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

Showing 26212630 of 3073 papers

TitleStatusHype
Supervising Feature Influence0
Active Metric Learning for Supervised Classification0
Towards Human-Machine Cooperation: Self-supervised Sample Mining for Object Detection0
Handling Adversarial Concept Drift in Streaming Data0
Evaluating Sentence-Level Relevance Feedback for High-Recall Information Retrieval0
Structural query-by-committee0
Leveraging Crowdsourcing Data For Deep Active Learning - An Application: Learning Intents in Alexa0
Highly Automated Learning for Improved Active Safety of Vulnerable Road Users0
Dimension-Robust MCMC in Bayesian Inverse Problems0
Multi-class Active Learning: A Hybrid Informative and Representative Criterion Inspired Approach0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1TypiClustAccuracy93.2Unverified
2PT4ALAccuracy93.1Unverified
3Learning lossAccuracy91.01Unverified
4CoreGCNAccuracy90.7Unverified
5Core-setAccuracy89.92Unverified
6Random Baseline (Resnet18)Accuracy88.45Unverified
7Random Baseline (VGG16)Accuracy85.09Unverified