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 2751–2760 of 3073 papers

TitleStatusHype
Are Good Explainers Secretly Human-in-the-Loop Active Learners?—0
A Review and A Robust Framework of Data-Efficient 3D Scene Parsing with Traditional/Learned 3D Descriptors—0
A Review of Bayesian Uncertainty Quantification in Deep Probabilistic Image Segmentation—0
A Review of Machine Learning Methods Applied to Video Analysis Systems—0
A Risk-Aware Adaptive Robust MPC with Learned Uncertainty Quantification—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
A Scalable Training Strategy for Blind Multi-Distribution Noise Removal—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