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 1541–1550 of 3073 papers

TitleStatusHype
Motor cortex mapping using active gaussian processes—0
MP-ALOE: An r2SCAN dataset for universal machine learning interatomic potentials—0
Multi-armed Bandit Problem with Known Trend—0
Multi-class Active Learning: A Hybrid Informative and Representative Criterion Inspired Approach—0
Multi-Class Multi-Annotator Active Learning With Robust Gaussian Process for Visual Recognition—0
Multi-class Text Classification using BERT-based Active Learning—0
Multi-Domain Learning From Insufficient Annotations—0
Multi-fidelity Bayesian Neural Networks: Algorithms and Applications—0
Multifidelity Simulation-based Inference for Computationally Expensive Simulators—0
Multi-Label Active Learning from Crowds—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