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 2211–2220 of 3073 papers

TitleStatusHype
ActiveAD: Planning-Oriented Active Learning for End-to-End Autonomous Driving—0
Active Adversarial Domain Adaptation—0
Active Algorithms For Preference Learning Problems with Multiple Populations—0
Active Altruism Learning and Information Sufficiency for Autonomous Driving—0
Active and Adaptive Sequential learning—0
Active and Continuous Exploration with Deep Neural Networks and Expected Model Output Changes—0
Active and Dynamic Beam Tracking UnderStochastic Mobility—0
Active and Incremental Learning with Weak Supervision—0
Active and passive learning of linear separators under log-concave distributions—0
Active and Semi-Supervised Learning in ASR: Benefits on the Acoustic and Language Models—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