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 2021–2030 of 3073 papers

TitleStatusHype
The Practical Challenges of Active Learning: Lessons Learned from Live Experimentation—0
The Relationship Between Agnostic Selective Classification Active Learning and the Disagreement Coefficient—0
The Relevance of Bayesian Layer Positioning to Model Uncertainty in Deep Bayesian Active Learning—0
The Role of Higher-Order Cognitive Models in Active Learning—0
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
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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