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 781–790 of 3073 papers

TitleStatusHype
A Review of Bayesian Uncertainty Quantification in Deep Probabilistic Image Segmentation—0
A Review of Machine Learning Methods Applied to Video Analysis Systems—0
Active Learning of Uniformly Accurate Inter-atomic Potentials for Materials Simulation—0
A Risk-Aware Adaptive Robust MPC with Learned Uncertainty Quantification—0
ALEVS: Active Learning by Statistical Leverage Sampling—0
Teach Me What You Want to Play: Learning Variants of Connect Four through Human-Robot Interaction—0
A Robust UCB Scheme for Active Learning in Regression from Strategic Crowds—0
Artificial intelligence approaches for materials-by-design of energetic materials: state-of-the-art, challenges, and future directions—0
A Scalable Algorithm for Active Learning—0
Active Learning for Vision-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