning for Person Re-identification—0
Unsupervised Clustering and Active Learning of Hyperspectral Images with Nonlinear Diffusion—0
Unsupervised Document Classification with Informed Topic Models—0
Unsupervised Frequent Pattern Mining for CEP—0
Unsupervised Learning of Distributional Properties can Supplement Human Labeling and Increase Active Learning Efficiency in Anomaly Detection—0
Unsupervised Person Slot Filling based on Graph Mining—0
UPM system for WMT 2012—0
Upper-Confidence-Bound Algorithms for Active Learning in Multi-Armed Bandits—0
USIM-DAL: Uncertainty-aware Statistical Image Modeling-based Dense Active Learning for Super-resolution—0
Using Active Learning Methods to Strategically Select Essays for Automated Scoring—0
Using Chao's Estimator as a Stopping Criterion for Technology-Assisted Review—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