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 321–330 of 3073 papers

TitleStatusHype
Active Learning for Human Pose Estimation—0
Active Altruism Learning and Information Sufficiency for Autonomous Driving—0
Active learning of neural response functions with Gaussian processes—0
Active Learning for High-Dimensional Binary Features—0
Active Learning for Graphs with Noisy Structures—0
Active Learning for Graph Neural Networks via Node Feature Propagation—0
Active Algorithms For Preference Learning Problems with Multiple Populations—0
LLMs as Probabilistic Minimally Adequate Teachers for DFA Learning—0
Active learning of neural population dynamics using two-photon holographic optogenetics—0
Active Learning of Ordinal Embeddings: A User Study on Football Data—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