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 611–620 of 3073 papers

TitleStatusHype
Active Learning in Noisy Conditions for Spoken Language Understanding—0
ACIL: Active Class Incremental Learning for Image Classification—0
Active Learning in Incomplete Label Multiple Instance Multiple Label Learning—0
Active Community Detection with Maximal Expected Model Change—0
ALPINE: Active Link Prediction using Network Embedding—0
AL-PINN: Active Learning-Driven Physics-Informed Neural Networks for Efficient Sample Selection in Solving Partial Differential Equations—0
Active Learning in Gaussian Process State Space Model—0
Active Learning++: Incorporating Annotator's Rationale using Local Model Explanation—0
Fast Rates in Pool-Based Batch Active Learning—0
Active Learning-Based Optimization of Hydroelectric Turbine Startup to Minimize Fatigue Damage—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