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 1876–1900 of 3073 papers

TitleStatusHype
Distance-Penalized Active Learning Using Quantile Search—0
Distilling the Posterior in Bayesian Neural Networks—0
Distributed Safe Learning and Planning for Multi-robot Systems—0
Distributional Latent Variable Models with an Application in Active Cognitive Testing—0
Distributionally Robust Active Learning for Gaussian Process Regression—0
Distributionally Robust Statistical Verification with Imprecise Neural Networks—0
Distributional Term Set Expansion—0
Distribution Aware Active Learning—0
Distribution-Dependent Sample Complexity of Large Margin Learning—0
Diverse Complexity Measures for Dataset Curation in Self-driving—0
Diverse mini-batch Active Learning—0
Does Informativeness Matter? Active Learning for Educational Dialogue Act Classification—0
Domain Adaptation and Active Learning for Fine-Grained Recognition in the Field of Biodiversity—0
Domain Adaptation with Active Learning for Coreference Resolution—0
Domain Adversarial Active Learning for Domain Generalization Classification—0
Dominant Set-based Active Learning for Text Classification and its Application to Online Social Media—0
Don't Stop Me Now! Using Global Dynamic Oracles to Correct Training Biases of Transition-Based Dependency Parsers—0
Double-Barreled Question Detection at Momentive—0
Downstream-Pretext Domain Knowledge Traceback for Active Learning—0
Do you Feel Certain about your Annotation? A Web-based Semantic Frame Annotation Tool Considering Annotators' Concerns and Behaviors—0
DP-Dueling: Learning from Preference Feedback without Compromising User Privacy—0
DroidStar: Callback Typestates for Android Classes—0
Dropout-based Active Learning for Regression—0
Dual Active Learning for Reinforcement Learning from Human Feedback—0
Dual Adversarial Network for Deep 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