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 26112620 of 3073 papers

TitleStatusHype
Active Learning for Efficient Testing of Student Programs0
Derivative free optimization via repeated classificationCode0
A Deep Active Survival Analysis Approach for Precision Treatment Recommendations: Application of Prostate Cancer0
Active covariance estimation by random sub-sampling of variables0
Active Metric Learning for Supervised Classification0
Supervising Feature Influence0
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
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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