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
Improved Algorithms for Neural Active LearningCode0
Improved detection of discarded fish species through BoxAL active learningCode0
Safe Active Learning for Multi-Output Gaussian ProcessesCode0
MORAL: Aligning AI with Human Norms through Multi-Objective Reinforced Active LearningCode0
Planning to Learn: A Novel Algorithm for Active Learning during Model-Based PlanningCode0
Stealing and Evading Malware Classifiers and Antivirus at Low False Positive ConditionsCode0
Clinical Trial Active LearningCode0
Active Classification with Uncertainty Comparison QueriesCode0
Class Balance Matters to Active Class-Incremental LearningCode0
Active Learning with Contrastive Pre-training for Facial Expression RecognitionCode0
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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