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 2301–2310 of 3073 papers

TitleStatusHype
A Flexible Framework for Anomaly Detection via Dimensionality ReductionCode0
Learning to Sample: an Active Learning Framework—0
Active learning to optimise time-expensive algorithm selection—0
An Active Learning Approach for Reducing Annotation Cost in Skin Lesion AnalysisCode0
Augmented Memory Networks for Streaming-Based Active One-Shot Learning—0
Active Collaborative Sensing for Energy BreakdownCode0
On the Expressiveness of Approximate Inference in Bayesian Neural NetworksCode0
Turning silver into gold: error-focused corpus reannotation with active learning—0
Active Learning for Financial Investment Reports—0
Epistemic Uncertainty Sampling—0
Show:102550
← PrevPage 231 of 308Next →

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