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 1726–1750 of 3073 papers

TitleStatusHype
Cost-Accuracy Aware Adaptive Labeling for Active LearningCode0
Mapping oil palm density at country scale: An active learning approach—0
Benchmarking the Performance of Bayesian Optimization across Multiple Experimental Materials Science DomainsCode1
LADA: Look-Ahead Data Acquisition via Augmentation for Deep Active LearningCode1
Coresets for Classification – Simplified and Strengthened—0
Should We Trust This Summary? Bayesian Abstractive Summarization to The Rescue—0
Actively Learning Concepts and Conjunctive Queries under ELr-Ontologies—0
Partitioned Active Learning for Heterogeneous Systems—0
Improved Algorithms for Agnostic Pool-based Active Classification—0
On risk-based active learning for structural health monitoring—0
Supporting Land Reuse of Former Open Pit Mining Sites using Text Classification and Active Learning—0
Bayesian Active Learning by Disagreements: A Geometric Perspective—0
Automatic Learning to Detect Concept Drift—0
Goldilocks: Just-Right Tuning of BERT for Technology-Assisted Review—0
Submodular Mutual Information for Targeted Data Subset Selection—0
Active WeaSuL: Improving Weak Supervision with Active LearningCode1
An efficient scheme based on graph centrality to select nodes for training for effective learning—0
Weather and Light Level Classification for Autonomous Driving: Dataset, Baseline and Active Learning—0
Diversity-Aware Batch Active Learning for Dependency ParsingCode0
Morphological classification of astronomical images with limited labelling—0
Multi-class Text Classification using BERT-based Active Learning—0
Unsupervised Instance Selection with Low-Label, Supervised Learning for Outlier DetectionCode0
Supervised Anomaly Detection via Conditional Generative Adversarial Network and Ensemble Active LearningCode1
One-Round Active Learning—0
Active Learning of Sequential Transducers with Side Information about the Domain—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