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 2671–2680 of 3073 papers

TitleStatusHype
Constrained Bayesian Active Learning of Interference Channels in Cognitive Radio Networks—0
Decomposition of Uncertainty in Bayesian Deep Learning for Efficient and Risk-sensitive Learning—0
Bayesian Hypernetworks—0
Bag-Level Aggregation for Multiple Instance Active Learning in Instance Classification Problems—0
Active Learning for Human Pose Estimation—0
Personalized Image Aesthetics—0
libact: Pool-based Active Learning in PythonCode0
Active Learning amidst Logical KnowledgeCode0
On the Discrimination Power and Effective Utilization of Active Learning Measures in Version Space Search—0
Catalyst design using actively learned machine with non-ab initio input features towards CO2 reduction reactions—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