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 2351–2360 of 3073 papers

TitleStatusHype
LMTurk: Few-Shot Learners as Crowdsourcing Workers in a Language-Model-as-a-Service Framework—0
Local Function Complexity for Active Learning via Mixture of Gaussian Processes—0
Localization-Aware Active Learning for Object Detection—0
Localized active learning of Gaussian process state space models—0
Active learning-assisted neutron spectroscopy with log-Gaussian processes—0
Looking at the posterior: accuracy and uncertainty of neural-network predictions—0
Loss Prediction: End-to-End Active Learning Approach For Speech Recognition—0
Low Budget Active Learning via Wasserstein Distance: An Integer Programming Approach—0
Low-Budget Active Learning via Wasserstein Distance: An Integer Programming Approach—0
Lower Bound on the Greedy Approximation Ratio for Adaptive Submodular Cover—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