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 1591–1600 of 3073 papers

TitleStatusHype
The Search for Squawk: Agile Modeling in Bioacoustics—0
The Solution Path Algorithm for Identity-Aware Multi-Object Tracking—0
The trade-off between data minimization and fairness in collaborative filtering—0
The University of Arizona at SemEval-2021 Task 10: Applying Self-training, Active Learning and Data Augmentation to Source-free Domain Adaptation—0
The Unreasonable Effectiveness of Fully-Connected Layers for Low-Data Regimes—0
The Use of AI-Robotic Systems for Scientific Discovery—0
The Use of Unlabeled Data versus Labeled Data for Stopping Active Learning for Text Classification—0
The Utility of Abstaining in Binary Classification—0
The Why, When, and How to Use Active Learning in Large-Data-Driven 3D Object Detection for Safe Autonomous Driving: An Empirical Exploration—0
The Work Capacity of Channels with Memory: Maximum Extractable Work in Percept-Action Loops—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