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 1231–1240 of 3073 papers

TitleStatusHype
Artificial intelligence approaches for materials-by-design of energetic materials: state-of-the-art, challenges, and future directions—0
A Robust UCB Scheme for Active Learning in Regression from Strategic Crowds—0
Active Learning on a Programmable Photonic Quantum Processor—0
Active Learning for Black-Box Adversarial Attacks in EEG-Based Brain-Computer Interfaces—0
Teach Me What You Want to Play: Learning Variants of Connect Four through Human-Robot Interaction—0
A Risk-Aware Adaptive Robust MPC with Learned Uncertainty Quantification—0
Active Learning of Uniformly Accurate Inter-atomic Potentials for Materials Simulation—0
Active learning for binary classification with variable selection—0
Active Deep Learning for Classification of Hyperspectral Images—0
A Comprehensive Review of Latent Space Dynamics Identification Algorithms for Intrusive and Non-Intrusive Reduced-Order-Modeling—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