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 771–780 of 3073 papers

TitleStatusHype
A Proxy Attack-Free Strategy for Practically Improving the Poisoning Efficiency in Backdoor Attacks—0
A Quality-based Active Sample Selection Strategy for Statistical Machine Translation—0
A quantum active learning algorithm for sampling against adversarial attacks—0
Active Learning for Autonomous Intelligent Agents: Exploration, Curiosity, and Interaction—0
A Survey of Active Learning for Natural Language Processing—0
ALEVS: Active Learning by Statistical Leverage Sampling—0
A Receding Horizon Approach for Simultaneous Active Learning and Control using Gaussian Processes—0
Are Good Explainers Secretly Human-in-the-Loop Active Learners?—0
Active Learning for Vision-Language Models—0
Accurate Prediction and Uncertainty Estimation using Decoupled Prediction Interval Networks—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