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 2876–2900 of 3073 papers

TitleStatusHype
From Cutting Planes Algorithms to Compression Schemes and Active Learning—0
Multi-Label Active Learning from Crowds—0
Active Learning for Entity Filtering in Microblog StreamsCode0
Task Selection for Bandit-Based Task Assignment in Heterogeneous Crowdsourcing—0
Upper-Confidence-Bound Algorithms for Active Learning in Multi-Armed Bandits—0
ALEVS: Active Learning by Statistical Leverage Sampling—0
Combining Active Learning and Partial Annotation for Domain Adaptation of a Japanese Dependency Parser—0
Can Natural Language Processing Become Natural Language Coaching?—0
A System for Generating Multiple Choice Questions: With a Novel Approach for Sentence Selection—0
Learning Salient Samples and Distributed Representations for Topic-Based Chinese Message Polarity Classification—0
Efficient and Parsimonious Agnostic Active Learning—0
S2: An Efficient Graph Based Active Learning Algorithm with Application to Nonparametric Classification—0
Bayesian Dark KnowledgeCode0
Convergence Rates of Active Learning for Maximum Likelihood Estimation—0
ICE: Rapid Information Extraction Customization for NLP Novices—0
Active Learning and Discovery of Object Categories in the Presence of Unnameable Instances—0
An Analytic and Empirical Evaluation of Return-on-Investment-Based Active Learning—0
Judging the Quality of Automatically Generated Gap-fill Question using Active Learning—0
What I've learned about annotating informal text (and why you shouldn't take my word for it)—0
Strat\'egies de s\'election des exemples pour l'apprentissage actif avec des champs al\'eatoires conditionnels—0
Active Learning for Structured Probabilistic Models With Histogram Approximation—0
Efficient Label Collection for Unlabeled Image Datasets—0
Narrowing the Loop: Integration of Resources and Linguistic Dataset Development with Interactive Machine Learning—0
Learning with a Drifting Target Concept—0
Algorithmic Connections Between Active Learning and Stochastic Convex Optimization—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