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 1676–1700 of 3073 papers

TitleStatusHype
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
Using memristor crossbar structure to implement a novel adaptive real time fuzzy modeling algorithm—0
Using Serious Games to Correct French Dictations: Proposal for a New Unity3D/NooJ Connector—0
VaB-AL: Incorporating Class Imbalance and Difficulty with Variational Bayes for Active Learning—0
Value of Information Analysis via Active Learning and Knowledge Sharing in Error-Controlled Adaptive Kriging—0
Value Preferences Estimation and Disambiguation in Hybrid Participatory Systems—0
Variance based sensitivity analysis for Monte Carlo and importance sampling reliability assessment with Gaussian processes—0
Variational Adaptive-Newton Method for Explorative Learning—0
Vibration-based Uncertainty Estimation for Learning from Limited Supervision—0
Video Annotation and Tracking with Active Learning—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